2024-12-17T23:44:07.4115919Z Current runner version: '2.321.0' 2024-12-17T23:44:07.4123286Z Runner name: 'i-0897f70f52bdfd343' 2024-12-17T23:44:07.4124210Z Runner group name: 'Default' 2024-12-17T23:44:07.4125241Z Machine name: 'ip-10-0-46-137' 2024-12-17T23:44:07.4129943Z ##[group]GITHUB_TOKEN Permissions 2024-12-17T23:44:07.4132889Z Actions: read 2024-12-17T23:44:07.4133577Z Attestations: read 2024-12-17T23:44:07.4134588Z Checks: read 2024-12-17T23:44:07.4135188Z Contents: read 2024-12-17T23:44:07.4135751Z Deployments: read 2024-12-17T23:44:07.4136431Z Discussions: read 2024-12-17T23:44:07.4137060Z Issues: read 2024-12-17T23:44:07.4137682Z Metadata: read 2024-12-17T23:44:07.4138314Z Packages: read 2024-12-17T23:44:07.4138898Z Pages: read 2024-12-17T23:44:07.4139532Z PullRequests: read 2024-12-17T23:44:07.4140204Z RepositoryProjects: read 2024-12-17T23:44:07.4140967Z SecurityEvents: read 2024-12-17T23:44:07.4141643Z Statuses: read 2024-12-17T23:44:07.4142208Z ##[endgroup] 2024-12-17T23:44:07.4146270Z Secret source: Actions 2024-12-17T23:44:07.4147331Z Prepare workflow directory 2024-12-17T23:44:07.7387691Z Prepare all required actions 2024-12-17T23:44:07.7433197Z Getting action download info 2024-12-17T23:44:07.9343486Z Download action repository 'pytorch/test-infra@release/2.6' (SHA:eb0adf5a84668865394af69e26428b32c8105c1c) 2024-12-17T23:44:09.6820918Z Download action repository 'pytorch/pytorch@release/2.6' (SHA:0cdf8b1d09254cfda66191d1bd01e3041c3c76f7) 2024-12-17T23:44:22.8533631Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2024-12-17T23:44:23.0507008Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2024-12-17T23:44:23.3204674Z Getting action download info 2024-12-17T23:44:23.4263417Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c) 2024-12-17T23:44:23.6814891Z Getting action download info 2024-12-17T23:44:23.7899845Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2024-12-17T23:44:23.9676904Z Getting action download info 2024-12-17T23:44:24.0688618Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2024-12-17T23:44:24.2204914Z Getting action download info 2024-12-17T23:44:24.3360932Z Download action repository 'pytorch/test-infra@main' (SHA:a07505a74641a4ff5123d635defac481ef28ef1e) 2024-12-17T23:44:25.7785137Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/release/2.6 (0cdf8b1d09254cfda66191d1bd01e3041c3c76f7) 2024-12-17T23:44:25.7787335Z ##[group] Inputs 2024-12-17T23:44:25.7787701Z build-environment: linux-focal-py3.13-clang10 2024-12-17T23:44:25.7790432Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]} 2024-12-17T23:44:25.7793034Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:44:25.7793786Z sync-tag: 2024-12-17T23:44:25.7794977Z timeout-minutes: 600 2024-12-17T23:44:25.7795262Z use-gha: 2024-12-17T23:44:25.7795508Z dashboard-tag: 2024-12-17T23:44:25.7795870Z s3-bucket: gha-artifacts 2024-12-17T23:44:25.7796166Z aws-role-to-assume: 2024-12-17T23:44:25.7796837Z disable-monitor: false 2024-12-17T23:44:25.7797412Z ##[endgroup] 2024-12-17T23:44:25.7798219Z Complete job name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:44:25.8254578Z A job started hook has been configured by the self-hosted runner administrator 2024-12-17T23:44:25.8362450Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2024-12-17T23:44:25.8372038Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:44:25.8372777Z ##[endgroup] 2024-12-17T23:44:27.7082212Z Runner Type: linux.2xlarge 2024-12-17T23:44:27.7082757Z Instance Type: c5.2xlarge 2024-12-17T23:44:27.7083035Z AMI Name: unknown 2024-12-17T23:44:27.7107276Z AMI ID: ami-0fff1b9a61dec8a5f 2024-12-17T23:44:33.6193071Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@release/2.6 2024-12-17T23:44:33.6193580Z with: 2024-12-17T23:44:33.6194313Z github-secret: *** 2024-12-17T23:44:33.6195032Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2024-12-17T23:44:33.6195941Z activate-with-label: false 2024-12-17T23:44:33.6196241Z label: with-ssh 2024-12-17T23:44:33.6196513Z remove-existing-keys: true 2024-12-17T23:44:33.6196813Z fail-silently: true 2024-12-17T23:44:33.6197063Z env: 2024-12-17T23:44:33.6197296Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:44:33.6197586Z ##[endgroup] 2024-12-17T23:44:33.7395268Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2024-12-17T23:44:33.7397050Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2024-12-17T23:44:33.7520483Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@release/2.6 2024-12-17T23:44:33.7520981Z with: 2024-12-17T23:44:33.7521222Z no-sudo: true 2024-12-17T23:44:33.7521494Z submodules: recursive 2024-12-17T23:44:33.7521758Z fetch-depth: 0 2024-12-17T23:44:33.7522006Z env: 2024-12-17T23:44:33.7522237Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:44:33.7522517Z ##[endgroup] 2024-12-17T23:44:33.7602055Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:44:33.7603054Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:44:33.7610571Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:44:33.7610974Z env: 2024-12-17T23:44:33.7611198Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:44:33.7611476Z ##[endgroup] 2024-12-17T23:44:33.7698964Z ##[group]Run retry () { 2024-12-17T23:44:33.7699285Z retry () { 2024-12-17T23:44:33.7699649Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2024-12-17T23:44:33.7700105Z } 2024-12-17T23:44:33.7700358Z echo "${GITHUB_WORKSPACE}" 2024-12-17T23:44:33.7700691Z if [ -z "${NO_SUDO}" ]; then 2024-12-17T23:44:33.7701048Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2024-12-17T23:44:33.7701403Z else 2024-12-17T23:44:33.7701672Z  retry rm -rf "${GITHUB_WORKSPACE}" 2024-12-17T23:44:33.7702005Z fi 2024-12-17T23:44:33.7702252Z mkdir "${GITHUB_WORKSPACE}" 2024-12-17T23:44:33.7707822Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:44:33.7708223Z env: 2024-12-17T23:44:33.7708445Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:44:33.7708728Z NO_SUDO: true 2024-12-17T23:44:33.7708971Z ##[endgroup] 2024-12-17T23:44:33.7731385Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:44:36.7520055Z ##[group]Run malfet/checkout@silent-checkout 2024-12-17T23:44:36.7520411Z with: 2024-12-17T23:44:36.7520677Z ref: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:44:36.7521025Z fetch-depth: 0 2024-12-17T23:44:36.7521277Z submodules: recursive 2024-12-17T23:44:36.7521531Z quiet-checkout: true 2024-12-17T23:44:36.7521809Z repository: pytorch/pytorch 2024-12-17T23:44:36.7522222Z token: *** 2024-12-17T23:44:36.7522677Z ssh-strict: true 2024-12-17T23:44:36.7522941Z persist-credentials: true 2024-12-17T23:44:36.7523228Z clean: true 2024-12-17T23:44:36.7523474Z sparse-checkout-cone-mode: true 2024-12-17T23:44:36.7523781Z lfs: false 2024-12-17T23:44:36.7524027Z set-safe-directory: true 2024-12-17T23:44:36.7524307Z env: 2024-12-17T23:44:36.7524519Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:44:36.7524790Z ##[endgroup] 2024-12-17T23:44:36.8385236Z Syncing repository: pytorch/pytorch 2024-12-17T23:44:36.8386630Z ##[group]Getting Git version info 2024-12-17T23:44:36.8387147Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-12-17T23:44:36.8387836Z [command]/usr/bin/git version 2024-12-17T23:44:36.8388133Z git version 2.40.1 2024-12-17T23:44:36.8394169Z ##[endgroup] 2024-12-17T23:44:36.8406248Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/3d78d8f2-ad49-46f1-bff5-51312db05bf8' before making global git config changes 2024-12-17T23:44:36.8407233Z Adding repository directory to the temporary git global config as a safe directory 2024-12-17T23:44:36.8410040Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:44:36.8436096Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2024-12-17T23:44:36.8438934Z ##[group]Initializing the repository 2024-12-17T23:44:36.8441262Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:44:36.8463941Z hint: Using 'master' as the name for the initial branch. This default branch name 2024-12-17T23:44:36.8464566Z hint: is subject to change. To configure the initial branch name to use in all 2024-12-17T23:44:36.8465154Z hint: of your new repositories, which will suppress this warning, call: 2024-12-17T23:44:36.8465587Z hint: 2024-12-17T23:44:36.8465881Z hint: git config --global init.defaultBranch 2024-12-17T23:44:36.8466250Z hint: 2024-12-17T23:44:36.8466608Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2024-12-17T23:44:36.8467297Z hint: 'development'. The just-created branch can be renamed via this command: 2024-12-17T23:44:36.8467752Z hint: 2024-12-17T23:44:36.8467980Z hint: git branch -m 2024-12-17T23:44:36.8468514Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2024-12-17T23:44:36.8474680Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2024-12-17T23:44:36.8497017Z ##[endgroup] 2024-12-17T23:44:36.8497490Z ##[group]Disabling automatic garbage collection 2024-12-17T23:44:36.8505423Z [command]/usr/bin/git config --local gc.auto 0 2024-12-17T23:44:36.8525975Z ##[endgroup] 2024-12-17T23:44:36.8526391Z ##[group]Setting up auth 2024-12-17T23:44:36.8531211Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-12-17T23:44:36.8554375Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-12-17T23:44:36.8798705Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-12-17T23:44:36.8820100Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-12-17T23:44:36.9063794Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-12-17T23:44:36.9101619Z ##[endgroup] 2024-12-17T23:44:36.9102381Z ##[group]Fetching the repository 2024-12-17T23:44:36.9108945Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --progress --no-recurse-submodules --quiet origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2024-12-17T23:44:39.6000965Z remote: Enumerating objects: 1056409 2024-12-17T23:44:39.6001929Z remote: Enumerating objects: 1057229, done. 2024-12-17T23:44:39.6002737Z remote: Counting objects: 0% (1/820) 2024-12-17T23:44:39.6003125Z remote: Counting objects: 1% (9/820) 2024-12-17T23:44:39.6003579Z remote: Counting objects: 2% (17/820) 2024-12-17T23:44:39.6004029Z remote: Counting objects: 3% (25/820) 2024-12-17T23:44:39.6004631Z remote: Counting objects: 4% (33/820) 2024-12-17T23:44:39.6005237Z remote: Counting objects: 5% (41/820) 2024-12-17T23:44:39.6005883Z remote: Counting objects: 6% (50/820) 2024-12-17T23:44:39.6006513Z remote: Counting objects: 7% (58/820) 2024-12-17T23:44:39.6007121Z remote: Counting objects: 8% (66/820) 2024-12-17T23:44:39.6007679Z remote: Counting objects: 9% (74/820) 2024-12-17T23:44:39.6008059Z remote: Counting objects: 10% (82/820) 2024-12-17T23:44:39.6008445Z remote: Counting objects: 11% (91/820) 2024-12-17T23:44:39.6009062Z remote: Counting objects: 12% (99/820) 2024-12-17T23:44:39.6009685Z remote: Counting objects: 13% (107/820) 2024-12-17T23:44:39.6010119Z remote: Counting objects: 14% (115/820) 2024-12-17T23:44:39.6010500Z remote: Counting objects: 15% (123/820) 2024-12-17T23:44:39.6010990Z remote: Counting objects: 16% (132/820) 2024-12-17T23:44:39.6011615Z remote: Counting objects: 17% (140/820) 2024-12-17T23:44:39.6012255Z remote: Counting objects: 18% (148/820) 2024-12-17T23:44:39.6012729Z remote: Counting objects: 19% (156/820) 2024-12-17T23:44:39.6013382Z remote: Counting objects: 20% (164/820) 2024-12-17T23:44:39.6013862Z remote: Counting objects: 21% (173/820) 2024-12-17T23:44:39.6014482Z remote: Counting objects: 22% (181/820) 2024-12-17T23:44:39.6015088Z remote: Counting objects: 23% (189/820) 2024-12-17T23:44:39.6015734Z remote: Counting objects: 24% (197/820) 2024-12-17T23:44:39.6016138Z remote: Counting objects: 25% (205/820) 2024-12-17T23:44:39.6016524Z remote: Counting objects: 26% (214/820) 2024-12-17T23:44:39.6017004Z remote: Counting objects: 27% (222/820) 2024-12-17T23:44:39.6017390Z remote: Counting objects: 28% (230/820) 2024-12-17T23:44:39.6017769Z remote: Counting objects: 29% (238/820) 2024-12-17T23:44:39.6018136Z remote: Counting objects: 30% (246/820) 2024-12-17T23:44:39.6018532Z remote: Counting objects: 31% (255/820) 2024-12-17T23:44:39.6018909Z remote: Counting objects: 32% (263/820) 2024-12-17T23:44:39.6019296Z remote: Counting objects: 33% (271/820) 2024-12-17T23:44:39.6019665Z remote: Counting objects: 34% (279/820) 2024-12-17T23:44:39.6020045Z remote: Counting objects: 35% (287/820) 2024-12-17T23:44:39.6020427Z remote: Counting objects: 36% (296/820) 2024-12-17T23:44:39.6020809Z remote: Counting objects: 37% (304/820) 2024-12-17T23:44:39.6021195Z remote: Counting objects: 38% (312/820) 2024-12-17T23:44:39.6021574Z remote: Counting objects: 39% (320/820) 2024-12-17T23:44:39.6021955Z remote: Counting objects: 40% (328/820) 2024-12-17T23:44:39.6022337Z remote: Counting objects: 41% (337/820) 2024-12-17T23:44:39.6022722Z remote: Counting objects: 42% (345/820) 2024-12-17T23:44:39.6023103Z remote: Counting objects: 43% (353/820) 2024-12-17T23:44:39.6023471Z remote: Counting objects: 44% (361/820) 2024-12-17T23:44:39.6023850Z remote: Counting objects: 45% (369/820) 2024-12-17T23:44:39.6024231Z remote: Counting objects: 46% (378/820) 2024-12-17T23:44:39.6024613Z remote: Counting objects: 47% (386/820) 2024-12-17T23:44:39.6024991Z remote: Counting objects: 48% (394/820) 2024-12-17T23:44:39.6025368Z remote: Counting objects: 49% (402/820) 2024-12-17T23:44:39.6025735Z remote: Counting objects: 50% (410/820) 2024-12-17T23:44:39.6026109Z remote: Counting objects: 51% (419/820) 2024-12-17T23:44:39.6026480Z remote: Counting objects: 52% (427/820) 2024-12-17T23:44:39.6027005Z remote: Counting objects: 53% (435/820) 2024-12-17T23:44:39.6027431Z remote: Counting objects: 54% (443/820) 2024-12-17T23:44:39.6027809Z remote: Counting objects: 55% (451/820) 2024-12-17T23:44:39.6028173Z remote: Counting objects: 56% (460/820) 2024-12-17T23:44:39.6028546Z remote: Counting objects: 57% (468/820) 2024-12-17T23:44:39.6028922Z remote: Counting objects: 58% (476/820) 2024-12-17T23:44:39.6029295Z remote: Counting objects: 59% (484/820) 2024-12-17T23:44:39.6029674Z remote: Counting objects: 60% (492/820) 2024-12-17T23:44:39.6030036Z remote: Counting objects: 61% (501/820) 2024-12-17T23:44:39.6030411Z remote: Counting objects: 62% (509/820) 2024-12-17T23:44:39.6030822Z remote: Counting objects: 63% (517/820) 2024-12-17T23:44:39.6031255Z remote: Counting objects: 64% (525/820) 2024-12-17T23:44:39.6031632Z remote: Counting objects: 65% (533/820) 2024-12-17T23:44:39.6032016Z remote: Counting objects: 66% (542/820) 2024-12-17T23:44:39.6032391Z remote: Counting objects: 67% (550/820) 2024-12-17T23:44:39.6032753Z remote: Counting objects: 68% (558/820) 2024-12-17T23:44:39.6033126Z remote: Counting objects: 69% (566/820) 2024-12-17T23:44:39.6033503Z remote: Counting objects: 70% (574/820) 2024-12-17T23:44:39.6033876Z remote: Counting objects: 71% (583/820) 2024-12-17T23:44:39.6034283Z remote: Counting objects: 72% (591/820) 2024-12-17T23:44:39.6034723Z remote: Counting objects: 73% (599/820) 2024-12-17T23:44:39.6035102Z remote: Counting objects: 74% (607/820) 2024-12-17T23:44:39.6035483Z remote: Counting objects: 75% (615/820) 2024-12-17T23:44:39.6035978Z remote: Counting objects: 76% (624/820) 2024-12-17T23:44:39.6036361Z remote: Counting objects: 77% (632/820) 2024-12-17T23:44:39.6036742Z remote: Counting objects: 78% (640/820) 2024-12-17T23:44:39.6037126Z remote: Counting objects: 79% (648/820) 2024-12-17T23:44:39.6037503Z remote: Counting objects: 80% (656/820) 2024-12-17T23:44:39.6037884Z remote: Counting objects: 81% (665/820) 2024-12-17T23:44:39.6038264Z remote: Counting objects: 82% (673/820) 2024-12-17T23:44:39.6038641Z remote: Counting objects: 83% (681/820) 2024-12-17T23:44:39.6039004Z remote: Counting objects: 84% (689/820) 2024-12-17T23:44:39.6039378Z remote: Counting objects: 85% (697/820) 2024-12-17T23:44:39.6039764Z remote: Counting objects: 86% (706/820) 2024-12-17T23:44:39.6040150Z remote: Counting objects: 87% (714/820) 2024-12-17T23:44:39.6040524Z remote: Counting objects: 88% (722/820) 2024-12-17T23:44:39.6040890Z remote: Counting objects: 89% (730/820) 2024-12-17T23:44:39.6041265Z remote: Counting objects: 90% (738/820) 2024-12-17T23:44:39.6041640Z remote: Counting objects: 91% (747/820) 2024-12-17T23:44:39.6042022Z remote: Counting objects: 92% (755/820) 2024-12-17T23:44:39.6042397Z remote: Counting objects: 93% (763/820) 2024-12-17T23:44:39.6042764Z remote: Counting objects: 94% (771/820) 2024-12-17T23:44:39.6043141Z remote: Counting objects: 95% (779/820) 2024-12-17T23:44:39.6043515Z remote: Counting objects: 96% (788/820) 2024-12-17T23:44:39.6043898Z remote: Counting objects: 97% (796/820) 2024-12-17T23:44:39.6044272Z remote: Counting objects: 98% (804/820) 2024-12-17T23:44:39.6044638Z remote: Counting objects: 99% (812/820) 2024-12-17T23:44:39.6045014Z remote: Counting objects: 100% (820/820) 2024-12-17T23:44:39.6045418Z remote: Counting objects: 100% (820/820), done. 2024-12-17T23:44:39.6086695Z remote: Compressing objects: 0% (1/315) 2024-12-17T23:44:39.6121329Z remote: Compressing objects: 1% (4/315) 2024-12-17T23:44:39.6163278Z remote: Compressing objects: 2% (7/315) 2024-12-17T23:44:39.6235851Z remote: Compressing objects: 3% (10/315) 2024-12-17T23:44:39.6255400Z remote: Compressing objects: 4% (13/315) 2024-12-17T23:44:39.6573502Z remote: Compressing objects: 5% (16/315) 2024-12-17T23:44:39.7021447Z remote: Compressing objects: 6% (19/315) 2024-12-17T23:44:39.7534347Z remote: Compressing objects: 7% (23/315) 2024-12-17T23:44:39.8109787Z remote: Compressing objects: 8% (26/315) 2024-12-17T23:44:39.8543580Z remote: Compressing objects: 9% (29/315) 2024-12-17T23:44:39.8845038Z remote: Compressing objects: 10% (32/315) 2024-12-17T23:44:39.8948991Z remote: Compressing objects: 11% (35/315) 2024-12-17T23:44:39.9073050Z remote: Compressing objects: 12% (38/315) 2024-12-17T23:44:39.9162246Z remote: Compressing objects: 13% (41/315) 2024-12-17T23:44:39.9165172Z remote: Compressing objects: 14% (45/315) 2024-12-17T23:44:39.9167824Z remote: Compressing objects: 15% (48/315) 2024-12-17T23:44:39.9170337Z remote: Compressing objects: 16% (51/315) 2024-12-17T23:44:39.9172899Z remote: Compressing objects: 17% (54/315) 2024-12-17T23:44:39.9175580Z remote: Compressing objects: 18% (57/315) 2024-12-17T23:44:39.9175979Z remote: Compressing objects: 19% (60/315) 2024-12-17T23:44:39.9178131Z remote: Compressing objects: 20% (63/315) 2024-12-17T23:44:39.9181679Z remote: Compressing objects: 21% (67/315) 2024-12-17T23:44:39.9184957Z remote: Compressing objects: 22% (70/315) 2024-12-17T23:44:39.9188181Z remote: Compressing objects: 23% (73/315) 2024-12-17T23:44:39.9190462Z remote: Compressing objects: 24% (76/315) 2024-12-17T23:44:39.9193127Z remote: Compressing objects: 25% (79/315) 2024-12-17T23:44:39.9198423Z remote: Compressing objects: 26% (82/315) 2024-12-17T23:44:39.9204330Z remote: Compressing objects: 27% (86/315) 2024-12-17T23:44:39.9207128Z remote: Compressing objects: 28% (89/315) 2024-12-17T23:44:39.9207540Z remote: Compressing objects: 29% (92/315) 2024-12-17T23:44:39.9209603Z remote: Compressing objects: 30% (95/315) 2024-12-17T23:44:39.9216147Z remote: Compressing objects: 31% (98/315) 2024-12-17T23:44:39.9218702Z remote: Compressing objects: 32% (101/315) 2024-12-17T23:44:39.9223302Z remote: Compressing objects: 33% (104/315) 2024-12-17T23:44:39.9228645Z remote: Compressing objects: 34% (108/315) 2024-12-17T23:44:39.9233274Z remote: Compressing objects: 35% (111/315) 2024-12-17T23:44:39.9235593Z remote: Compressing objects: 36% (114/315) 2024-12-17T23:44:39.9238133Z remote: Compressing objects: 37% (117/315) 2024-12-17T23:44:39.9240622Z remote: Compressing objects: 38% (120/315) 2024-12-17T23:44:39.9243476Z remote: Compressing objects: 39% (123/315) 2024-12-17T23:44:39.9249275Z remote: Compressing objects: 40% (126/315) 2024-12-17T23:44:39.9251754Z remote: Compressing objects: 41% (130/315) 2024-12-17T23:44:39.9252166Z remote: Compressing objects: 42% (133/315) 2024-12-17T23:44:39.9256451Z remote: Compressing objects: 43% (136/315) 2024-12-17T23:44:39.9259595Z remote: Compressing objects: 44% (139/315) 2024-12-17T23:44:39.9262192Z remote: Compressing objects: 45% (142/315) 2024-12-17T23:44:39.9264828Z remote: Compressing objects: 46% (145/315) 2024-12-17T23:44:39.9267313Z remote: Compressing objects: 47% (149/315) 2024-12-17T23:44:39.9269942Z remote: Compressing objects: 48% (152/315) 2024-12-17T23:44:39.9270353Z remote: Compressing objects: 49% (155/315) 2024-12-17T23:44:39.9272439Z remote: Compressing objects: 50% (158/315) 2024-12-17T23:44:39.9275044Z remote: Compressing objects: 51% (161/315) 2024-12-17T23:44:39.9275461Z remote: Compressing objects: 52% (164/315) 2024-12-17T23:44:39.9277677Z remote: Compressing objects: 53% (167/315) 2024-12-17T23:44:39.9280219Z remote: Compressing objects: 54% (171/315) 2024-12-17T23:44:39.9280629Z remote: Compressing objects: 55% (174/315) 2024-12-17T23:44:39.9282633Z remote: Compressing objects: 56% (177/315) 2024-12-17T23:44:39.9285244Z remote: Compressing objects: 57% (180/315) 2024-12-17T23:44:39.9287787Z remote: Compressing objects: 58% (183/315) 2024-12-17T23:44:39.9290418Z remote: Compressing objects: 59% (186/315) 2024-12-17T23:44:39.9290828Z remote: Compressing objects: 60% (189/315) 2024-12-17T23:44:39.9293013Z remote: Compressing objects: 61% (193/315) 2024-12-17T23:44:39.9295537Z remote: Compressing objects: 62% (196/315) 2024-12-17T23:44:39.9299318Z remote: Compressing objects: 63% (199/315) 2024-12-17T23:44:39.9302757Z remote: Compressing objects: 64% (202/315) 2024-12-17T23:44:39.9305317Z remote: Compressing objects: 65% (205/315) 2024-12-17T23:44:39.9307780Z remote: Compressing objects: 66% (208/315) 2024-12-17T23:44:39.9310448Z remote: Compressing objects: 67% (212/315) 2024-12-17T23:44:39.9312954Z remote: Compressing objects: 68% (215/315) 2024-12-17T23:44:39.9315535Z remote: Compressing objects: 69% (218/315) 2024-12-17T23:44:39.9318047Z remote: Compressing objects: 70% (221/315) 2024-12-17T23:44:39.9320647Z remote: Compressing objects: 71% (224/315) 2024-12-17T23:44:39.9321052Z remote: Compressing objects: 72% (227/315) 2024-12-17T23:44:39.9323375Z remote: Compressing objects: 73% (230/315) 2024-12-17T23:44:39.9323787Z remote: Compressing objects: 74% (234/315) 2024-12-17T23:44:39.9324190Z remote: Compressing objects: 75% (237/315) 2024-12-17T23:44:39.9325818Z remote: Compressing objects: 76% (240/315) 2024-12-17T23:44:39.9326226Z remote: Compressing objects: 77% (243/315) 2024-12-17T23:44:39.9326637Z remote: Compressing objects: 78% (246/315) 2024-12-17T23:44:39.9328296Z remote: Compressing objects: 79% (249/315) 2024-12-17T23:44:39.9328700Z remote: Compressing objects: 80% (252/315) 2024-12-17T23:44:39.9330853Z remote: Compressing objects: 81% (256/315) 2024-12-17T23:44:39.9333451Z remote: Compressing objects: 82% (259/315) 2024-12-17T23:44:39.9333859Z remote: Compressing objects: 83% (262/315) 2024-12-17T23:44:39.9336010Z remote: Compressing objects: 84% (265/315) 2024-12-17T23:44:39.9338510Z remote: Compressing objects: 85% (268/315) 2024-12-17T23:44:39.9341385Z remote: Compressing objects: 86% (271/315) 2024-12-17T23:44:39.9341792Z remote: Compressing objects: 87% (275/315) 2024-12-17T23:44:39.9342179Z remote: Compressing objects: 88% (278/315) 2024-12-17T23:44:39.9343822Z remote: Compressing objects: 89% (281/315) 2024-12-17T23:44:39.9344226Z remote: Compressing objects: 90% (284/315) 2024-12-17T23:44:39.9346280Z remote: Compressing objects: 91% (287/315) 2024-12-17T23:44:39.9346698Z remote: Compressing objects: 92% (290/315) 2024-12-17T23:44:39.9347106Z remote: Compressing objects: 93% (293/315) 2024-12-17T23:44:39.9348761Z remote: Compressing objects: 94% (297/315) 2024-12-17T23:44:39.9349170Z remote: Compressing objects: 95% (300/315) 2024-12-17T23:44:39.9351299Z remote: Compressing objects: 96% (303/315) 2024-12-17T23:44:39.9351700Z remote: Compressing objects: 97% (306/315) 2024-12-17T23:44:39.9352113Z remote: Compressing objects: 98% (309/315) 2024-12-17T23:44:39.9352513Z remote: Compressing objects: 99% (312/315) 2024-12-17T23:44:39.9354523Z remote: Compressing objects: 100% (315/315) 2024-12-17T23:44:39.9354997Z remote: Compressing objects: 100% (315/315), done. 2024-12-17T23:45:01.7154439Z remote: Total 1057229 (delta 664), reused 518 (delta 505), pack-reused 1056409 (from 3) 2024-12-17T23:45:30.5248791Z [command]/usr/bin/git rev-parse --verify --quiet 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7^{object} 2024-12-17T23:45:30.5269778Z 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:45:30.5274191Z ##[endgroup] 2024-12-17T23:45:30.5274902Z ##[group]Determining the checkout info 2024-12-17T23:45:30.5275847Z ##[endgroup] 2024-12-17T23:45:30.5276514Z ##[group]Checking out the ref 2024-12-17T23:45:30.5279833Z [command]/usr/bin/git checkout --quiet --force 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:45:31.9387723Z ##[endgroup] 2024-12-17T23:45:31.9388433Z ##[group]Setting up auth for fetching submodules 2024-12-17T23:45:31.9392799Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2024-12-17T23:45:31.9428772Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2024-12-17T23:45:31.9450021Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2024-12-17T23:45:31.9471212Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2024-12-17T23:45:31.9493016Z ##[endgroup] 2024-12-17T23:45:31.9493446Z ##[group]Fetching submodules 2024-12-17T23:45:31.9494902Z [command]/usr/bin/git submodule sync --recursive 2024-12-17T23:45:31.9762852Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2024-12-17T23:45:32.0021702Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2024-12-17T23:45:32.0023527Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2024-12-17T23:45:32.0025717Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2024-12-17T23:45:32.0028692Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2024-12-17T23:45:32.0032389Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2024-12-17T23:45:32.0036815Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2024-12-17T23:45:32.0040742Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2024-12-17T23:45:32.0045094Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2024-12-17T23:45:32.0048292Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2024-12-17T23:45:32.0051220Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2024-12-17T23:45:32.0054115Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2024-12-17T23:45:32.0057290Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2024-12-17T23:45:32.0060349Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2024-12-17T23:45:32.0064187Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2024-12-17T23:45:32.0067614Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2024-12-17T23:45:32.0071886Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2024-12-17T23:45:32.0077031Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2024-12-17T23:45:32.0080982Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:45:32.0084678Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2024-12-17T23:45:32.0088676Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2024-12-17T23:45:32.0092475Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2024-12-17T23:45:32.0096510Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2024-12-17T23:45:32.0101154Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2024-12-17T23:45:32.0105440Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2024-12-17T23:45:32.0109664Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2024-12-17T23:45:32.0114088Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2024-12-17T23:45:32.0118697Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2024-12-17T23:45:32.0123513Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2024-12-17T23:45:32.0127953Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2024-12-17T23:45:32.0132938Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2024-12-17T23:45:32.0139551Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2024-12-17T23:45:32.0144764Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2024-12-17T23:45:32.0149687Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2024-12-17T23:45:32.0156370Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2024-12-17T23:45:32.0161711Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2024-12-17T23:45:32.0167049Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2024-12-17T23:45:32.0193596Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2024-12-17T23:45:32.2993273Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2024-12-17T23:45:32.4985209Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2024-12-17T23:45:32.6803707Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2024-12-17T23:45:32.9205408Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2024-12-17T23:45:33.2348858Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2024-12-17T23:45:35.3301283Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2024-12-17T23:45:48.9973665Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2024-12-17T23:45:49.4014005Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2024-12-17T23:45:51.5506795Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2024-12-17T23:45:52.1229358Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2024-12-17T23:45:52.7555492Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2024-12-17T23:45:53.9288638Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2024-12-17T23:45:55.9579387Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2024-12-17T23:46:01.0743599Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2024-12-17T23:46:03.0472818Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2024-12-17T23:46:04.4329725Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2024-12-17T23:46:06.1574485Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2024-12-17T23:46:06.5339480Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2024-12-17T23:46:06.8584130Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2024-12-17T23:46:07.9843251Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2024-12-17T23:46:08.3510601Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2024-12-17T23:46:08.6183797Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2024-12-17T23:46:10.0891328Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2024-12-17T23:46:11.0311372Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2024-12-17T23:46:11.3997334Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2024-12-17T23:46:18.7041179Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2024-12-17T23:46:20.9995202Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2024-12-17T23:46:28.4846469Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2024-12-17T23:46:28.7172456Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2024-12-17T23:46:38.9320485Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2024-12-17T23:46:39.1215430Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2024-12-17T23:46:39.3394455Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2024-12-17T23:46:40.4423651Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2024-12-17T23:46:40.7451981Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2024-12-17T23:46:41.3734341Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2024-12-17T23:46:41.7916866Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2024-12-17T23:46:41.8028639Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2024-12-17T23:46:41.8114289Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2024-12-17T23:46:41.8344921Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2024-12-17T23:46:41.8667649Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1' 2024-12-17T23:46:41.9030606Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2024-12-17T23:46:42.5874796Z Submodule path 'third_party/XNNPACK': checked out '4ea82e595b36106653175dcb04b2aa532660d0d8' 2024-12-17T23:46:42.6098103Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2024-12-17T23:46:42.8326793Z Submodule path 'third_party/composable_kernel': checked out '50ee4267e27b875d149e642f4cebd47be1dc3b57' 2024-12-17T23:46:42.8799805Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2024-12-17T23:46:42.9750646Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99' 2024-12-17T23:46:43.0079148Z Submodule path 'third_party/cudnn_frontend': checked out '936021bfed8c91dc416af1588b2c4eca631a9e45' 2024-12-17T23:46:43.4951595Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49' 2024-12-17T23:46:43.7405845Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2024-12-17T23:46:43.8347450Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2024-12-17T23:46:43.8365024Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:46:43.8367192Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:46:43.8369525Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:46:43.8371921Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:46:43.8374721Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:46:43.8399812Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2024-12-17T23:46:44.6996659Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2024-12-17T23:46:45.3191129Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2024-12-17T23:46:47.3695345Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2024-12-17T23:46:48.5534033Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2024-12-17T23:46:48.9167066Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2024-12-17T23:46:49.0097257Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2024-12-17T23:46:49.4000475Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2024-12-17T23:46:49.4613681Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2024-12-17T23:46:49.4735805Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2024-12-17T23:46:49.6013465Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2024-12-17T23:46:49.6398611Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592' 2024-12-17T23:46:49.6797110Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2024-12-17T23:46:49.7052640Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2024-12-17T23:46:49.7481280Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59' 2024-12-17T23:46:49.7607753Z Submodule path 'third_party/ideep': checked out 'c7ccd5bdbe5434ba156f4e856dcef0601637334b' 2024-12-17T23:46:49.7623143Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2024-12-17T23:46:49.7645489Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2024-12-17T23:47:04.9368069Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af' 2024-12-17T23:47:04.9546374Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2024-12-17T23:47:05.0379953Z Submodule path 'third_party/kineto': checked out '338140f58a28d599da3434ced4fd2d75dd1a213d' 2024-12-17T23:47:05.0397040Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:47:05.0398844Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:47:05.0401472Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:47:05.0426237Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2024-12-17T23:47:06.0163172Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2024-12-17T23:47:07.4004183Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2024-12-17T23:47:08.6251087Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2024-12-17T23:47:08.6266439Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:47:08.6268843Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:47:08.6271116Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:47:08.6273604Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:47:08.6276288Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:47:08.6279096Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:47:08.6281860Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:47:08.6284730Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:47:08.6310335Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2024-12-17T23:47:09.5156493Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2024-12-17T23:47:09.8848956Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2024-12-17T23:47:11.2561431Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2024-12-17T23:47:11.5731451Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2024-12-17T23:47:12.0779056Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2024-12-17T23:47:13.2617369Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2024-12-17T23:47:20.6868857Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2024-12-17T23:47:21.0718291Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2024-12-17T23:47:21.0889474Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2024-12-17T23:47:21.1239298Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2024-12-17T23:47:21.1362714Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2024-12-17T23:47:21.1376566Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:47:21.1400259Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2024-12-17T23:47:21.4430787Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2024-12-17T23:47:21.4602946Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2024-12-17T23:47:21.4988749Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2024-12-17T23:47:21.5955550Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2024-12-17T23:47:21.6110340Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2024-12-17T23:47:21.6478013Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2024-12-17T23:47:21.7025475Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2024-12-17T23:47:21.7367703Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2024-12-17T23:47:21.7651091Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2024-12-17T23:47:21.8705383Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2024-12-17T23:47:22.2133813Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c' 2024-12-17T23:47:22.2167294Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2024-12-17T23:47:22.2193142Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2024-12-17T23:47:23.4220680Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b' 2024-12-17T23:47:23.4872932Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2024-12-17T23:47:23.4891610Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:47:23.4893602Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:47:23.4896026Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:47:23.4898597Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:47:23.4901518Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:47:23.4904117Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:47:23.4906636Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-17T23:47:23.4909303Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-17T23:47:23.4934166Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2024-12-17T23:47:23.9764867Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2024-12-17T23:47:25.1208191Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2024-12-17T23:47:25.4555434Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2024-12-17T23:47:32.7546736Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2024-12-17T23:47:33.0436514Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2024-12-17T23:47:33.2654652Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2024-12-17T23:47:33.5817129Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2024-12-17T23:47:40.8534340Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2024-12-17T23:47:40.8918684Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2024-12-17T23:47:40.9068902Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa' 2024-12-17T23:47:41.0105764Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d' 2024-12-17T23:47:41.0229948Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce' 2024-12-17T23:47:41.0366058Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5' 2024-12-17T23:47:41.0510618Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d' 2024-12-17T23:47:41.0524231Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-17T23:47:41.0526495Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-17T23:47:41.0549955Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2024-12-17T23:47:43.1529694Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2024-12-17T23:47:44.5108440Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2024-12-17T23:47:44.5554632Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2024-12-17T23:47:44.9886084Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50' 2024-12-17T23:47:44.9995464Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe' 2024-12-17T23:47:45.2661821Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a' 2024-12-17T23:47:45.2683108Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:47:45.2684800Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest' 2024-12-17T23:47:45.2709863Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'... 2024-12-17T23:47:45.7377438Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'... 2024-12-17T23:47:46.8919298Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8' 2024-12-17T23:47:46.9601017Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081' 2024-12-17T23:47:46.9687133Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900' 2024-12-17T23:47:46.9799956Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8' 2024-12-17T23:47:47.0146265Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2024-12-17T23:47:47.0422554Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67' 2024-12-17T23:47:47.0832019Z Submodule path 'third_party/sleef': checked out '60e76d2bce17d278b439d9da17177c8f957a9e9b' 2024-12-17T23:47:47.1083439Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e' 2024-12-17T23:47:47.1098388Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:47:47.1101233Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:47:47.1103545Z Submodule 'third_party/libuv' (https://github.com/libuv/libuv.git) registered for path 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:47:47.1106071Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:47:47.1129729Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/googletest'... 2024-12-17T23:47:48.2887537Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libnop'... 2024-12-17T23:47:48.5261741Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libuv'... 2024-12-17T23:47:50.8599187Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11'... 2024-12-17T23:47:51.9967108Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e' 2024-12-17T23:47:52.0111826Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281' 2024-12-17T23:47:52.0695614Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242' 2024-12-17T23:47:52.0967958Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef' 2024-12-17T23:47:52.0980461Z Submodule 'tools/clang' (https://github.com/wjakob/clang-cindex-python3) registered for path 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:47:52.1004267Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11/tools/clang'... 2024-12-17T23:47:52.3091994Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5' 2024-12-17T23:47:52.3123837Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2024-12-17T23:47:52.3389259Z Entering 'android/libs/fbjni' 2024-12-17T23:47:52.3428386Z Entering 'third_party/FP16' 2024-12-17T23:47:52.3465448Z Entering 'third_party/FXdiv' 2024-12-17T23:47:52.3502881Z Entering 'third_party/NNPACK' 2024-12-17T23:47:52.3541142Z Entering 'third_party/NVTX' 2024-12-17T23:47:52.3578963Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-17T23:47:52.3616939Z Entering 'third_party/XNNPACK' 2024-12-17T23:47:52.3670272Z Entering 'third_party/benchmark' 2024-12-17T23:47:52.3708984Z Entering 'third_party/composable_kernel' 2024-12-17T23:47:52.3752101Z Entering 'third_party/cpp-httplib' 2024-12-17T23:47:52.3789490Z Entering 'third_party/cpuinfo' 2024-12-17T23:47:52.3830188Z Entering 'third_party/cudnn_frontend' 2024-12-17T23:47:52.3868960Z Entering 'third_party/cutlass' 2024-12-17T23:47:52.3914321Z Entering 'third_party/eigen' 2024-12-17T23:47:52.3953720Z Entering 'third_party/fbgemm' 2024-12-17T23:47:52.3991971Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:47:52.4030421Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:47:52.4066971Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:47:52.4111416Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:47:52.4148439Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:47:52.4186600Z Entering 'third_party/flatbuffers' 2024-12-17T23:47:52.4227401Z Entering 'third_party/fmt' 2024-12-17T23:47:52.4265602Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:47:52.4303440Z Entering 'third_party/gloo' 2024-12-17T23:47:52.4341337Z Entering 'third_party/googletest' 2024-12-17T23:47:52.4378617Z Entering 'third_party/ideep' 2024-12-17T23:47:52.4419394Z Entering 'third_party/ideep/mkl-dnn' 2024-12-17T23:47:52.4463955Z Entering 'third_party/ittapi' 2024-12-17T23:47:52.4501395Z Entering 'third_party/kineto' 2024-12-17T23:47:52.4539105Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:47:52.4576366Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:47:52.4616137Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:47:52.4654745Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:47:52.4692036Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:47:52.4732831Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:47:52.4771974Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:47:52.4809068Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:47:52.4845454Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:47:52.4883164Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:47:52.4921743Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:47:52.4958027Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:47:52.4995065Z Entering 'third_party/mimalloc' 2024-12-17T23:47:52.5033541Z Entering 'third_party/nccl/nccl' 2024-12-17T23:47:52.5070766Z Entering 'third_party/nlohmann' 2024-12-17T23:47:52.5109513Z Entering 'third_party/onnx' 2024-12-17T23:47:52.5163917Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-17T23:47:52.5203486Z Entering 'third_party/opentelemetry-cpp' 2024-12-17T23:47:52.5244332Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:47:52.5281041Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:47:52.5318941Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:47:52.5355062Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:47:52.5393435Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:47:52.5429729Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:47:52.5465912Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-17T23:47:52.5501936Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-17T23:47:52.5541052Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-17T23:47:52.5579006Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-17T23:47:52.5635067Z Entering 'third_party/pocketfft' 2024-12-17T23:47:52.5675830Z Entering 'third_party/protobuf' 2024-12-17T23:47:52.5714183Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:47:52.5749964Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-17T23:47:52.5787520Z Entering 'third_party/psimd' 2024-12-17T23:47:52.5825681Z Entering 'third_party/pthreadpool' 2024-12-17T23:47:52.5864934Z Entering 'third_party/pybind11' 2024-12-17T23:47:52.5903113Z Entering 'third_party/python-peachpy' 2024-12-17T23:47:52.5940555Z Entering 'third_party/sleef' 2024-12-17T23:47:52.5978355Z Entering 'third_party/tensorpipe' 2024-12-17T23:47:52.6016163Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:47:52.6052687Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:47:52.6088630Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:47:52.6126884Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:47:52.6163480Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:47:52.6213487Z ##[endgroup] 2024-12-17T23:47:52.6216383Z ##[group]Persisting credentials for submodules 2024-12-17T23:47:52.6219020Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :" 2024-12-17T23:47:52.6487366Z Entering 'android/libs/fbjni' 2024-12-17T23:47:52.6538578Z Entering 'third_party/FP16' 2024-12-17T23:47:52.6588359Z Entering 'third_party/FXdiv' 2024-12-17T23:47:52.6638217Z Entering 'third_party/NNPACK' 2024-12-17T23:47:52.6688560Z Entering 'third_party/NVTX' 2024-12-17T23:47:52.6740946Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-17T23:47:52.6790381Z Entering 'third_party/XNNPACK' 2024-12-17T23:47:52.6855390Z Entering 'third_party/benchmark' 2024-12-17T23:47:52.6904626Z Entering 'third_party/composable_kernel' 2024-12-17T23:47:52.6959505Z Entering 'third_party/cpp-httplib' 2024-12-17T23:47:52.7008899Z Entering 'third_party/cpuinfo' 2024-12-17T23:47:52.7058097Z Entering 'third_party/cudnn_frontend' 2024-12-17T23:47:52.7107558Z Entering 'third_party/cutlass' 2024-12-17T23:47:52.7164303Z Entering 'third_party/eigen' 2024-12-17T23:47:52.7215472Z Entering 'third_party/fbgemm' 2024-12-17T23:47:52.7264495Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:47:52.7314774Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:47:52.7362602Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:47:52.7417585Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:47:52.7465750Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:47:52.7517581Z Entering 'third_party/flatbuffers' 2024-12-17T23:47:52.7569235Z Entering 'third_party/fmt' 2024-12-17T23:47:52.7619318Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:47:52.7668662Z Entering 'third_party/gloo' 2024-12-17T23:47:52.7717845Z Entering 'third_party/googletest' 2024-12-17T23:47:52.7766555Z Entering 'third_party/ideep' 2024-12-17T23:47:52.7815992Z Entering 'third_party/ideep/mkl-dnn' 2024-12-17T23:47:52.7871631Z Entering 'third_party/ittapi' 2024-12-17T23:47:52.7921633Z Entering 'third_party/kineto' 2024-12-17T23:47:52.7969970Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:47:52.8018522Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:47:52.8068654Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:47:52.8117042Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:47:52.8165151Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:47:52.8212310Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:47:52.8262974Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:47:52.8311045Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:47:52.8359544Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:47:52.8410075Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:47:52.8459628Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:47:52.8508052Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:47:52.8557428Z Entering 'third_party/mimalloc' 2024-12-17T23:47:52.8608297Z Entering 'third_party/nccl/nccl' 2024-12-17T23:47:52.8658219Z Entering 'third_party/nlohmann' 2024-12-17T23:47:52.8709015Z Entering 'third_party/onnx' 2024-12-17T23:47:52.8773363Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-17T23:47:52.8825218Z Entering 'third_party/opentelemetry-cpp' 2024-12-17T23:47:52.8876723Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:47:52.8926475Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:47:52.8974354Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:47:52.9022178Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:47:52.9071354Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:47:52.9119269Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:47:52.9167035Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-17T23:47:52.9215888Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-17T23:47:52.9265634Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-17T23:47:52.9314481Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-17T23:47:52.9382835Z Entering 'third_party/pocketfft' 2024-12-17T23:47:52.9432795Z Entering 'third_party/protobuf' 2024-12-17T23:47:52.9483908Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:47:52.9533293Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-17T23:47:52.9582757Z Entering 'third_party/psimd' 2024-12-17T23:47:52.9632339Z Entering 'third_party/pthreadpool' 2024-12-17T23:47:52.9681392Z Entering 'third_party/pybind11' 2024-12-17T23:47:52.9730967Z Entering 'third_party/python-peachpy' 2024-12-17T23:47:52.9779814Z Entering 'third_party/sleef' 2024-12-17T23:47:52.9829568Z Entering 'third_party/tensorpipe' 2024-12-17T23:47:52.9878130Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:47:52.9926296Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:47:52.9974139Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:47:53.0022645Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:47:53.0069388Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:47:53.0133296Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url" 2024-12-17T23:47:53.0394235Z Entering 'android/libs/fbjni' 2024-12-17T23:47:53.0440648Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2024-12-17T23:47:53.0454766Z Entering 'third_party/FP16' 2024-12-17T23:47:53.0500969Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2024-12-17T23:47:53.0515333Z Entering 'third_party/FXdiv' 2024-12-17T23:47:53.0561228Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2024-12-17T23:47:53.0575637Z Entering 'third_party/NNPACK' 2024-12-17T23:47:53.0621386Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config remote.origin.url 2024-12-17T23:47:53.0636224Z Entering 'third_party/NVTX' 2024-12-17T23:47:53.0682308Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config remote.origin.url 2024-12-17T23:47:53.0699175Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-17T23:47:53.0746370Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config remote.origin.url 2024-12-17T23:47:53.0762308Z Entering 'third_party/XNNPACK' 2024-12-17T23:47:53.0807099Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config remote.origin.url 2024-12-17T23:47:53.0837882Z Entering 'third_party/benchmark' 2024-12-17T23:47:53.0883123Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config remote.origin.url 2024-12-17T23:47:53.0898880Z Entering 'third_party/composable_kernel' 2024-12-17T23:47:53.0944106Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config remote.origin.url 2024-12-17T23:47:53.0964440Z Entering 'third_party/cpp-httplib' 2024-12-17T23:47:53.1010084Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config remote.origin.url 2024-12-17T23:47:53.1025103Z Entering 'third_party/cpuinfo' 2024-12-17T23:47:53.1070136Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config remote.origin.url 2024-12-17T23:47:53.1086160Z Entering 'third_party/cudnn_frontend' 2024-12-17T23:47:53.1132046Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config remote.origin.url 2024-12-17T23:47:53.1147431Z Entering 'third_party/cutlass' 2024-12-17T23:47:53.1192846Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config remote.origin.url 2024-12-17T23:47:53.1215955Z Entering 'third_party/eigen' 2024-12-17T23:47:53.1261325Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config remote.origin.url 2024-12-17T23:47:53.1278581Z Entering 'third_party/fbgemm' 2024-12-17T23:47:53.1324811Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config remote.origin.url 2024-12-17T23:47:53.1339558Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:47:53.1384865Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config remote.origin.url 2024-12-17T23:47:53.1400519Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:47:53.1445518Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config remote.origin.url 2024-12-17T23:47:53.1460346Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:47:53.1506417Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cutlass/config remote.origin.url 2024-12-17T23:47:53.1526854Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:47:53.1571596Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config remote.origin.url 2024-12-17T23:47:53.1585578Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:47:53.1630655Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config remote.origin.url 2024-12-17T23:47:53.1646394Z Entering 'third_party/flatbuffers' 2024-12-17T23:47:53.1692224Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config remote.origin.url 2024-12-17T23:47:53.1712187Z Entering 'third_party/fmt' 2024-12-17T23:47:53.1757486Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config remote.origin.url 2024-12-17T23:47:53.1773253Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:47:53.1820801Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config remote.origin.url 2024-12-17T23:47:53.1835537Z Entering 'third_party/gloo' 2024-12-17T23:47:53.1881723Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config remote.origin.url 2024-12-17T23:47:53.1896836Z Entering 'third_party/googletest' 2024-12-17T23:47:53.1941929Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2024-12-17T23:47:53.1957836Z Entering 'third_party/ideep' 2024-12-17T23:47:53.2003979Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2024-12-17T23:47:53.2017959Z Entering 'third_party/ideep/mkl-dnn' 2024-12-17T23:47:53.2063959Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2024-12-17T23:47:53.2085962Z Entering 'third_party/ittapi' 2024-12-17T23:47:53.2131191Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2024-12-17T23:47:53.2147061Z Entering 'third_party/kineto' 2024-12-17T23:47:53.2193755Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config remote.origin.url 2024-12-17T23:47:53.2208346Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:47:53.2255212Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config remote.origin.url 2024-12-17T23:47:53.2268841Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:47:53.2316570Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config remote.origin.url 2024-12-17T23:47:53.2331930Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:47:53.2377460Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config remote.origin.url 2024-12-17T23:47:53.2391475Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:47:53.2438943Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config remote.origin.url 2024-12-17T23:47:53.2452953Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:47:53.2499981Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config remote.origin.url 2024-12-17T23:47:53.2512938Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:47:53.2560863Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config remote.origin.url 2024-12-17T23:47:53.2576582Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:47:53.2621949Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config remote.origin.url 2024-12-17T23:47:53.2636151Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:47:53.2681954Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2024-12-17T23:47:53.2696490Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:47:53.2744415Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config remote.origin.url 2024-12-17T23:47:53.2759859Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:47:53.2805693Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config remote.origin.url 2024-12-17T23:47:53.2821682Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:47:53.2868635Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config remote.origin.url 2024-12-17T23:47:53.2882339Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:47:53.2928299Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2024-12-17T23:47:53.2944384Z Entering 'third_party/mimalloc' 2024-12-17T23:47:53.2990876Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2024-12-17T23:47:53.3008784Z Entering 'third_party/nccl/nccl' 2024-12-17T23:47:53.3054680Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config remote.origin.url 2024-12-17T23:47:53.3070220Z Entering 'third_party/nlohmann' 2024-12-17T23:47:53.3117180Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2024-12-17T23:47:53.3133963Z Entering 'third_party/onnx' 2024-12-17T23:47:53.3180423Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2024-12-17T23:47:53.3210681Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-17T23:47:53.3256605Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2024-12-17T23:47:53.3273559Z Entering 'third_party/opentelemetry-cpp' 2024-12-17T23:47:53.3321960Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2024-12-17T23:47:53.3337784Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:47:53.3383949Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2024-12-17T23:47:53.3398165Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:47:53.3443581Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2024-12-17T23:47:53.3457667Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:47:53.3502556Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config remote.origin.url 2024-12-17T23:47:53.3516308Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:47:53.3561517Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config remote.origin.url 2024-12-17T23:47:53.3576590Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:47:53.3622234Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config remote.origin.url 2024-12-17T23:47:53.3635985Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:47:53.3680592Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2024-12-17T23:47:53.3694721Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-17T23:47:53.3743382Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2024-12-17T23:47:53.3756578Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-17T23:47:53.3803722Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2024-12-17T23:47:53.3819673Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-17T23:47:53.3865363Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2024-12-17T23:47:53.3880679Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-17T23:47:53.3926727Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2024-12-17T23:47:53.3960607Z Entering 'third_party/pocketfft' 2024-12-17T23:47:53.4007288Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2024-12-17T23:47:53.4022411Z Entering 'third_party/protobuf' 2024-12-17T23:47:53.4068842Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2024-12-17T23:47:53.4086206Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:47:53.4130839Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2024-12-17T23:47:53.4144943Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-17T23:47:53.4189461Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2024-12-17T23:47:53.4205789Z Entering 'third_party/psimd' 2024-12-17T23:47:53.4250471Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2024-12-17T23:47:53.4265495Z Entering 'third_party/pthreadpool' 2024-12-17T23:47:53.4311259Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2024-12-17T23:47:53.4326073Z Entering 'third_party/pybind11' 2024-12-17T23:47:53.4372309Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2024-12-17T23:47:53.4387562Z Entering 'third_party/python-peachpy' 2024-12-17T23:47:53.4433909Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2024-12-17T23:47:53.4450062Z Entering 'third_party/sleef' 2024-12-17T23:47:53.4495953Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2024-12-17T23:47:53.4511140Z Entering 'third_party/tensorpipe' 2024-12-17T23:47:53.4556445Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2024-12-17T23:47:53.4571088Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:47:53.4618488Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2024-12-17T23:47:53.4634515Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:47:53.4678681Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2024-12-17T23:47:53.4692268Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:47:53.4736816Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2024-12-17T23:47:53.4751072Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:47:53.4795735Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2024-12-17T23:47:53.4811024Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:47:53.4856886Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config remote.origin.url 2024-12-17T23:47:53.5329152Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2024-12-17T23:47:53.5600827Z Entering 'android/libs/fbjni' 2024-12-17T23:47:53.5638826Z Entering 'third_party/FP16' 2024-12-17T23:47:53.5678279Z Entering 'third_party/FXdiv' 2024-12-17T23:47:53.5716183Z Entering 'third_party/NNPACK' 2024-12-17T23:47:53.5754775Z Entering 'third_party/NVTX' 2024-12-17T23:47:53.5793140Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-17T23:47:53.5831972Z Entering 'third_party/XNNPACK' 2024-12-17T23:47:53.5884226Z Entering 'third_party/benchmark' 2024-12-17T23:47:53.5922809Z Entering 'third_party/composable_kernel' 2024-12-17T23:47:53.5967415Z Entering 'third_party/cpp-httplib' 2024-12-17T23:47:53.6005419Z Entering 'third_party/cpuinfo' 2024-12-17T23:47:53.6044491Z Entering 'third_party/cudnn_frontend' 2024-12-17T23:47:53.6081926Z Entering 'third_party/cutlass' 2024-12-17T23:47:53.6128302Z Entering 'third_party/eigen' 2024-12-17T23:47:53.6167623Z Entering 'third_party/fbgemm' 2024-12-17T23:47:53.6207241Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:47:53.6243987Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:47:53.6280565Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:47:53.6322841Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:47:53.6359231Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:47:53.6397383Z Entering 'third_party/flatbuffers' 2024-12-17T23:47:53.6437713Z Entering 'third_party/fmt' 2024-12-17T23:47:53.6474698Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:47:53.6513351Z Entering 'third_party/gloo' 2024-12-17T23:47:53.6550360Z Entering 'third_party/googletest' 2024-12-17T23:47:53.6587918Z Entering 'third_party/ideep' 2024-12-17T23:47:53.6624822Z Entering 'third_party/ideep/mkl-dnn' 2024-12-17T23:47:53.6669942Z Entering 'third_party/ittapi' 2024-12-17T23:47:53.6707817Z Entering 'third_party/kineto' 2024-12-17T23:47:53.6746458Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:47:53.6783037Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:47:53.6821961Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:47:53.6859867Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:47:53.6898355Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:47:53.6936724Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:47:53.6976592Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:47:53.7015049Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:47:53.7053195Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:47:53.7092409Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:47:53.7132284Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:47:53.7169364Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:47:53.7209637Z Entering 'third_party/mimalloc' 2024-12-17T23:47:53.7248434Z Entering 'third_party/nccl/nccl' 2024-12-17T23:47:53.7287390Z Entering 'third_party/nlohmann' 2024-12-17T23:47:53.7326975Z Entering 'third_party/onnx' 2024-12-17T23:47:53.7379667Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-17T23:47:53.7421599Z Entering 'third_party/opentelemetry-cpp' 2024-12-17T23:47:53.7460670Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:47:53.7496933Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:47:53.7533695Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:47:53.7569785Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:47:53.7607169Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:47:53.7643871Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:47:53.7680580Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-17T23:47:53.7717868Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-17T23:47:53.7757057Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-17T23:47:53.7795926Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-17T23:47:53.7855113Z Entering 'third_party/pocketfft' 2024-12-17T23:47:53.7893549Z Entering 'third_party/protobuf' 2024-12-17T23:47:53.7935439Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:47:53.7973154Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-17T23:47:53.8012358Z Entering 'third_party/psimd' 2024-12-17T23:47:53.8051402Z Entering 'third_party/pthreadpool' 2024-12-17T23:47:53.8089702Z Entering 'third_party/pybind11' 2024-12-17T23:47:53.8128559Z Entering 'third_party/python-peachpy' 2024-12-17T23:47:53.8166128Z Entering 'third_party/sleef' 2024-12-17T23:47:53.8205871Z Entering 'third_party/tensorpipe' 2024-12-17T23:47:53.8244305Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:47:53.8280988Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:47:53.8317242Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:47:53.8354277Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:47:53.8390455Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:47:53.8441456Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2024-12-17T23:47:53.8703055Z Entering 'android/libs/fbjni' 2024-12-17T23:47:53.8740967Z Entering 'third_party/FP16' 2024-12-17T23:47:53.8778565Z Entering 'third_party/FXdiv' 2024-12-17T23:47:53.8816252Z Entering 'third_party/NNPACK' 2024-12-17T23:47:53.8854566Z Entering 'third_party/NVTX' 2024-12-17T23:47:53.8892975Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-17T23:47:53.8931721Z Entering 'third_party/XNNPACK' 2024-12-17T23:47:53.8986548Z Entering 'third_party/benchmark' 2024-12-17T23:47:53.9025842Z Entering 'third_party/composable_kernel' 2024-12-17T23:47:53.9069463Z Entering 'third_party/cpp-httplib' 2024-12-17T23:47:53.9106790Z Entering 'third_party/cpuinfo' 2024-12-17T23:47:53.9144068Z Entering 'third_party/cudnn_frontend' 2024-12-17T23:47:53.9181014Z Entering 'third_party/cutlass' 2024-12-17T23:47:53.9224835Z Entering 'third_party/eigen' 2024-12-17T23:47:53.9263436Z Entering 'third_party/fbgemm' 2024-12-17T23:47:53.9301422Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-17T23:47:53.9339613Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-17T23:47:53.9376053Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-17T23:47:53.9420068Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-17T23:47:53.9456961Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-17T23:47:53.9494633Z Entering 'third_party/flatbuffers' 2024-12-17T23:47:53.9536789Z Entering 'third_party/fmt' 2024-12-17T23:47:53.9573909Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-17T23:47:53.9612561Z Entering 'third_party/gloo' 2024-12-17T23:47:53.9651246Z Entering 'third_party/googletest' 2024-12-17T23:47:53.9689441Z Entering 'third_party/ideep' 2024-12-17T23:47:53.9728050Z Entering 'third_party/ideep/mkl-dnn' 2024-12-17T23:47:53.9773436Z Entering 'third_party/ittapi' 2024-12-17T23:47:53.9814425Z Entering 'third_party/kineto' 2024-12-17T23:47:53.9853474Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-17T23:47:53.9892826Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-17T23:47:53.9933275Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-17T23:47:53.9970857Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-17T23:47:54.0009078Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-17T23:47:54.0046176Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-17T23:47:54.0085249Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-17T23:47:54.0122785Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-17T23:47:54.0159311Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-17T23:47:54.0197008Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-17T23:47:54.0235514Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-17T23:47:54.0272196Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-17T23:47:54.0310313Z Entering 'third_party/mimalloc' 2024-12-17T23:47:54.0348162Z Entering 'third_party/nccl/nccl' 2024-12-17T23:47:54.0385258Z Entering 'third_party/nlohmann' 2024-12-17T23:47:54.0427035Z Entering 'third_party/onnx' 2024-12-17T23:47:54.0479392Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-17T23:47:54.0518904Z Entering 'third_party/opentelemetry-cpp' 2024-12-17T23:47:54.0557670Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-17T23:47:54.0594070Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-17T23:47:54.0630862Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-17T23:47:54.0667434Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-17T23:47:54.0706796Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-17T23:47:54.0743570Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-17T23:47:54.0780521Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-17T23:47:54.0817718Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-17T23:47:54.0857519Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-17T23:47:54.0896539Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-17T23:47:54.0952993Z Entering 'third_party/pocketfft' 2024-12-17T23:47:54.0990692Z Entering 'third_party/protobuf' 2024-12-17T23:47:54.1031446Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-17T23:47:54.1068079Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-17T23:47:54.1106441Z Entering 'third_party/psimd' 2024-12-17T23:47:54.1143345Z Entering 'third_party/pthreadpool' 2024-12-17T23:47:54.1180189Z Entering 'third_party/pybind11' 2024-12-17T23:47:54.1218283Z Entering 'third_party/python-peachpy' 2024-12-17T23:47:54.1256172Z Entering 'third_party/sleef' 2024-12-17T23:47:54.1294077Z Entering 'third_party/tensorpipe' 2024-12-17T23:47:54.1334941Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-17T23:47:54.1371479Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-17T23:47:54.1406956Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-17T23:47:54.1442930Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-17T23:47:54.1478995Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-17T23:47:54.1527698Z ##[endgroup] 2024-12-17T23:47:54.1554458Z [command]/usr/bin/git log -1 --format='%H' 2024-12-17T23:47:54.1572511Z '0cdf8b1d09254cfda66191d1bd01e3041c3c76f7' 2024-12-17T23:47:54.1736681Z Prepare all required actions 2024-12-17T23:47:54.1737303Z Getting action download info 2024-12-17T23:47:54.3310933Z ##[group]Run ./.github/actions/setup-linux 2024-12-17T23:47:54.3311273Z env: 2024-12-17T23:47:54.3311492Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:54.3311765Z ##[endgroup] 2024-12-17T23:47:54.3356027Z ##[group]Run set -euo pipefail 2024-12-17T23:47:54.3356396Z set -euo pipefail 2024-12-17T23:47:54.3356697Z function get_ec2_metadata() { 2024-12-17T23:47:54.3357094Z  # Pulled from instance metadata endpoint for EC2 2024-12-17T23:47:54.3357768Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2024-12-17T23:47:54.3358347Z  category=$1 2024-12-17T23:47:54.3358724Z  # If it is GCP runner (runner name contains gcp), do not run this 2024-12-17T23:47:54.3359170Z  runner_name_str=i-0897f70f52bdfd343 2024-12-17T23:47:54.3359576Z  if [[ -f /.inarc ]]; then 2024-12-17T23:47:54.3359939Z  echo "ARC Runner, no info on ec2 metadata" 2024-12-17T23:47:54.3360331Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2024-12-17T23:47:54.3360811Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2024-12-17T23:47:54.3361254Z  else 2024-12-17T23:47:54.3362132Z  curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2024-12-17T23:47:54.3363063Z  fi 2024-12-17T23:47:54.3363294Z } 2024-12-17T23:47:54.3363555Z echo "ami-id: $(get_ec2_metadata ami-id)" 2024-12-17T23:47:54.3363992Z echo "instance-id: $(get_ec2_metadata instance-id)" 2024-12-17T23:47:54.3364482Z echo "instance-type: $(get_ec2_metadata instance-type)" 2024-12-17T23:47:54.3364909Z echo "system info $(uname -a)" 2024-12-17T23:47:54.3372673Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:54.3373064Z env: 2024-12-17T23:47:54.3373283Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:54.3373562Z ##[endgroup] 2024-12-17T23:47:54.3504650Z ami-id: ami-0fff1b9a61dec8a5f 2024-12-17T23:47:54.3591533Z instance-id: i-0897f70f52bdfd343 2024-12-17T23:47:54.3680361Z instance-type: c5.2xlarge 2024-12-17T23:47:54.3689576Z system info Linux ip-10-0-46-137.ec2.internal 6.1.109-118.189.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Sep 10 08:59:12 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2024-12-17T23:47:54.3714483Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:47:54.3715440Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:47:54.3722367Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:54.3722789Z env: 2024-12-17T23:47:54.3723026Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:54.3723318Z ##[endgroup] 2024-12-17T23:47:54.3775249Z ##[group]Run if systemctl is-active --quiet docker; then 2024-12-17T23:47:54.3775711Z if systemctl is-active --quiet docker; then 2024-12-17T23:47:54.3776106Z  echo "Docker daemon is running..."; 2024-12-17T23:47:54.3776432Z else 2024-12-17T23:47:54.3776797Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2024-12-17T23:47:54.3777231Z fi 2024-12-17T23:47:54.3782353Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:54.3782877Z env: 2024-12-17T23:47:54.3783105Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:54.3783371Z ##[endgroup] 2024-12-17T23:47:54.3852027Z Docker daemon is running... 2024-12-17T23:47:54.3893558Z ##[group]Run nick-fields/retry@v3.0.0 2024-12-17T23:47:54.3893883Z with: 2024-12-17T23:47:54.3894101Z shell: bash 2024-12-17T23:47:54.3894476Z timeout_minutes: 5 2024-12-17T23:47:54.3894735Z max_attempts: 3 2024-12-17T23:47:54.3894989Z retry_wait_seconds: 30 2024-12-17T23:47:54.3897264Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" # For LF Runners we need to make sure we also login to Meta's ECR docker registry too. META_AWS_ACCOUNT_ID=308535385114 if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" fi 2024-12-17T23:47:54.3899902Z polling_interval_seconds: 1 2024-12-17T23:47:54.3900206Z warning_on_retry: true 2024-12-17T23:47:54.3900484Z continue_on_error: false 2024-12-17T23:47:54.3900742Z env: 2024-12-17T23:47:54.3900971Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:54.3901251Z AWS_RETRY_MODE: standard 2024-12-17T23:47:54.3901529Z AWS_MAX_ATTEMPTS: 5 2024-12-17T23:47:54.3901809Z AWS_DEFAULT_REGION: us-east-1 2024-12-17T23:47:54.3902089Z ##[endgroup] 2024-12-17T23:47:55.6467466Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:55.6468104Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:55.6468682Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:55.6469099Z 2024-12-17T23:47:55.6469233Z Login Succeeded 2024-12-17T23:47:56.5182572Z Command completed after 1 attempt(s). 2024-12-17T23:47:56.5244532Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:47:56.5245096Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:47:56.5245574Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2024-12-17T23:47:56.5252047Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:56.5252446Z env: 2024-12-17T23:47:56.5252693Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:56.5252976Z ##[endgroup] 2024-12-17T23:47:56.5333724Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-12-17T23:47:56.5334303Z # ignore expansion of "docker ps -q" since it could be empty 2024-12-17T23:47:56.5334739Z # shellcheck disable=SC2046 2024-12-17T23:47:56.5335073Z docker stop $(docker ps -q) || true 2024-12-17T23:47:56.5335429Z # Prune all of the docker images 2024-12-17T23:47:56.5335791Z docker system prune -af 2024-12-17T23:47:56.5341179Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:56.5341569Z env: 2024-12-17T23:47:56.5341791Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:56.5342052Z ##[endgroup] 2024-12-17T23:47:56.5578752Z "docker stop" requires at least 1 argument. 2024-12-17T23:47:56.5579147Z See 'docker stop --help'. 2024-12-17T23:47:56.5579328Z 2024-12-17T23:47:56.5579531Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2024-12-17T23:47:56.5579807Z 2024-12-17T23:47:56.5579922Z Stop one or more running containers 2024-12-17T23:47:56.5717568Z Total reclaimed space: 0B 2024-12-17T23:47:56.5753781Z ##[group]Run set +e 2024-12-17T23:47:56.5754081Z set +e 2024-12-17T23:47:56.5754332Z set -x 2024-12-17T23:47:56.5754570Z  2024-12-17T23:47:56.5754814Z PT_DOMAIN=download.pytorch.org 2024-12-17T23:47:56.5755412Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2024-12-17T23:47:56.5756414Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2024-12-17T23:47:56.5756951Z # one is returned at random 2024-12-17T23:47:56.5757356Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2024-12-17T23:47:56.5757746Z  2024-12-17T23:47:56.5758093Z if [ -z "${RESOLVED_IP}" ]; then 2024-12-17T23:47:56.5758544Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2024-12-17T23:47:56.5759078Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2024-12-17T23:47:56.5759486Z  2024-12-17T23:47:56.5759734Z  if [ -z "${RESOLVED_IP}" ]; then 2024-12-17T23:47:56.5760129Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2024-12-17T23:47:56.5760490Z  exit 1 2024-12-17T23:47:56.5760732Z  fi 2024-12-17T23:47:56.5760956Z fi 2024-12-17T23:47:56.5761175Z  2024-12-17T23:47:56.5761447Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2024-12-17T23:47:56.5761810Z  # Clean up any old records first 2024-12-17T23:47:56.5762185Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2024-12-17T23:47:56.5762523Z fi 2024-12-17T23:47:56.5762745Z  2024-12-17T23:47:56.5763072Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2024-12-17T23:47:56.5763467Z cat /etc/hosts 2024-12-17T23:47:56.5768968Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:56.5769353Z env: 2024-12-17T23:47:56.5769587Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:56.5769864Z ##[endgroup] 2024-12-17T23:47:56.5791004Z + PT_DOMAIN=download.pytorch.org 2024-12-17T23:47:56.5796150Z ++ dig -4 +short download.pytorch.org 2024-12-17T23:47:56.5796526Z ++ tail -n1 2024-12-17T23:47:56.5986891Z + RESOLVED_IP=3.167.99.125 2024-12-17T23:47:56.5987218Z + '[' -z 3.167.99.125 ']' 2024-12-17T23:47:56.5987529Z + grep -r download.pytorch.org /etc/hosts 2024-12-17T23:47:56.5997092Z 3.167.99.125 download.pytorch.org 2024-12-17T23:47:56.5998682Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2024-12-17T23:47:56.8184898Z + echo '3.167.99.125 download.pytorch.org' 2024-12-17T23:47:56.8185310Z + sudo tee -a /etc/hosts 2024-12-17T23:47:56.8578399Z 3.167.99.125 download.pytorch.org 2024-12-17T23:47:56.8592182Z + cat /etc/hosts 2024-12-17T23:47:56.8602065Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2024-12-17T23:47:56.8607922Z ::1 localhost6 localhost6.localdomain6 2024-12-17T23:47:56.8608332Z 3.167.99.125 download.pytorch.org 2024-12-17T23:47:56.8807055Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@release/2.6 2024-12-17T23:47:56.8807592Z with: 2024-12-17T23:47:56.8808421Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8809194Z docker-build-dir: .ci/docker 2024-12-17T23:47:56.8809504Z working-directory: . 2024-12-17T23:47:56.8809857Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:56.8810329Z force-push: false 2024-12-17T23:47:56.8810568Z env: 2024-12-17T23:47:56.8810815Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:56.8811098Z ##[endgroup] 2024-12-17T23:47:56.8840081Z ##[group]Run set -ex 2024-12-17T23:47:56.8840409Z set -ex 2024-12-17T23:47:56.8840653Z  2024-12-17T23:47:56.8841068Z # If the docker build directory or the build script doesn't exist, the action will 2024-12-17T23:47:56.8841801Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2024-12-17T23:47:56.8842384Z # job could then download the pre-built image as usual 2024-12-17T23:47:56.8842898Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2024-12-17T23:47:56.8843388Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8843999Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8844430Z  2024-12-17T23:47:56.8844803Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2024-12-17T23:47:56.8845250Z  exit 0 2024-12-17T23:47:56.8845499Z else 2024-12-17T23:47:56.8845787Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8846132Z fi 2024-12-17T23:47:56.8846357Z  2024-12-17T23:47:56.8846710Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2024-12-17T23:47:56.8847315Z  # The docker image name already includes the ECR prefix and tag, so we can just 2024-12-17T23:47:56.8847858Z  # use it as it is, but first let's extract the tag 2024-12-17T23:47:56.8848352Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2024-12-17T23:47:56.8848871Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8849374Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8849793Z else 2024-12-17T23:47:56.8850112Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2024-12-17T23:47:56.8850586Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8851238Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8851807Z fi 2024-12-17T23:47:56.8859194Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:56.8859595Z env: 2024-12-17T23:47:56.8859829Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:56.8860117Z REPO_NAME: pytorch 2024-12-17T23:47:56.8860824Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8861598Z DOCKER_BUILD_DIR: .ci/docker 2024-12-17T23:47:56.8861974Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:56.8862373Z ##[endgroup] 2024-12-17T23:47:56.8886265Z + [[ ! -d .ci/docker ]] 2024-12-17T23:47:56.8886572Z + [[ ! -f .ci/docker/build.sh ]] 2024-12-17T23:47:56.8886877Z + echo skip=false 2024-12-17T23:47:56.8888036Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]] 2024-12-17T23:47:56.8893705Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8894456Z ++ awk -F '[:,]' '{print $2}' 2024-12-17T23:47:56.8912324Z + DOCKER_TAG=45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8912829Z + echo docker-tag=45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8913681Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8946414Z ##[group]Run set +e 2024-12-17T23:47:56.8946756Z set +e 2024-12-17T23:47:56.8946999Z set -x 2024-12-17T23:47:56.8947237Z  2024-12-17T23:47:56.8947444Z login() { 2024-12-17T23:47:56.8947934Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-12-17T23:47:56.8948467Z } 2024-12-17T23:47:56.8948687Z  2024-12-17T23:47:56.8948909Z retry () { 2024-12-17T23:47:56.8949191Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-12-17T23:47:56.8949520Z } 2024-12-17T23:47:56.8949742Z  2024-12-17T23:47:56.8949987Z retry login "${DOCKER_REGISTRY}" 2024-12-17T23:47:56.8950306Z  2024-12-17T23:47:56.8950526Z START_TIME=$(date +%s) 2024-12-17T23:47:56.8950949Z # Wait up to 90 minutes 2024-12-17T23:47:56.8951329Z while [[ $(( $(date +%s) - 5400 )) -lt $START_TIME ]]; do 2024-12-17T23:47:56.8951840Z  # Check if image already exists, if it does then skip building it 2024-12-17T23:47:56.8952348Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2024-12-17T23:47:56.8952726Z  exit 0 2024-12-17T23:47:56.8952959Z  fi 2024-12-17T23:47:56.8953186Z  2024-12-17T23:47:56.8953578Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2024-12-17T23:47:56.8954252Z  # use this to differentiate between the Docker build and regular build jobs. For the 2024-12-17T23:47:56.8954921Z  # latter, it will wait for the Docker images to become available before continuing 2024-12-17T23:47:56.8955446Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2024-12-17T23:47:56.8955959Z  # It's a Docker build job, let's build the image 2024-12-17T23:47:56.8956326Z  break 2024-12-17T23:47:56.8956575Z  else 2024-12-17T23:47:56.8956930Z  # It's a regular build job, wait for the image to become available 2024-12-17T23:47:56.8957353Z  sleep 300 2024-12-17T23:47:56.8957603Z  fi 2024-12-17T23:47:56.8957829Z done 2024-12-17T23:47:56.8958052Z  2024-12-17T23:47:56.8958408Z # NB: This part requires a full checkout. Otherwise, the merge base will 2024-12-17T23:47:56.8958985Z # be empty. The default action would be to continue rebuild the image 2024-12-17T23:47:56.8959499Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2024-12-17T23:47:56.8959964Z  # if we're on the base branch then use the parent commit 2024-12-17T23:47:56.8960379Z  MERGE_BASE=$(git rev-parse HEAD~) 2024-12-17T23:47:56.8960707Z else 2024-12-17T23:47:56.8961045Z  # otherwise we're on a PR, so use the most recent base commit 2024-12-17T23:47:56.8961535Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2024-12-17T23:47:56.8961897Z fi 2024-12-17T23:47:56.8962121Z  2024-12-17T23:47:56.8962368Z if [[ -z "${MERGE_BASE}" ]]; then 2024-12-17T23:47:56.8962739Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8963083Z  2024-12-17T23:47:56.8963550Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2024-12-17T23:47:56.8964112Z  exit 0 2024-12-17T23:47:56.8964343Z fi 2024-12-17T23:47:56.8964676Z  2024-12-17T23:47:56.8965001Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2024-12-17T23:47:56.8965691Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2024-12-17T23:47:56.8966272Z  exit 1 2024-12-17T23:47:56.8966510Z fi 2024-12-17T23:47:56.8966741Z  2024-12-17T23:47:56.8967113Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2024-12-17T23:47:56.8967780Z # If no image exists but the hash is the same as the previous hash then we should error out here 2024-12-17T23:47:56.8968364Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2024-12-17T23:47:56.8969050Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2024-12-17T23:47:56.8969833Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2024-12-17T23:47:56.8970303Z fi 2024-12-17T23:47:56.8970531Z  2024-12-17T23:47:56.8970803Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2024-12-17T23:47:56.8976328Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:56.8976702Z env: 2024-12-17T23:47:56.8976931Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:56.8977309Z DOCKER_BUILD_DIR: .ci/docker 2024-12-17T23:47:56.8977671Z BASE_REVISION: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:47:56.8978478Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8979261Z DOCKER_TAG: 45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:56.8979697Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:56.8980097Z DOCKER_PUSH: 2024-12-17T23:47:56.8980333Z ##[endgroup] 2024-12-17T23:47:56.9001872Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:56.9004564Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:56.9005020Z + aws ecr get-login-password --region us-east-1 2024-12-17T23:47:56.9005577Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:57.4233122Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:57.4233851Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:57.4234586Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:57.4235131Z 2024-12-17T23:47:57.4235252Z Login Succeeded 2024-12-17T23:47:57.4248028Z ++ date +%s 2024-12-17T23:47:57.4256764Z + START_TIME=1734479277 2024-12-17T23:47:57.4259565Z ++ date +%s 2024-12-17T23:47:57.4267476Z + [[ 1734473877 -lt 1734479277 ]] 2024-12-17T23:47:57.4268319Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:57.6701930Z { 2024-12-17T23:47:57.6702221Z "schemaVersion": 2, 2024-12-17T23:47:57.6702693Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2024-12-17T23:47:57.6703155Z "config": { 2024-12-17T23:47:57.6703509Z "mediaType": "application/vnd.docker.container.image.v1+json", 2024-12-17T23:47:57.6703949Z "size": 41580, 2024-12-17T23:47:57.6704414Z "digest": "sha256:bca7896c0150bd62bb439228906ce331a02b4d3cd9ef140e4946794ede2ac12d" 2024-12-17T23:47:57.6705130Z }, 2024-12-17T23:47:57.6705443Z "layers": [ 2024-12-17T23:47:57.6705689Z { 2024-12-17T23:47:57.6706123Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6706843Z "size": 28583948, 2024-12-17T23:47:57.6707318Z "digest": "sha256:86e5016c269355b382c9cabab4f6646d56d75914f20d545289970436dae431b1" 2024-12-17T23:47:57.6708178Z }, 2024-12-17T23:47:57.6708521Z { 2024-12-17T23:47:57.6709245Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6709946Z "size": 1825, 2024-12-17T23:47:57.6710699Z "digest": "sha256:aec9e3dc3b214d1977be2640618dfe86f312acecc8ee5a35eac1e27caba083da" 2024-12-17T23:47:57.6711204Z }, 2024-12-17T23:47:57.6711410Z { 2024-12-17T23:47:57.6711850Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6712296Z "size": 313456486, 2024-12-17T23:47:57.6712745Z "digest": "sha256:c6543eb4cb51a83a4cf9224d08038ae94174bceee64de8b104164118d2c985a1" 2024-12-17T23:47:57.6713228Z }, 2024-12-17T23:47:57.6713432Z { 2024-12-17T23:47:57.6713753Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6714175Z "size": 864, 2024-12-17T23:47:57.6714597Z "digest": "sha256:71f87d3b18fa2a22e22b909afc30e4a8ba8181bc5089ed9f2e3f86d40d24bd4e" 2024-12-17T23:47:57.6715159Z }, 2024-12-17T23:47:57.6715524Z { 2024-12-17T23:47:57.6716121Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6716734Z "size": 79405092, 2024-12-17T23:47:57.6717255Z "digest": "sha256:c6ad7e035002e6ff80ebd83ce0bf3cb9db51f553cac3b74f1ea19486a19b6dbf" 2024-12-17T23:47:57.6717755Z }, 2024-12-17T23:47:57.6717962Z { 2024-12-17T23:47:57.6718307Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6718740Z "size": 704, 2024-12-17T23:47:57.6719314Z "digest": "sha256:28a6604dc7e22554839ce9c1c94b72a436d8caa1a96bf90c37adf00afa4ca0d4" 2024-12-17T23:47:57.6719802Z }, 2024-12-17T23:47:57.6720009Z { 2024-12-17T23:47:57.6720349Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6720774Z "size": 1260, 2024-12-17T23:47:57.6721171Z "digest": "sha256:9a955de2200893769521154542f692c4cf210566dbd5e5347075d3de812ca1c6" 2024-12-17T23:47:57.6721639Z }, 2024-12-17T23:47:57.6721844Z { 2024-12-17T23:47:57.6722179Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6722606Z "size": 484, 2024-12-17T23:47:57.6723018Z "digest": "sha256:c704eb5496d38fe253dab9a721fcda4b6241c9312b3e7d89158760b781451f23" 2024-12-17T23:47:57.6723501Z }, 2024-12-17T23:47:57.6723705Z { 2024-12-17T23:47:57.6724049Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6724477Z "size": 110, 2024-12-17T23:47:57.6724892Z "digest": "sha256:3e321eab5dfaf17692f88bdf4e0e6ec2d75cc319f474d9e564d1f29988fa23b4" 2024-12-17T23:47:57.6725389Z }, 2024-12-17T23:47:57.6725590Z { 2024-12-17T23:47:57.6725926Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6726352Z "size": 4152, 2024-12-17T23:47:57.6726756Z "digest": "sha256:df85228900459b006599c7caa7fe3cf6d07ecf17c81f45036ff33f47322c52af" 2024-12-17T23:47:57.6727237Z }, 2024-12-17T23:47:57.6727440Z { 2024-12-17T23:47:57.6727775Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6728202Z "size": 1860, 2024-12-17T23:47:57.6728622Z "digest": "sha256:6efa7ba6eaf91bc340fa98abb452844d2c53ee52dc1122056bd399b887ed75df" 2024-12-17T23:47:57.6729113Z }, 2024-12-17T23:47:57.6729313Z { 2024-12-17T23:47:57.6729651Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6730075Z "size": 701, 2024-12-17T23:47:57.6730483Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:47:57.6730966Z }, 2024-12-17T23:47:57.6731165Z { 2024-12-17T23:47:57.6731499Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6731925Z "size": 479, 2024-12-17T23:47:57.6732333Z "digest": "sha256:8dbdd9e962a97ea1ee0dd73599b1a4f82c377975d1138b2cf2c5e83702e21bbd" 2024-12-17T23:47:57.6732819Z }, 2024-12-17T23:47:57.6733020Z { 2024-12-17T23:47:57.6733356Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6733787Z "size": 2884231269, 2024-12-17T23:47:57.6734317Z "digest": "sha256:45260f60bb0a474a8aab43cf3eaf58799b9c24a3c40cbd7df1addfe8ff59b4e3" 2024-12-17T23:47:57.6734814Z }, 2024-12-17T23:47:57.6735020Z { 2024-12-17T23:47:57.6735358Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6735787Z "size": 32, 2024-12-17T23:47:57.6736199Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6736694Z }, 2024-12-17T23:47:57.6736903Z { 2024-12-17T23:47:57.6737242Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6737655Z "size": 380, 2024-12-17T23:47:57.6738084Z "digest": "sha256:3668f833b0e44ab22face07bfe5a41e1f6cebdcc76f350109554da996700705e" 2024-12-17T23:47:57.6738574Z }, 2024-12-17T23:47:57.6738779Z { 2024-12-17T23:47:57.6739114Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6739528Z "size": 104, 2024-12-17T23:47:57.6739941Z "digest": "sha256:aa4035866598715c0fa2465896f8c76097e89d62beccf7428a170ab0e7b971e4" 2024-12-17T23:47:57.6740420Z }, 2024-12-17T23:47:57.6740623Z { 2024-12-17T23:47:57.6740965Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6741382Z "size": 232, 2024-12-17T23:47:57.6741796Z "digest": "sha256:2d39f659a403494ec291c731068f0e18494d434b36b999298389952753a4527c" 2024-12-17T23:47:57.6742265Z }, 2024-12-17T23:47:57.6742470Z { 2024-12-17T23:47:57.6742872Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6743290Z "size": 3206156, 2024-12-17T23:47:57.6743728Z "digest": "sha256:cb7d736bf0d86d52f80ee3d9288b94d5ff95c75d0304ace58fe761045386be72" 2024-12-17T23:47:57.6744218Z }, 2024-12-17T23:47:57.6744431Z { 2024-12-17T23:47:57.6744769Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6745187Z "size": 2004, 2024-12-17T23:47:57.6745609Z "digest": "sha256:3cc97a2522410c161a299d34bfc9844f98b3e13b20d353fd607f95384e5b9702" 2024-12-17T23:47:57.6746090Z }, 2024-12-17T23:47:57.6746299Z { 2024-12-17T23:47:57.6746642Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6747058Z "size": 105, 2024-12-17T23:47:57.6747476Z "digest": "sha256:d901eaf7f5a011d42125a95da686dd3734506394e03bc7e681f7309175fa98a0" 2024-12-17T23:47:57.6747955Z }, 2024-12-17T23:47:57.6748156Z { 2024-12-17T23:47:57.6748491Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6748909Z "size": 565, 2024-12-17T23:47:57.6749330Z "digest": "sha256:c8b4328c0ae1f401ca0bab96e2b6680c4240e097a1e5e578fbaa1e868a5c84f4" 2024-12-17T23:47:57.6749816Z }, 2024-12-17T23:47:57.6750015Z { 2024-12-17T23:47:57.6750350Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6750763Z "size": 32, 2024-12-17T23:47:57.6751189Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6751680Z }, 2024-12-17T23:47:57.6751885Z { 2024-12-17T23:47:57.6752225Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6752638Z "size": 104, 2024-12-17T23:47:57.6753054Z "digest": "sha256:c3c70a336d7dbd25106117d3a4674375182adc809096f6d1d955b5aa9d787bd7" 2024-12-17T23:47:57.6753531Z }, 2024-12-17T23:47:57.6753731Z { 2024-12-17T23:47:57.6754068Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6754489Z "size": 504, 2024-12-17T23:47:57.6754906Z "digest": "sha256:0e26764e29494f03ea560afcd508ae0a77c2d396f4172d32a034883af1383c56" 2024-12-17T23:47:57.6755385Z }, 2024-12-17T23:47:57.6755584Z { 2024-12-17T23:47:57.6756036Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6756458Z "size": 121478573, 2024-12-17T23:47:57.6756888Z "digest": "sha256:12920fcbf355503b40ba103c14743cb337be4452a06d4f504800a5043603e64f" 2024-12-17T23:47:57.6757365Z }, 2024-12-17T23:47:57.6757571Z { 2024-12-17T23:47:57.6758019Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6758440Z "size": 109, 2024-12-17T23:47:57.6758875Z "digest": "sha256:3b8143344aeebacaef8c7f4946c207067bc27abae35f07b00957ea7ff1ec1ed0" 2024-12-17T23:47:57.6759367Z }, 2024-12-17T23:47:57.6759579Z { 2024-12-17T23:47:57.6759917Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6760334Z "size": 489, 2024-12-17T23:47:57.6760754Z "digest": "sha256:ad803458908bd8937af63f1a05be363cd5783ad5342330e514dee02c48e2bd90" 2024-12-17T23:47:57.6761233Z }, 2024-12-17T23:47:57.6761438Z { 2024-12-17T23:47:57.6761773Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6762181Z "size": 383, 2024-12-17T23:47:57.6762612Z "digest": "sha256:d4e3b9c27d5376de1dc3e2716d85aa0b2be2bb86e8195ebec0bd646d83f6a9d7" 2024-12-17T23:47:57.6763104Z }, 2024-12-17T23:47:57.6763307Z { 2024-12-17T23:47:57.6763641Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6764061Z "size": 103, 2024-12-17T23:47:57.6764489Z "digest": "sha256:0704e0edafd463c5cd2f9fdd5be7fe7a77557c9a418f85a91aa9f20a13013528" 2024-12-17T23:47:57.6764981Z }, 2024-12-17T23:47:57.6765181Z { 2024-12-17T23:47:57.6765518Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6765930Z "size": 1474, 2024-12-17T23:47:57.6766366Z "digest": "sha256:eff65b2f6bc4ad750c8acaecf94a25be481eb749fbc5886b0fba6359599f96e5" 2024-12-17T23:47:57.6766939Z }, 2024-12-17T23:47:57.6767143Z { 2024-12-17T23:47:57.6767476Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6767890Z "size": 430322088, 2024-12-17T23:47:57.6768327Z "digest": "sha256:c3d37a289144f6c3e5c40a496b8c8773922b236cc2705f3d93189e73fe1f2579" 2024-12-17T23:47:57.6768801Z }, 2024-12-17T23:47:57.6769005Z { 2024-12-17T23:47:57.6769344Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6769757Z "size": 163, 2024-12-17T23:47:57.6770195Z "digest": "sha256:8a082c3c11caa6253ddf5658cf92d58dc60fd2ebe7d9f44cbcd3a516afbd8bc9" 2024-12-17T23:47:57.6770689Z }, 2024-12-17T23:47:57.6770891Z { 2024-12-17T23:47:57.6771232Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6771650Z "size": 423, 2024-12-17T23:47:57.6772078Z "digest": "sha256:5f373dd245eeeda941e744bd6108b2c60cd9507d441c1f8fbff89bc88a07f35e" 2024-12-17T23:47:57.6772574Z }, 2024-12-17T23:47:57.6772774Z { 2024-12-17T23:47:57.6773109Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6773524Z "size": 32, 2024-12-17T23:47:57.6773943Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6774432Z }, 2024-12-17T23:47:57.6774640Z { 2024-12-17T23:47:57.6774978Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6775394Z "size": 111, 2024-12-17T23:47:57.6775828Z "digest": "sha256:f81fdfe7558995633cbd3d80ef1b4d45b59c5c718f4f04bfaf9d3c779c802611" 2024-12-17T23:47:57.6776318Z }, 2024-12-17T23:47:57.6776523Z { 2024-12-17T23:47:57.6776852Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6777281Z "size": 475, 2024-12-17T23:47:57.6777706Z "digest": "sha256:57a000ae2392a0a0739eb6a99916025c3b83fa76bf6cd915cfe7135c9adba6ca" 2024-12-17T23:47:57.6778195Z }, 2024-12-17T23:47:57.6778401Z { 2024-12-17T23:47:57.6778732Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6779169Z "size": 32, 2024-12-17T23:47:57.6779595Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6780089Z }, 2024-12-17T23:47:57.6780289Z { 2024-12-17T23:47:57.6780612Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6781039Z "size": 112, 2024-12-17T23:47:57.6781464Z "digest": "sha256:0bcf8b6039ec534ff78480f05b3e2f8a7b6aa6783f11fea422d974e1fa88a2d8" 2024-12-17T23:47:57.6782020Z }, 2024-12-17T23:47:57.6782226Z { 2024-12-17T23:47:57.6782547Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6782974Z "size": 565, 2024-12-17T23:47:57.6783413Z "digest": "sha256:29eb39eca539bcd394e7fc15e37cedddd4e50e1d77be08dcfbcdd29cfae070f7" 2024-12-17T23:47:57.6783909Z }, 2024-12-17T23:47:57.6784113Z { 2024-12-17T23:47:57.6784437Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6784861Z "size": 43148719, 2024-12-17T23:47:57.6785300Z "digest": "sha256:a4e958f84c25eac68ae21f89137bb98510da40e36acb563a7dc7c3cb00ec4e2b" 2024-12-17T23:47:57.6785784Z }, 2024-12-17T23:47:57.6785984Z { 2024-12-17T23:47:57.6786309Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6786732Z "size": 106, 2024-12-17T23:47:57.6787142Z "digest": "sha256:ea68936d1a73d75964d0d386d66a6b8292c6e0126239916ccf527bd5f4b819b3" 2024-12-17T23:47:57.6787620Z }, 2024-12-17T23:47:57.6787826Z { 2024-12-17T23:47:57.6788150Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6788575Z "size": 294, 2024-12-17T23:47:57.6789009Z "digest": "sha256:7745f672cbb176f001f0ceb1b4cbec37d8da7d6d541ec801eecba9cca07b1f94" 2024-12-17T23:47:57.6789501Z }, 2024-12-17T23:47:57.6789703Z { 2024-12-17T23:47:57.6790091Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6790514Z "size": 32, 2024-12-17T23:47:57.6790942Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6791429Z }, 2024-12-17T23:47:57.6791637Z { 2024-12-17T23:47:57.6791964Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6792392Z "size": 106, 2024-12-17T23:47:57.6792806Z "digest": "sha256:a69e77d01a8c60037687973eae90a8e127f34857edcc59e407d89e4880c9393c" 2024-12-17T23:47:57.6793282Z }, 2024-12-17T23:47:57.6793484Z { 2024-12-17T23:47:57.6793814Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6794240Z "size": 425, 2024-12-17T23:47:57.6794656Z "digest": "sha256:c478133b8a8bb86f7b39cf7be895897267a185b59ef63b6957e0b62db4b1d68e" 2024-12-17T23:47:57.6795134Z }, 2024-12-17T23:47:57.6795333Z { 2024-12-17T23:47:57.6795742Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6796183Z "size": 20262182, 2024-12-17T23:47:57.6796623Z "digest": "sha256:c56df19c4d1f0a76368754d7883cabcf4379af7d9aca46c3fc2d9ca246ba042e" 2024-12-17T23:47:57.6797111Z }, 2024-12-17T23:47:57.6797318Z { 2024-12-17T23:47:57.6797640Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6798256Z "size": 108, 2024-12-17T23:47:57.6798675Z "digest": "sha256:c6619167a3e6d46f5274c3764310b212a7ca386d6bd4c9d3b543e20d3a238711" 2024-12-17T23:47:57.6799187Z }, 2024-12-17T23:47:57.6799392Z { 2024-12-17T23:47:57.6799720Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6800145Z "size": 643, 2024-12-17T23:47:57.6800579Z "digest": "sha256:7dbfc0fcaa06ef74ed42ffabd0410775a91a564afe4e18083cce201ba90011e1" 2024-12-17T23:47:57.6801073Z }, 2024-12-17T23:47:57.6801271Z { 2024-12-17T23:47:57.6801593Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6802020Z "size": 701, 2024-12-17T23:47:57.6802435Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:47:57.6802914Z }, 2024-12-17T23:47:57.6803117Z { 2024-12-17T23:47:57.6803441Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6803866Z "size": 142, 2024-12-17T23:47:57.6804276Z "digest": "sha256:88d2b1d09ff6d9556808d28a348937758e52680f9ec47b71fe16953c028f5d32" 2024-12-17T23:47:57.6804755Z }, 2024-12-17T23:47:57.6804963Z { 2024-12-17T23:47:57.6805289Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6805860Z "size": 136, 2024-12-17T23:47:57.6806282Z "digest": "sha256:74215dfb05b9e9c35284a651cccd5b7bdd91355023b6389a0c525cc95ebd7147" 2024-12-17T23:47:57.6806763Z }, 2024-12-17T23:47:57.6806967Z { 2024-12-17T23:47:57.6807291Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6807721Z "size": 32, 2024-12-17T23:47:57.6808154Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6808641Z }, 2024-12-17T23:47:57.6808843Z { 2024-12-17T23:47:57.6809172Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6809598Z "size": 196, 2024-12-17T23:47:57.6810023Z "digest": "sha256:e2f8bba90803b9c21aef0f388b17f37f37c4a89e16c53524542677ccc27263b8" 2024-12-17T23:47:57.6810512Z }, 2024-12-17T23:47:57.6810716Z { 2024-12-17T23:47:57.6811041Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6811473Z "size": 1402, 2024-12-17T23:47:57.6811896Z "digest": "sha256:56e66f0db375d2732197e4af125fc85ab84051c31e857c14386da9c36b0ce3c1" 2024-12-17T23:47:57.6812378Z }, 2024-12-17T23:47:57.6812581Z { 2024-12-17T23:47:57.6812910Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6813341Z "size": 701, 2024-12-17T23:47:57.6813759Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:47:57.6814327Z }, 2024-12-17T23:47:57.6814533Z { 2024-12-17T23:47:57.6814859Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6815285Z "size": 137, 2024-12-17T23:47:57.6815709Z "digest": "sha256:5e47d63035823c88b5b5052e0ffcdef30c0d9880b95acd7621c60875f583b1ab" 2024-12-17T23:47:57.6816189Z }, 2024-12-17T23:47:57.6816390Z { 2024-12-17T23:47:57.6816715Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6817139Z "size": 32, 2024-12-17T23:47:57.6817567Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6818051Z }, 2024-12-17T23:47:57.6818254Z { 2024-12-17T23:47:57.6818581Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6819009Z "size": 157, 2024-12-17T23:47:57.6819437Z "digest": "sha256:f14b36f011bf58756d6d89ae5ffa0a44ac587040dfecb8a6fa376dd559f7ee84" 2024-12-17T23:47:57.6819933Z }, 2024-12-17T23:47:57.6820136Z { 2024-12-17T23:47:57.6820463Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6820897Z "size": 1402, 2024-12-17T23:47:57.6821318Z "digest": "sha256:56e66f0db375d2732197e4af125fc85ab84051c31e857c14386da9c36b0ce3c1" 2024-12-17T23:47:57.6821803Z }, 2024-12-17T23:47:57.6822014Z { 2024-12-17T23:47:57.6822338Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6822768Z "size": 701, 2024-12-17T23:47:57.6823196Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:47:57.6823678Z }, 2024-12-17T23:47:57.6823869Z { 2024-12-17T23:47:57.6824211Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6824640Z "size": 140, 2024-12-17T23:47:57.6825058Z "digest": "sha256:f40f5582e6620a7dbd0349c6e926c8ba046380f1c2ddf8c4c0499356c9b06bae" 2024-12-17T23:47:57.6825537Z }, 2024-12-17T23:47:57.6825734Z { 2024-12-17T23:47:57.6826072Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6826501Z "size": 32, 2024-12-17T23:47:57.6826923Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6827413Z }, 2024-12-17T23:47:57.6827602Z { 2024-12-17T23:47:57.6827940Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6828367Z "size": 161, 2024-12-17T23:47:57.6828779Z "digest": "sha256:4699807ef69f0d61ab68f825da259c68cb093b18af932839fc36c93e00203139" 2024-12-17T23:47:57.6829253Z }, 2024-12-17T23:47:57.6829521Z { 2024-12-17T23:47:57.6829865Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6830294Z "size": 682, 2024-12-17T23:47:57.6830715Z "digest": "sha256:633926ef19787d7d6a27cb1f248dd3e8601a0d4e77a7b5a88b7173cc958e46f3" 2024-12-17T23:47:57.6831193Z }, 2024-12-17T23:47:57.6831382Z { 2024-12-17T23:47:57.6831721Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6832147Z "size": 701, 2024-12-17T23:47:57.6832562Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:47:57.6833041Z }, 2024-12-17T23:47:57.6833228Z { 2024-12-17T23:47:57.6833568Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6833997Z "size": 138, 2024-12-17T23:47:57.6834401Z "digest": "sha256:4dd1e4338891022059638c6ea550e879ba0f49e323740b7f4566567de12c90ba" 2024-12-17T23:47:57.6834870Z }, 2024-12-17T23:47:57.6835056Z { 2024-12-17T23:47:57.6835391Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6835927Z "size": 32, 2024-12-17T23:47:57.6836351Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6836838Z }, 2024-12-17T23:47:57.6837026Z { 2024-12-17T23:47:57.6837360Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6837873Z "size": 159, 2024-12-17T23:47:57.6838290Z "digest": "sha256:fafe335b683238800f721cab6383022f05d8214d09a8da384f393faa5a47a065" 2024-12-17T23:47:57.6838769Z }, 2024-12-17T23:47:57.6838960Z { 2024-12-17T23:47:57.6839298Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6839728Z "size": 907, 2024-12-17T23:47:57.6840159Z "digest": "sha256:7b96d6fc18dbf9a8cd85ae285775fe5cb88151abd34a1ec34d55963c185836a8" 2024-12-17T23:47:57.6840650Z }, 2024-12-17T23:47:57.6840839Z { 2024-12-17T23:47:57.6841184Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6841608Z "size": 701, 2024-12-17T23:47:57.6842024Z "digest": "sha256:f29844292cf79d6bfd4f4c33d2a2335653d6055daf0f4ec0194029b18aedfba9" 2024-12-17T23:47:57.6842506Z }, 2024-12-17T23:47:57.6842694Z { 2024-12-17T23:47:57.6843027Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6843639Z "size": 135, 2024-12-17T23:47:57.6844141Z "digest": "sha256:5eb2aa388cbde039dc7f3a6a21bbf5862212c3f99e3188382acce5c4d1c7f574" 2024-12-17T23:47:57.6844735Z }, 2024-12-17T23:47:57.6845054Z { 2024-12-17T23:47:57.6845500Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6846026Z "size": 32, 2024-12-17T23:47:57.6846506Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6847101Z }, 2024-12-17T23:47:57.6847405Z { 2024-12-17T23:47:57.6847860Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6848399Z "size": 159, 2024-12-17T23:47:57.6848864Z "digest": "sha256:6647884d3ec59ea0f46ec545856797837a5fe92da31d679138d5d8a356cf9990" 2024-12-17T23:47:57.6849456Z }, 2024-12-17T23:47:57.6849759Z { 2024-12-17T23:47:57.6850175Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6850737Z "size": 1519, 2024-12-17T23:47:57.6851226Z "digest": "sha256:fb0d52d47e5d449f8b26c0e67df88e5eafd8275912b9d61a47d100fcee2b364c" 2024-12-17T23:47:57.6851789Z }, 2024-12-17T23:47:57.6852126Z { 2024-12-17T23:47:57.6852567Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6853101Z "size": 32, 2024-12-17T23:47:57.6853599Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6854187Z }, 2024-12-17T23:47:57.6854503Z { 2024-12-17T23:47:57.6854958Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6855483Z "size": 137, 2024-12-17T23:47:57.6856111Z "digest": "sha256:2e082b79862beb2f9fbaf83954fb4c9dd0c4fe7d53c33f3b5275d549599aad8c" 2024-12-17T23:47:57.6856648Z }, 2024-12-17T23:47:57.6856947Z { 2024-12-17T23:47:57.6857411Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6857933Z "size": 379, 2024-12-17T23:47:57.6858445Z "digest": "sha256:a649e2ecceb3b5dfa0ef623faea2c287bf95eb5dc3757ba847ced37aa97f7cf7" 2024-12-17T23:47:57.6859052Z }, 2024-12-17T23:47:57.6859349Z { 2024-12-17T23:47:57.6859772Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6860330Z "size": 32, 2024-12-17T23:47:57.6860848Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6861376Z }, 2024-12-17T23:47:57.6861715Z { 2024-12-17T23:47:57.6862144Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6862678Z "size": 104, 2024-12-17T23:47:57.6863228Z "digest": "sha256:0f48fbbac9ba4b9e5a957f8bcd8ae7b540b18cd34e64cdaf5fdc949c27909169" 2024-12-17T23:47:57.6863785Z }, 2024-12-17T23:47:57.6864088Z { 2024-12-17T23:47:57.6864535Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6865067Z "size": 1932, 2024-12-17T23:47:57.6865619Z "digest": "sha256:d0ca2167ad3f44d47ddf0c04913584aa7168e753b77ed9f0b9140d55764099d3" 2024-12-17T23:47:57.6866271Z }, 2024-12-17T23:47:57.6866525Z { 2024-12-17T23:47:57.6866988Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6867519Z "size": 234174408, 2024-12-17T23:47:57.6868032Z "digest": "sha256:a7966f3017643e03d22935c9ebc74f40f62db1ee412e68aa7c274fb2cee73745" 2024-12-17T23:47:57.6868659Z }, 2024-12-17T23:47:57.6868908Z { 2024-12-17T23:47:57.6869319Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6869899Z "size": 106, 2024-12-17T23:47:57.6870404Z "digest": "sha256:5a7317de868d498389a798f3d564217b316747791255113df27e652c22ecbb23" 2024-12-17T23:47:57.6870973Z }, 2024-12-17T23:47:57.6871251Z { 2024-12-17T23:47:57.6871678Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6872208Z "size": 165, 2024-12-17T23:47:57.6872747Z "digest": "sha256:fe1994ffc6714ff248de0f7f85378ea8c5b9132f3be7d3fd9c7531a61738e48f" 2024-12-17T23:47:57.6873332Z }, 2024-12-17T23:47:57.6873613Z { 2024-12-17T23:47:57.6874070Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6874592Z "size": 7943, 2024-12-17T23:47:57.6875133Z "digest": "sha256:c5d0d5389f3b75505f972b820e171b25fa7277a75f3386e745e16fa04467dc6b" 2024-12-17T23:47:57.6875810Z }, 2024-12-17T23:47:57.6876074Z { 2024-12-17T23:47:57.6876547Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6877076Z "size": 8070, 2024-12-17T23:47:57.6877578Z "digest": "sha256:642c500944e63596bfc4e43fd9f7aea7b12098519eb254472a00f7fb8e6de3d7" 2024-12-17T23:47:57.6878196Z }, 2024-12-17T23:47:57.6878498Z { 2024-12-17T23:47:57.6878877Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6879445Z "size": 304, 2024-12-17T23:47:57.6879962Z "digest": "sha256:990f6a099a4741eec3ed17414d2fb550f632f9fbb4c361be5729f35b0e132be2" 2024-12-17T23:47:57.6880562Z }, 2024-12-17T23:47:57.6880873Z { 2024-12-17T23:47:57.6881273Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6881801Z "size": 32, 2024-12-17T23:47:57.6882334Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6882925Z }, 2024-12-17T23:47:57.6883242Z { 2024-12-17T23:47:57.6883627Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6884157Z "size": 108, 2024-12-17T23:47:57.6897126Z "digest": "sha256:c5d5ea84cde4e2d1bd30fe4191995c8c0fd57093728055818d2fd1960aa8c608" 2024-12-17T23:47:57.6897648Z }, 2024-12-17T23:47:57.6897859Z { 2024-12-17T23:47:57.6898602Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6899048Z "size": 54145664, 2024-12-17T23:47:57.6899483Z "digest": "sha256:32258a594a306cdb6b4d7164dd7fa649727be4bc32b05edab4b1b19a9e84aeae" 2024-12-17T23:47:57.6899973Z }, 2024-12-17T23:47:57.6900182Z { 2024-12-17T23:47:57.6900528Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2024-12-17T23:47:57.6900966Z "size": 32, 2024-12-17T23:47:57.6901382Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2024-12-17T23:47:57.6901869Z } 2024-12-17T23:47:57.6902070Z ] 2024-12-17T23:47:57.6902268Z } 2024-12-17T23:47:57.6902480Z + exit 0 2024-12-17T23:47:57.6938827Z ##[group]Run set -eux 2024-12-17T23:47:57.6939129Z set -eux 2024-12-17T23:47:57.6940023Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin 2024-12-17T23:47:57.6946394Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:57.6946787Z env: 2024-12-17T23:47:57.6947002Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:57.6947279Z ##[endgroup] 2024-12-17T23:47:57.6972927Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2024-12-17T23:47:57.6973971Z + jq --raw-output .SecretString 2024-12-17T23:47:57.6974856Z + jq -r .docker_hub_readonly_token 2024-12-17T23:47:57.6976115Z + docker login --username pytorchbot --password-stdin 2024-12-17T23:47:58.3231927Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:58.3232664Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:58.3233693Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:58.3234120Z 2024-12-17T23:47:58.3234229Z Login Succeeded 2024-12-17T23:47:58.3319826Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2024-12-17T23:47:58.3320215Z tag=${ECR_DOCKER_IMAGE##*/} 2024-12-17T23:47:58.3320638Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2024-12-17T23:47:58.3326587Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:58.3326985Z env: 2024-12-17T23:47:58.3327223Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:58.3327948Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:58.3328710Z ##[endgroup] 2024-12-17T23:47:58.3353438Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.13-clang10-45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:58.3404636Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@release/2.6 2024-12-17T23:47:58.3405123Z with: 2024-12-17T23:47:58.3405786Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:58.3406618Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:58.3407021Z env: 2024-12-17T23:47:58.3407247Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:58.3407529Z ##[endgroup] 2024-12-17T23:47:58.3433141Z ##[group]Run set -x 2024-12-17T23:47:58.3433421Z set -x 2024-12-17T23:47:58.3433668Z set +e 2024-12-17T23:47:58.3433908Z  2024-12-17T23:47:58.3434132Z login() { 2024-12-17T23:47:58.3434824Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2024-12-17T23:47:58.3435368Z } 2024-12-17T23:47:58.3435589Z  2024-12-17T23:47:58.3435922Z retry () { 2024-12-17T23:47:58.3436214Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2024-12-17T23:47:58.3436537Z } 2024-12-17T23:47:58.3436766Z  2024-12-17T23:47:58.3437017Z retry login "${DOCKER_REGISTRY}" 2024-12-17T23:47:58.3437347Z  2024-12-17T23:47:58.3437574Z set -e 2024-12-17T23:47:58.3438037Z # ignore output since only exit code is used for conditional 2024-12-17T23:47:58.3438552Z # only pull docker image if it's not available locally 2024-12-17T23:47:58.3439121Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2024-12-17T23:47:58.3439647Z  retry docker pull "${DOCKER_IMAGE}" 2024-12-17T23:47:58.3440010Z fi 2024-12-17T23:47:58.3445382Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:47:58.3445780Z env: 2024-12-17T23:47:58.3446013Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:47:58.3446752Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:58.3447592Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:58.3448001Z ##[endgroup] 2024-12-17T23:47:58.3469697Z + set +e 2024-12-17T23:47:58.3470240Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:58.3470938Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:58.3473285Z + aws ecr get-login-password --region us-east-1 2024-12-17T23:47:58.3474194Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2024-12-17T23:47:58.8917916Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2024-12-17T23:47:58.8919200Z Configure a credential helper to remove this warning. See 2024-12-17T23:47:58.8919932Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2024-12-17T23:47:58.8920333Z 2024-12-17T23:47:58.8920473Z Login Succeeded 2024-12-17T23:47:58.8929821Z + set -e 2024-12-17T23:47:58.8930965Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:58.9052480Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:58.9053751Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:47:59.1923533Z 45e1356b47a284893081276eff3000b7b534f3b1: Pulling from pytorch/pytorch-linux-focal-py3.13-clang10 2024-12-17T23:47:59.1934581Z 86e5016c2693: Pulling fs layer 2024-12-17T23:47:59.1935273Z aec9e3dc3b21: Pulling fs layer 2024-12-17T23:47:59.1935843Z c6543eb4cb51: Pulling fs layer 2024-12-17T23:47:59.1938266Z 71f87d3b18fa: Pulling fs layer 2024-12-17T23:47:59.1938750Z c6ad7e035002: Pulling fs layer 2024-12-17T23:47:59.1939568Z 28a6604dc7e2: Pulling fs layer 2024-12-17T23:47:59.1940065Z 9a955de22008: Pulling fs layer 2024-12-17T23:47:59.1940565Z c704eb5496d3: Pulling fs layer 2024-12-17T23:47:59.1941010Z 3e321eab5dfa: Pulling fs layer 2024-12-17T23:47:59.1941498Z df8522890045: Pulling fs layer 2024-12-17T23:47:59.1942033Z 6efa7ba6eaf9: Pulling fs layer 2024-12-17T23:47:59.1942491Z f29844292cf7: Pulling fs layer 2024-12-17T23:47:59.1942983Z c6ad7e035002: Waiting 2024-12-17T23:47:59.1943437Z 8dbdd9e962a9: Pulling fs layer 2024-12-17T23:47:59.1943923Z 28a6604dc7e2: Waiting 2024-12-17T23:47:59.1944367Z 45260f60bb0a: Pulling fs layer 2024-12-17T23:47:59.1944844Z 9a955de22008: Waiting 2024-12-17T23:47:59.1945321Z 4f4fb700ef54: Pulling fs layer 2024-12-17T23:47:59.1945846Z 3668f833b0e4: Pulling fs layer 2024-12-17T23:47:59.1946354Z aa4035866598: Pulling fs layer 2024-12-17T23:47:59.1946832Z df8522890045: Waiting 2024-12-17T23:47:59.1947268Z c704eb5496d3: Waiting 2024-12-17T23:47:59.1947565Z 2d39f659a403: Pulling fs layer 2024-12-17T23:47:59.1947863Z 6efa7ba6eaf9: Waiting 2024-12-17T23:47:59.1948134Z cb7d736bf0d8: Pulling fs layer 2024-12-17T23:47:59.1948454Z 3e321eab5dfa: Waiting 2024-12-17T23:47:59.1948710Z f29844292cf7: Waiting 2024-12-17T23:47:59.1948965Z 4f4fb700ef54: Waiting 2024-12-17T23:47:59.1949299Z 3668f833b0e4: Waiting 2024-12-17T23:47:59.1949548Z 8dbdd9e962a9: Waiting 2024-12-17T23:47:59.1949822Z 3cc97a252241: Pulling fs layer 2024-12-17T23:47:59.1950157Z d901eaf7f5a0: Pulling fs layer 2024-12-17T23:47:59.1950444Z 2d39f659a403: Waiting 2024-12-17T23:47:59.1950751Z c8b4328c0ae1: Pulling fs layer 2024-12-17T23:47:59.1951037Z cb7d736bf0d8: Waiting 2024-12-17T23:47:59.1951374Z c3c70a336d7d: Pulling fs layer 2024-12-17T23:47:59.1951669Z aa4035866598: Waiting 2024-12-17T23:47:59.1951922Z 71f87d3b18fa: Waiting 2024-12-17T23:47:59.1952212Z 0e26764e2949: Pulling fs layer 2024-12-17T23:47:59.1952485Z 3cc97a252241: Waiting 2024-12-17T23:47:59.1952746Z 12920fcbf355: Pulling fs layer 2024-12-17T23:47:59.1953077Z d901eaf7f5a0: Waiting 2024-12-17T23:47:59.1953337Z c8b4328c0ae1: Waiting 2024-12-17T23:47:59.1953624Z c3c70a336d7d: Waiting 2024-12-17T23:47:59.1953876Z 3b8143344aee: Pulling fs layer 2024-12-17T23:47:59.1954162Z 0e26764e2949: Waiting 2024-12-17T23:47:59.1954416Z 12920fcbf355: Waiting 2024-12-17T23:47:59.1954679Z ad803458908b: Pulling fs layer 2024-12-17T23:47:59.1954969Z 45260f60bb0a: Waiting 2024-12-17T23:47:59.1955256Z d4e3b9c27d53: Pulling fs layer 2024-12-17T23:47:59.1955556Z 0704e0edafd4: Pulling fs layer 2024-12-17T23:47:59.1955944Z ad803458908b: Waiting 2024-12-17T23:47:59.1956212Z eff65b2f6bc4: Pulling fs layer 2024-12-17T23:47:59.1956555Z d4e3b9c27d53: Waiting 2024-12-17T23:47:59.1956898Z c3d37a289144: Pulling fs layer 2024-12-17T23:47:59.1957366Z 8a082c3c11ca: Pulling fs layer 2024-12-17T23:47:59.1957707Z 5f373dd245ee: Pulling fs layer 2024-12-17T23:47:59.1958001Z 0704e0edafd4: Waiting 2024-12-17T23:47:59.1958248Z eff65b2f6bc4: Waiting 2024-12-17T23:47:59.1958517Z f81fdfe75589: Pulling fs layer 2024-12-17T23:47:59.1958818Z 57a000ae2392: Pulling fs layer 2024-12-17T23:47:59.1959152Z 5f373dd245ee: Waiting 2024-12-17T23:47:59.1959513Z 0bcf8b6039ec: Pulling fs layer 2024-12-17T23:47:59.1959799Z 29eb39eca539: Pulling fs layer 2024-12-17T23:47:59.1960093Z a4e958f84c25: Pulling fs layer 2024-12-17T23:47:59.1960385Z ea68936d1a73: Pulling fs layer 2024-12-17T23:47:59.1960696Z 8a082c3c11ca: Waiting 2024-12-17T23:47:59.1960962Z 7745f672cbb1: Pulling fs layer 2024-12-17T23:47:59.1961239Z f81fdfe75589: Waiting 2024-12-17T23:47:59.1961493Z 0bcf8b6039ec: Waiting 2024-12-17T23:47:59.1961762Z a69e77d01a8c: Pulling fs layer 2024-12-17T23:47:59.1962045Z 29eb39eca539: Waiting 2024-12-17T23:47:59.1962300Z a4e958f84c25: Waiting 2024-12-17T23:47:59.1962548Z 57a000ae2392: Waiting 2024-12-17T23:47:59.1962813Z ea68936d1a73: Waiting 2024-12-17T23:47:59.1963069Z 7745f672cbb1: Waiting 2024-12-17T23:47:59.1963337Z c478133b8a8b: Pulling fs layer 2024-12-17T23:47:59.1963641Z c56df19c4d1f: Pulling fs layer 2024-12-17T23:47:59.1964125Z c6619167a3e6: Pulling fs layer 2024-12-17T23:47:59.1964443Z 7dbfc0fcaa06: Pulling fs layer 2024-12-17T23:47:59.1964737Z a69e77d01a8c: Waiting 2024-12-17T23:47:59.1964997Z c478133b8a8b: Waiting 2024-12-17T23:47:59.1965277Z c6619167a3e6: Waiting 2024-12-17T23:47:59.1965537Z 7dbfc0fcaa06: Waiting 2024-12-17T23:47:59.1965838Z 88d2b1d09ff6: Pulling fs layer 2024-12-17T23:47:59.1966135Z c56df19c4d1f: Waiting 2024-12-17T23:47:59.1966430Z 74215dfb05b9: Pulling fs layer 2024-12-17T23:47:59.1966733Z 88d2b1d09ff6: Waiting 2024-12-17T23:47:59.1967018Z 74215dfb05b9: Waiting 2024-12-17T23:47:59.1967316Z e2f8bba90803: Pulling fs layer 2024-12-17T23:47:59.1967617Z 56e66f0db375: Pulling fs layer 2024-12-17T23:47:59.1967966Z 5e47d6303582: Pulling fs layer 2024-12-17T23:47:59.1968264Z f14b36f011bf: Pulling fs layer 2024-12-17T23:47:59.1968589Z 56e66f0db375: Waiting 2024-12-17T23:47:59.1968835Z f14b36f011bf: Waiting 2024-12-17T23:47:59.1969135Z f40f5582e662: Pulling fs layer 2024-12-17T23:47:59.1969470Z 4699807ef69f: Pulling fs layer 2024-12-17T23:47:59.1969850Z f40f5582e662: Waiting 2024-12-17T23:47:59.1970118Z 633926ef1978: Pulling fs layer 2024-12-17T23:47:59.1970416Z 5e47d6303582: Waiting 2024-12-17T23:47:59.1970676Z 4dd1e4338891: Pulling fs layer 2024-12-17T23:47:59.1970960Z 4699807ef69f: Waiting 2024-12-17T23:47:59.1971225Z fafe335b6832: Pulling fs layer 2024-12-17T23:47:59.1971509Z 4dd1e4338891: Waiting 2024-12-17T23:47:59.1971760Z 7b96d6fc18db: Pulling fs layer 2024-12-17T23:47:59.1972050Z fafe335b6832: Waiting 2024-12-17T23:47:59.1972318Z 5eb2aa388cbd: Pulling fs layer 2024-12-17T23:47:59.1972640Z 7b96d6fc18db: Waiting 2024-12-17T23:47:59.1972907Z 6647884d3ec5: Pulling fs layer 2024-12-17T23:47:59.1973222Z 5eb2aa388cbd: Waiting 2024-12-17T23:47:59.1973532Z fb0d52d47e5d: Pulling fs layer 2024-12-17T23:47:59.1973822Z 6647884d3ec5: Waiting 2024-12-17T23:47:59.1974075Z fb0d52d47e5d: Waiting 2024-12-17T23:47:59.1974328Z 2e082b79862b: Pulling fs layer 2024-12-17T23:47:59.1974627Z a649e2ecceb3: Pulling fs layer 2024-12-17T23:47:59.1974923Z 2e082b79862b: Waiting 2024-12-17T23:47:59.1975191Z 0f48fbbac9ba: Pulling fs layer 2024-12-17T23:47:59.1975487Z d0ca2167ad3f: Pulling fs layer 2024-12-17T23:47:59.1975763Z a649e2ecceb3: Waiting 2024-12-17T23:47:59.1976030Z a7966f301764: Pulling fs layer 2024-12-17T23:47:59.1976323Z 5a7317de868d: Pulling fs layer 2024-12-17T23:47:59.1976612Z 0f48fbbac9ba: Waiting 2024-12-17T23:47:59.1976883Z fe1994ffc671: Pulling fs layer 2024-12-17T23:47:59.1977160Z d0ca2167ad3f: Waiting 2024-12-17T23:47:59.1977417Z 5a7317de868d: Waiting 2024-12-17T23:47:59.1977672Z a7966f301764: Waiting 2024-12-17T23:47:59.1977923Z fe1994ffc671: Waiting 2024-12-17T23:47:59.1978190Z c5d0d5389f3b: Pulling fs layer 2024-12-17T23:47:59.1978873Z 642c500944e6: Pulling fs layer 2024-12-17T23:47:59.1979172Z 990f6a099a47: Pulling fs layer 2024-12-17T23:47:59.1979474Z c5d5ea84cde4: Pulling fs layer 2024-12-17T23:47:59.1979766Z c5d0d5389f3b: Waiting 2024-12-17T23:47:59.1980030Z 32258a594a30: Pulling fs layer 2024-12-17T23:47:59.1980299Z 642c500944e6: Waiting 2024-12-17T23:47:59.1980559Z c5d5ea84cde4: Waiting 2024-12-17T23:47:59.1980813Z 990f6a099a47: Waiting 2024-12-17T23:47:59.1981062Z 32258a594a30: Waiting 2024-12-17T23:47:59.2723168Z aec9e3dc3b21: Verifying Checksum 2024-12-17T23:47:59.2723696Z aec9e3dc3b21: Download complete 2024-12-17T23:47:59.4153942Z 71f87d3b18fa: Download complete 2024-12-17T23:47:59.5446282Z 86e5016c2693: Verifying Checksum 2024-12-17T23:47:59.5446958Z 86e5016c2693: Download complete 2024-12-17T23:47:59.6374592Z 28a6604dc7e2: Download complete 2024-12-17T23:47:59.7265977Z 9a955de22008: Verifying Checksum 2024-12-17T23:47:59.7266561Z 9a955de22008: Download complete 2024-12-17T23:47:59.8150849Z c704eb5496d3: Verifying Checksum 2024-12-17T23:47:59.8151350Z c704eb5496d3: Download complete 2024-12-17T23:47:59.8963040Z 3e321eab5dfa: Verifying Checksum 2024-12-17T23:47:59.8963587Z 3e321eab5dfa: Download complete 2024-12-17T23:47:59.9975380Z df8522890045: Verifying Checksum 2024-12-17T23:47:59.9975974Z df8522890045: Download complete 2024-12-17T23:48:00.1278308Z 6efa7ba6eaf9: Verifying Checksum 2024-12-17T23:48:00.1278727Z 6efa7ba6eaf9: Download complete 2024-12-17T23:48:00.2743874Z c6ad7e035002: Verifying Checksum 2024-12-17T23:48:00.2744441Z c6ad7e035002: Download complete 2024-12-17T23:48:00.2801211Z f29844292cf7: Verifying Checksum 2024-12-17T23:48:00.2801825Z f29844292cf7: Download complete 2024-12-17T23:48:00.3671053Z 8dbdd9e962a9: Verifying Checksum 2024-12-17T23:48:00.3671625Z 8dbdd9e962a9: Download complete 2024-12-17T23:48:00.3725779Z 4f4fb700ef54: Verifying Checksum 2024-12-17T23:48:00.3726294Z 4f4fb700ef54: Download complete 2024-12-17T23:48:00.3969023Z 86e5016c2693: Pull complete 2024-12-17T23:48:00.4186271Z aec9e3dc3b21: Pull complete 2024-12-17T23:48:00.4764103Z 3668f833b0e4: Download complete 2024-12-17T23:48:00.5632915Z aa4035866598: Verifying Checksum 2024-12-17T23:48:00.5633307Z aa4035866598: Download complete 2024-12-17T23:48:00.6646625Z 2d39f659a403: Verifying Checksum 2024-12-17T23:48:00.6647173Z 2d39f659a403: Download complete 2024-12-17T23:48:00.7958211Z cb7d736bf0d8: Verifying Checksum 2024-12-17T23:48:00.7958650Z cb7d736bf0d8: Download complete 2024-12-17T23:48:00.8928619Z 3cc97a252241: Verifying Checksum 2024-12-17T23:48:00.8929051Z 3cc97a252241: Download complete 2024-12-17T23:48:00.9993721Z d901eaf7f5a0: Verifying Checksum 2024-12-17T23:48:00.9994109Z d901eaf7f5a0: Download complete 2024-12-17T23:48:01.0949720Z c8b4328c0ae1: Verifying Checksum 2024-12-17T23:48:01.0950126Z c8b4328c0ae1: Download complete 2024-12-17T23:48:01.1990092Z c3c70a336d7d: Download complete 2024-12-17T23:48:01.3163655Z 0e26764e2949: Verifying Checksum 2024-12-17T23:48:01.3164036Z 0e26764e2949: Download complete 2024-12-17T23:48:02.4017289Z c6543eb4cb51: Verifying Checksum 2024-12-17T23:48:02.4017862Z c6543eb4cb51: Download complete 2024-12-17T23:48:02.5156437Z 3b8143344aee: Download complete 2024-12-17T23:48:02.5854526Z 12920fcbf355: Verifying Checksum 2024-12-17T23:48:02.5855154Z 12920fcbf355: Download complete 2024-12-17T23:48:02.6111937Z ad803458908b: Verifying Checksum 2024-12-17T23:48:02.6112383Z ad803458908b: Download complete 2024-12-17T23:48:02.6885251Z d4e3b9c27d53: Verifying Checksum 2024-12-17T23:48:02.6885706Z d4e3b9c27d53: Download complete 2024-12-17T23:48:02.6983954Z 0704e0edafd4: Verifying Checksum 2024-12-17T23:48:02.6984655Z 0704e0edafd4: Download complete 2024-12-17T23:48:02.7840004Z eff65b2f6bc4: Verifying Checksum 2024-12-17T23:48:02.7840648Z eff65b2f6bc4: Download complete 2024-12-17T23:48:02.8785460Z 8a082c3c11ca: Verifying Checksum 2024-12-17T23:48:02.8786025Z 8a082c3c11ca: Download complete 2024-12-17T23:48:02.9739463Z 5f373dd245ee: Verifying Checksum 2024-12-17T23:48:02.9740042Z 5f373dd245ee: Download complete 2024-12-17T23:48:03.0625496Z f81fdfe75589: Verifying Checksum 2024-12-17T23:48:03.0625987Z f81fdfe75589: Download complete 2024-12-17T23:48:03.1640035Z 57a000ae2392: Download complete 2024-12-17T23:48:03.2361291Z 0bcf8b6039ec: Download complete 2024-12-17T23:48:03.3378530Z 29eb39eca539: Verifying Checksum 2024-12-17T23:48:03.3379141Z 29eb39eca539: Download complete 2024-12-17T23:48:03.8514028Z a4e958f84c25: Verifying Checksum 2024-12-17T23:48:03.8514425Z a4e958f84c25: Download complete 2024-12-17T23:48:03.9248038Z ea68936d1a73: Verifying Checksum 2024-12-17T23:48:03.9248659Z ea68936d1a73: Download complete 2024-12-17T23:48:04.0121292Z 7745f672cbb1: Download complete 2024-12-17T23:48:04.1092035Z a69e77d01a8c: Download complete 2024-12-17T23:48:04.1936078Z c478133b8a8b: Verifying Checksum 2024-12-17T23:48:04.1936886Z c478133b8a8b: Download complete 2024-12-17T23:48:04.4706051Z c56df19c4d1f: Verifying Checksum 2024-12-17T23:48:04.4706606Z c56df19c4d1f: Download complete 2024-12-17T23:48:04.5945948Z c6619167a3e6: Verifying Checksum 2024-12-17T23:48:04.5946744Z c6619167a3e6: Download complete 2024-12-17T23:48:04.6619102Z 7dbfc0fcaa06: Verifying Checksum 2024-12-17T23:48:04.6619668Z 7dbfc0fcaa06: Download complete 2024-12-17T23:48:04.7583481Z 88d2b1d09ff6: Download complete 2024-12-17T23:48:04.8289213Z 74215dfb05b9: Verifying Checksum 2024-12-17T23:48:04.8290148Z 74215dfb05b9: Download complete 2024-12-17T23:48:04.9243262Z e2f8bba90803: Verifying Checksum 2024-12-17T23:48:04.9243910Z e2f8bba90803: Download complete 2024-12-17T23:48:05.0165167Z 56e66f0db375: Download complete 2024-12-17T23:48:05.0876307Z 5e47d6303582: Verifying Checksum 2024-12-17T23:48:05.0876721Z 5e47d6303582: Download complete 2024-12-17T23:48:05.1717469Z f14b36f011bf: Verifying Checksum 2024-12-17T23:48:05.1718360Z f14b36f011bf: Download complete 2024-12-17T23:48:05.2648189Z f40f5582e662: Download complete 2024-12-17T23:48:05.3408103Z 4699807ef69f: Verifying Checksum 2024-12-17T23:48:05.3408635Z 4699807ef69f: Download complete 2024-12-17T23:48:05.4243695Z 633926ef1978: Verifying Checksum 2024-12-17T23:48:05.4244109Z 633926ef1978: Download complete 2024-12-17T23:48:05.5156714Z 4dd1e4338891: Verifying Checksum 2024-12-17T23:48:05.5157336Z 4dd1e4338891: Download complete 2024-12-17T23:48:05.6487422Z fafe335b6832: Verifying Checksum 2024-12-17T23:48:05.6488001Z fafe335b6832: Download complete 2024-12-17T23:48:05.7261506Z 7b96d6fc18db: Verifying Checksum 2024-12-17T23:48:05.7262120Z 7b96d6fc18db: Download complete 2024-12-17T23:48:05.8067417Z 5eb2aa388cbd: Download complete 2024-12-17T23:48:05.8879931Z 6647884d3ec5: Verifying Checksum 2024-12-17T23:48:05.8880444Z 6647884d3ec5: Download complete 2024-12-17T23:48:05.9951548Z fb0d52d47e5d: Verifying Checksum 2024-12-17T23:48:05.9952113Z fb0d52d47e5d: Download complete 2024-12-17T23:48:06.1255439Z 2e082b79862b: Verifying Checksum 2024-12-17T23:48:06.1256064Z 2e082b79862b: Download complete 2024-12-17T23:48:06.2173605Z a649e2ecceb3: Verifying Checksum 2024-12-17T23:48:06.2174236Z a649e2ecceb3: Download complete 2024-12-17T23:48:06.3505438Z 0f48fbbac9ba: Download complete 2024-12-17T23:48:06.4173716Z d0ca2167ad3f: Verifying Checksum 2024-12-17T23:48:06.4174394Z d0ca2167ad3f: Download complete 2024-12-17T23:48:07.0862259Z c3d37a289144: Verifying Checksum 2024-12-17T23:48:07.0862753Z c3d37a289144: Download complete 2024-12-17T23:48:07.1673963Z 5a7317de868d: Verifying Checksum 2024-12-17T23:48:07.1674528Z 5a7317de868d: Download complete 2024-12-17T23:48:07.2573771Z fe1994ffc671: Verifying Checksum 2024-12-17T23:48:07.2574405Z fe1994ffc671: Download complete 2024-12-17T23:48:07.3408560Z c5d0d5389f3b: Verifying Checksum 2024-12-17T23:48:07.3409069Z c5d0d5389f3b: Download complete 2024-12-17T23:48:07.4296481Z 642c500944e6: Download complete 2024-12-17T23:48:07.5043030Z 990f6a099a47: Verifying Checksum 2024-12-17T23:48:07.5043519Z 990f6a099a47: Download complete 2024-12-17T23:48:07.5924737Z c5d5ea84cde4: Verifying Checksum 2024-12-17T23:48:07.5925270Z c5d5ea84cde4: Download complete 2024-12-17T23:48:08.1960327Z 32258a594a30: Verifying Checksum 2024-12-17T23:48:08.1961300Z 32258a594a30: Download complete 2024-12-17T23:48:08.8078487Z a7966f301764: Verifying Checksum 2024-12-17T23:48:08.8078869Z a7966f301764: Download complete 2024-12-17T23:48:10.8051951Z c6543eb4cb51: Pull complete 2024-12-17T23:48:10.9011537Z 71f87d3b18fa: Pull complete 2024-12-17T23:48:13.2055212Z c6ad7e035002: Pull complete 2024-12-17T23:48:13.3267837Z 28a6604dc7e2: Pull complete 2024-12-17T23:48:13.4041974Z 9a955de22008: Pull complete 2024-12-17T23:48:13.4505055Z c704eb5496d3: Pull complete 2024-12-17T23:48:13.5406766Z 3e321eab5dfa: Pull complete 2024-12-17T23:48:13.6257931Z df8522890045: Pull complete 2024-12-17T23:48:13.6920587Z 6efa7ba6eaf9: Pull complete 2024-12-17T23:48:13.7896052Z f29844292cf7: Pull complete 2024-12-17T23:48:13.8379579Z 8dbdd9e962a9: Pull complete 2024-12-17T23:48:30.8932240Z 45260f60bb0a: Download complete 2024-12-17T23:49:15.5716661Z 45260f60bb0a: Pull complete 2024-12-17T23:49:15.5841709Z 4f4fb700ef54: Pull complete 2024-12-17T23:49:15.5939119Z 3668f833b0e4: Pull complete 2024-12-17T23:49:15.6049274Z aa4035866598: Pull complete 2024-12-17T23:49:15.6165440Z 2d39f659a403: Pull complete 2024-12-17T23:49:15.6740038Z cb7d736bf0d8: Pull complete 2024-12-17T23:49:15.6942125Z 3cc97a252241: Pull complete 2024-12-17T23:49:15.7147901Z d901eaf7f5a0: Pull complete 2024-12-17T23:49:15.7367824Z c8b4328c0ae1: Pull complete 2024-12-17T23:49:15.7791870Z c3c70a336d7d: Pull complete 2024-12-17T23:49:15.8016041Z 0e26764e2949: Pull complete 2024-12-17T23:49:18.6870061Z 12920fcbf355: Pull complete 2024-12-17T23:49:18.8803962Z 3b8143344aee: Pull complete 2024-12-17T23:49:18.9878722Z ad803458908b: Pull complete 2024-12-17T23:49:19.0906431Z d4e3b9c27d53: Pull complete 2024-12-17T23:49:19.2892334Z 0704e0edafd4: Pull complete 2024-12-17T23:49:19.4972455Z eff65b2f6bc4: Pull complete 2024-12-17T23:49:26.5124231Z c3d37a289144: Pull complete 2024-12-17T23:49:26.6724032Z 8a082c3c11ca: Pull complete 2024-12-17T23:49:26.8761875Z 5f373dd245ee: Pull complete 2024-12-17T23:49:27.2667812Z f81fdfe75589: Pull complete 2024-12-17T23:49:27.4119278Z 57a000ae2392: Pull complete 2024-12-17T23:49:27.6997431Z 0bcf8b6039ec: Pull complete 2024-12-17T23:49:27.9012317Z 29eb39eca539: Pull complete 2024-12-17T23:49:29.6943610Z a4e958f84c25: Pull complete 2024-12-17T23:49:29.8581924Z ea68936d1a73: Pull complete 2024-12-17T23:49:30.0548076Z 7745f672cbb1: Pull complete 2024-12-17T23:49:30.4460017Z a69e77d01a8c: Pull complete 2024-12-17T23:49:30.6181681Z c478133b8a8b: Pull complete 2024-12-17T23:49:31.0134579Z c56df19c4d1f: Pull complete 2024-12-17T23:49:31.1731861Z c6619167a3e6: Pull complete 2024-12-17T23:49:31.3573033Z 7dbfc0fcaa06: Pull complete 2024-12-17T23:49:31.7370878Z 88d2b1d09ff6: Pull complete 2024-12-17T23:49:31.8738720Z 74215dfb05b9: Pull complete 2024-12-17T23:49:32.0775402Z e2f8bba90803: Pull complete 2024-12-17T23:49:32.2268793Z 56e66f0db375: Pull complete 2024-12-17T23:49:32.5972153Z 5e47d6303582: Pull complete 2024-12-17T23:49:32.7845397Z f14b36f011bf: Pull complete 2024-12-17T23:49:32.9280308Z f40f5582e662: Pull complete 2024-12-17T23:49:33.1194029Z 4699807ef69f: Pull complete 2024-12-17T23:49:33.2438175Z 633926ef1978: Pull complete 2024-12-17T23:49:33.6977898Z 4dd1e4338891: Pull complete 2024-12-17T23:49:33.9481524Z fafe335b6832: Pull complete 2024-12-17T23:49:34.1439393Z 7b96d6fc18db: Pull complete 2024-12-17T23:49:34.5723280Z 5eb2aa388cbd: Pull complete 2024-12-17T23:49:35.0043184Z 6647884d3ec5: Pull complete 2024-12-17T23:49:35.2044019Z fb0d52d47e5d: Pull complete 2024-12-17T23:49:35.6362757Z 2e082b79862b: Pull complete 2024-12-17T23:49:35.6470735Z a649e2ecceb3: Pull complete 2024-12-17T23:49:35.6704687Z 0f48fbbac9ba: Pull complete 2024-12-17T23:49:35.6811130Z d0ca2167ad3f: Pull complete 2024-12-17T23:49:40.9980563Z a7966f301764: Pull complete 2024-12-17T23:49:41.0198696Z 5a7317de868d: Pull complete 2024-12-17T23:49:41.0418571Z fe1994ffc671: Pull complete 2024-12-17T23:49:41.0632428Z c5d0d5389f3b: Pull complete 2024-12-17T23:49:41.0851122Z 642c500944e6: Pull complete 2024-12-17T23:49:41.1067606Z 990f6a099a47: Pull complete 2024-12-17T23:49:41.1496321Z c5d5ea84cde4: Pull complete 2024-12-17T23:49:42.6519452Z 32258a594a30: Pull complete 2024-12-17T23:49:42.6856880Z Digest: sha256:061c030ced34ec2531cb2d0841ec9a10875e6b82f7a9a932b0a62f77f1c9910a 2024-12-17T23:49:42.6897271Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:42.6927016Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:49:42.6972484Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:49:42.6973450Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2024-12-17T23:49:42.6980599Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:42.6981004Z env: 2024-12-17T23:49:42.6981241Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:42.6981527Z ##[endgroup] 2024-12-17T23:49:42.7071691Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-12-17T23:49:42.7072280Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 2024-12-17T23:49:42.7072789Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2024-12-17T23:49:42.7073283Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2024-12-17T23:49:42.7078912Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:42.7079300Z env: 2024-12-17T23:49:42.7079528Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:42.7079803Z ##[endgroup] 2024-12-17T23:49:43.0017600Z Defaulting to user installation because normal site-packages is not writeable 2024-12-17T23:49:43.4248970Z Collecting psutil==5.9.1 2024-12-17T23:49:43.4601109Z Downloading psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB) 2024-12-17T23:49:43.5166671Z Collecting nvidia-ml-py==11.525.84 2024-12-17T23:49:43.5201452Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2024-12-17T23:49:43.6059750Z Installing collected packages: psutil, nvidia-ml-py 2024-12-17T23:49:43.7710279Z Successfully installed nvidia-ml-py-11.525.84 psutil-5.9.1 2024-12-17T23:49:43.8379366Z Prepare all required actions 2024-12-17T23:49:43.8380176Z Getting action download info 2024-12-17T23:49:44.0509964Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2024-12-17T23:49:44.2640779Z Download action repository 'actions/download-artifact@v4' (SHA:fa0a91b85d4f404e444e00e005971372dc801d16) 2024-12-17T23:49:44.5428073Z ##[group]Run ./.github/actions/download-build-artifacts 2024-12-17T23:49:44.5428659Z with: 2024-12-17T23:49:44.5429015Z name: linux-focal-py3.13-clang10 2024-12-17T23:49:44.5429343Z s3-bucket: gha-artifacts 2024-12-17T23:49:44.5429627Z env: 2024-12-17T23:49:44.5429849Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:44.5430120Z ##[endgroup] 2024-12-17T23:49:44.5480797Z ##[group]Run seemethere/download-artifact-s3@v4 2024-12-17T23:49:44.5481367Z with: 2024-12-17T23:49:44.5481743Z name: linux-focal-py3.13-clang10 2024-12-17T23:49:44.5482246Z s3-bucket: gha-artifacts 2024-12-17T23:49:44.5482764Z region: us-east-1 2024-12-17T23:49:44.5483150Z env: 2024-12-17T23:49:44.5483509Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:44.5483974Z ##[endgroup] 2024-12-17T23:49:45.0353248Z (node:231724) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-12-17T23:49:45.0353862Z 2024-12-17T23:49:45.0354060Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-12-17T23:49:45.0354620Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-12-17T23:49:45.0355195Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-12-17T23:49:45.1363619Z Found 1 objects with prefix pytorch/pytorch/12383255652/linux-focal-py3.13-clang10/ 2024-12-17T23:49:45.1364420Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-12-17T23:49:49.1323282Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2024-12-17T23:49:49.1329156Z Artifact download has finished successfully 2024-12-17T23:49:49.1505426Z ##[group]Run unzip -o artifacts.zip 2024-12-17T23:49:49.1505810Z unzip -o artifacts.zip 2024-12-17T23:49:49.1517554Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:49.1518000Z env: 2024-12-17T23:49:49.1518234Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:49.1518527Z ##[endgroup] 2024-12-17T23:49:49.1605366Z Archive: artifacts.zip 2024-12-17T23:49:49.1606572Z creating: dist/ 2024-12-17T23:49:50.1306899Z inflating: dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl 2024-12-17T23:49:50.1307513Z creating: build/custom_test_artifacts/ 2024-12-17T23:49:50.1307986Z creating: build/custom_test_artifacts/custom-op-build/ 2024-12-17T23:49:50.1308485Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2024-12-17T23:49:50.1309497Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeOutput.log 2024-12-17T23:49:50.1310328Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/ 2024-12-17T23:49:50.1311079Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeSystem.cmake 2024-12-17T23:49:50.1311817Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/ 2024-12-17T23:49:50.1312652Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/tmp/ 2024-12-17T23:49:50.1313451Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c 2024-12-17T23:49:50.1314488Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/a.out 2024-12-17T23:49:50.1315336Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/ 2024-12-17T23:49:50.1316279Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/ 2024-12-17T23:49:50.1317490Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp 2024-12-17T23:49:50.1318434Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out 2024-12-17T23:49:50.1319447Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin 2024-12-17T23:49:50.1320334Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake 2024-12-17T23:49:50.1321301Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin 2024-12-17T23:49:50.1322630Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake 2024-12-17T23:49:50.1323428Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeTmp/ 2024-12-17T23:49:50.1324096Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/cmake.check_cache 2024-12-17T23:49:50.1324863Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/ 2024-12-17T23:49:50.1345143Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/depend.make 2024-12-17T23:49:50.1345939Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/link.txt 2024-12-17T23:49:50.1346716Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/cmake_clean.cmake 2024-12-17T23:49:50.1347527Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/build.make 2024-12-17T23:49:50.1348330Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/DependInfo.cmake 2024-12-17T23:49:50.1349387Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/flags.make 2024-12-17T23:49:50.1350181Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/progress.make 2024-12-17T23:49:50.1397835Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/CXX.includecache 2024-12-17T23:49:50.1415201Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/depend.internal 2024-12-17T23:49:50.1507405Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/op.cpp.o 2024-12-17T23:49:50.1508132Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/ 2024-12-17T23:49:50.1532065Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/depend.make 2024-12-17T23:49:50.1532883Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/link.txt 2024-12-17T23:49:50.1533737Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/cmake_clean.cmake 2024-12-17T23:49:50.1534582Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/build.make 2024-12-17T23:49:50.1535425Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/DependInfo.cmake 2024-12-17T23:49:50.1536275Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/flags.make 2024-12-17T23:49:50.1537102Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/progress.make 2024-12-17T23:49:50.1584317Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/CXX.includecache 2024-12-17T23:49:50.1601798Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/depend.internal 2024-12-17T23:49:50.1648487Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/test_custom_ops.cpp.o 2024-12-17T23:49:50.1649405Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeDirectoryInformation.cmake 2024-12-17T23:49:50.1650218Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/TargetDirectories.txt 2024-12-17T23:49:50.1651174Z extracting: build/custom_test_artifacts/custom-op-build/CMakeFiles/progress.marks 2024-12-17T23:49:50.1651848Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile2 2024-12-17T23:49:50.1652492Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile.cmake 2024-12-17T23:49:50.1653136Z inflating: build/custom_test_artifacts/custom-op-build/CMakeCache.txt 2024-12-17T23:49:50.1653710Z inflating: build/custom_test_artifacts/custom-op-build/Makefile 2024-12-17T23:49:50.1654297Z inflating: build/custom_test_artifacts/custom-op-build/cmake_install.cmake 2024-12-17T23:49:50.1741127Z inflating: build/custom_test_artifacts/custom-op-build/libcustom_ops.so 2024-12-17T23:49:50.1779822Z inflating: build/custom_test_artifacts/custom-op-build/test_custom_ops 2024-12-17T23:49:50.1780413Z creating: build/custom_test_artifacts/jit-hook-build/ 2024-12-17T23:49:50.1780902Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/ 2024-12-17T23:49:50.1782217Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeOutput.log 2024-12-17T23:49:50.1782858Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/ 2024-12-17T23:49:50.1783525Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeSystem.cmake 2024-12-17T23:49:50.1784242Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/ 2024-12-17T23:49:50.1784938Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/tmp/ 2024-12-17T23:49:50.1785820Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c 2024-12-17T23:49:50.1786893Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/a.out 2024-12-17T23:49:50.1787619Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/ 2024-12-17T23:49:50.1788411Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/ 2024-12-17T23:49:50.1789257Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp 2024-12-17T23:49:50.1790216Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out 2024-12-17T23:49:50.1791595Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin 2024-12-17T23:49:50.1792445Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake 2024-12-17T23:49:50.1793510Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin 2024-12-17T23:49:50.1794868Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake 2024-12-17T23:49:50.1796254Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeTmp/ 2024-12-17T23:49:50.1797490Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/cmake.check_cache 2024-12-17T23:49:50.1798985Z creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/ 2024-12-17T23:49:50.1820153Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/depend.make 2024-12-17T23:49:50.1820954Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/link.txt 2024-12-17T23:49:50.1821776Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/cmake_clean.cmake 2024-12-17T23:49:50.1822665Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/build.make 2024-12-17T23:49:50.1823477Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/DependInfo.cmake 2024-12-17T23:49:50.1824303Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/flags.make 2024-12-17T23:49:50.1825107Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/progress.make 2024-12-17T23:49:50.1873139Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/CXX.includecache 2024-12-17T23:49:50.1890149Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/depend.internal 2024-12-17T23:49:50.1921166Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/test_jit_hooks.cpp.o 2024-12-17T23:49:50.1922053Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeDirectoryInformation.cmake 2024-12-17T23:49:50.1922852Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/TargetDirectories.txt 2024-12-17T23:49:50.1923574Z extracting: build/custom_test_artifacts/jit-hook-build/CMakeFiles/progress.marks 2024-12-17T23:49:50.1924233Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile2 2024-12-17T23:49:50.1924883Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile.cmake 2024-12-17T23:49:50.1925933Z inflating: build/custom_test_artifacts/jit-hook-build/CMakeCache.txt 2024-12-17T23:49:50.1926729Z inflating: build/custom_test_artifacts/jit-hook-build/Makefile 2024-12-17T23:49:50.1927839Z inflating: build/custom_test_artifacts/jit-hook-build/cmake_install.cmake 2024-12-17T23:49:50.1953316Z inflating: build/custom_test_artifacts/jit-hook-build/test_jit_hooks 2024-12-17T23:49:50.1953858Z creating: build/custom_test_artifacts/custom-backend-build/ 2024-12-17T23:49:50.1954404Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/ 2024-12-17T23:49:50.1956061Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeOutput.log 2024-12-17T23:49:50.1956949Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/ 2024-12-17T23:49:50.1957677Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeSystem.cmake 2024-12-17T23:49:50.1958453Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/ 2024-12-17T23:49:50.1959215Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/tmp/ 2024-12-17T23:49:50.1960059Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c 2024-12-17T23:49:50.1961078Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/a.out 2024-12-17T23:49:50.1961864Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/ 2024-12-17T23:49:50.1962635Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/ 2024-12-17T23:49:50.1963655Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp 2024-12-17T23:49:50.1964919Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out 2024-12-17T23:49:50.1966352Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin 2024-12-17T23:49:50.1967257Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake 2024-12-17T23:49:50.1968358Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin 2024-12-17T23:49:50.1969722Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake 2024-12-17T23:49:50.1970490Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeTmp/ 2024-12-17T23:49:50.1971203Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/cmake.check_cache 2024-12-17T23:49:50.1971979Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/ 2024-12-17T23:49:50.2002786Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/depend.make 2024-12-17T23:49:50.2003855Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/link.txt 2024-12-17T23:49:50.2004795Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/cmake_clean.cmake 2024-12-17T23:49:50.2005748Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/build.make 2024-12-17T23:49:50.2006678Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/DependInfo.cmake 2024-12-17T23:49:50.2007618Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/flags.make 2024-12-17T23:49:50.2008542Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/progress.make 2024-12-17T23:49:50.2056346Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/CXX.includecache 2024-12-17T23:49:50.2073960Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/depend.internal 2024-12-17T23:49:50.2097257Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/test_custom_backend.cpp.o 2024-12-17T23:49:50.2098282Z creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/ 2024-12-17T23:49:50.2101980Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/depend.make 2024-12-17T23:49:50.2102830Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/link.txt 2024-12-17T23:49:50.2103748Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/cmake_clean.cmake 2024-12-17T23:49:50.2104784Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/build.make 2024-12-17T23:49:50.2105688Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/DependInfo.cmake 2024-12-17T23:49:50.2106587Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/flags.make 2024-12-17T23:49:50.2107470Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/progress.make 2024-12-17T23:49:50.2111614Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/CXX.includecache 2024-12-17T23:49:50.2114872Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/depend.internal 2024-12-17T23:49:50.2194882Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/custom_backend.cpp.o 2024-12-17T23:49:50.2195894Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeDirectoryInformation.cmake 2024-12-17T23:49:50.2196752Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/TargetDirectories.txt 2024-12-17T23:49:50.2197540Z extracting: build/custom_test_artifacts/custom-backend-build/CMakeFiles/progress.marks 2024-12-17T23:49:50.2198358Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile2 2024-12-17T23:49:50.2199070Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile.cmake 2024-12-17T23:49:50.2200104Z inflating: build/custom_test_artifacts/custom-backend-build/CMakeCache.txt 2024-12-17T23:49:50.2200957Z inflating: build/custom_test_artifacts/custom-backend-build/Makefile 2024-12-17T23:49:50.2201591Z inflating: build/custom_test_artifacts/custom-backend-build/cmake_install.cmake 2024-12-17T23:49:50.2270638Z inflating: build/custom_test_artifacts/custom-backend-build/libcustom_backend.so 2024-12-17T23:49:50.2289663Z inflating: build/custom_test_artifacts/custom-backend-build/test_custom_backend 2024-12-17T23:49:50.2290184Z creating: build/lib/ 2024-12-17T23:49:50.2290641Z inflating: build/lib/libclog.a 2024-12-17T23:49:50.2299998Z inflating: build/lib/libpthreadpool.a 2024-12-17T23:49:50.2313129Z inflating: build/lib/libcpuinfo.a 2024-12-17T23:49:50.2320221Z inflating: build/lib/libcpuinfo_internals.a 2024-12-17T23:49:50.2379089Z inflating: build/lib/libgtest.a 2024-12-17T23:49:50.2439425Z inflating: build/lib/libbenchmark.a 2024-12-17T23:49:50.2510851Z inflating: build/lib/libprotobuf-lite.a 2024-12-17T23:49:50.2518470Z inflating: build/lib/libittnotify.a 2024-12-17T23:49:50.2542518Z inflating: build/lib/libtensorpipe_uv.a 2024-12-17T23:49:50.2602505Z inflating: build/lib/libasmjit.a 2024-12-17T23:49:50.2681546Z inflating: build/lib/libgloo.a 2024-12-17T23:49:50.3053737Z inflating: build/lib/libprotobuf.a 2024-12-17T23:49:50.3072176Z inflating: build/lib/libfmt.a 2024-12-17T23:49:50.3073541Z inflating: build/lib/libtorch_global_deps.so 2024-12-17T23:49:50.3157416Z inflating: build/lib/libc10.so 2024-12-17T23:49:50.3159579Z inflating: build/lib/libnnpack_reference_layers.a 2024-12-17T23:49:50.3176547Z inflating: build/lib/libpytorch_qnnpack.a 2024-12-17T23:49:50.3589257Z inflating: build/lib/libprotoc.a 2024-12-17T23:49:50.3600632Z inflating: build/lib/libgmock.a 2024-12-17T23:49:50.3601382Z inflating: build/lib/libgtest_main.a 2024-12-17T23:49:50.3602727Z inflating: build/lib/libbenchmark_main.a 2024-12-17T23:49:51.5762615Z inflating: build/lib/libdnnl.a 2024-12-17T23:49:51.6215100Z inflating: build/lib/libtensorpipe.a 2024-12-17T23:49:51.6231117Z inflating: build/lib/libnnpack.a 2024-12-17T23:49:51.6487036Z inflating: build/lib/libkineto.a 2024-12-17T23:49:51.6487703Z inflating: build/lib/libgmock_main.a 2024-12-17T23:49:51.6528187Z inflating: build/lib/libonnx_proto.a 2024-12-17T23:49:51.7202559Z inflating: build/lib/libonnx.a 2024-12-17T23:49:51.7366254Z inflating: build/lib/libmicrokernels-prod.a 2024-12-17T23:49:51.7468776Z inflating: build/lib/libXNNPACK.a 2024-12-17T23:49:51.8411707Z inflating: build/lib/libfbgemm.a 2024-12-17T23:49:51.9080233Z inflating: build/lib/libmicrokernels-all.a 2024-12-17T23:49:54.2092252Z inflating: build/lib/libtorch_cpu.so 2024-12-17T23:49:54.2093081Z inflating: build/lib/libtorch.so 2024-12-17T23:49:54.2097233Z inflating: build/lib/libunbox_lib.a 2024-12-17T23:49:54.2101269Z inflating: build/lib/libshm.so 2024-12-17T23:49:54.2121801Z inflating: build/lib/libjitbackend_test.so 2024-12-17T23:49:54.2191963Z inflating: build/lib/libtorchbind_test.so 2024-12-17T23:49:54.2216789Z inflating: build/lib/libbackend_with_compiler.so 2024-12-17T23:49:54.2242582Z inflating: build/lib/libaoti_custom_ops.so 2024-12-17T23:49:54.4201111Z inflating: build/lib/libtorch_python.so 2024-12-17T23:49:54.4236684Z inflating: build/lib/libnnapi_backend.so 2024-12-17T23:49:54.4237069Z creating: build/bin/ 2024-12-17T23:49:54.4237363Z creating: build/bin/CMakeFiles/ 2024-12-17T23:49:54.4238266Z inflating: build/bin/CMakeFiles/CMakeDirectoryInformation.cmake 2024-12-17T23:49:54.4238876Z extracting: build/bin/CMakeFiles/progress.marks 2024-12-17T23:49:54.4239274Z inflating: build/bin/Makefile 2024-12-17T23:49:54.4239753Z inflating: build/bin/cmake_install.cmake 2024-12-17T23:49:54.4240138Z inflating: build/bin/CTestTestfile.cmake 2024-12-17T23:49:54.4288801Z inflating: build/bin/c10_TypeIndex_test 2024-12-17T23:49:54.4336952Z inflating: build/bin/c10_Synchronized_test 2024-12-17T23:49:54.4387427Z inflating: build/bin/c10_Metaprogramming_test 2024-12-17T23:49:54.4434754Z inflating: build/bin/c10_ConstexprCrc_test 2024-12-17T23:49:54.4483857Z inflating: build/bin/c10_ssize_test 2024-12-17T23:49:54.4536558Z inflating: build/bin/c10_LeftRight_test 2024-12-17T23:49:54.4584096Z inflating: build/bin/c10_DeadlockDetection_test 2024-12-17T23:49:54.4632296Z inflating: build/bin/c10_Half_test 2024-12-17T23:49:54.4683984Z inflating: build/bin/c10_ThreadLocal_test 2024-12-17T23:49:54.4734025Z inflating: build/bin/c10_NetworkFlow_test 2024-12-17T23:49:54.4789046Z inflating: build/bin/c10_DispatchKeySet_test 2024-12-17T23:49:54.4861650Z inflating: build/bin/c10_optional_test 2024-12-17T23:49:54.4908875Z inflating: build/bin/c10_StreamGuard_test 2024-12-17T23:49:54.4966640Z inflating: build/bin/c10_ordered_preserving_dict_test 2024-12-17T23:49:54.5014621Z inflating: build/bin/c10_CompileTimeFunctionPointer_test 2024-12-17T23:49:54.5062329Z inflating: build/bin/c10_tempfile_test 2024-12-17T23:49:54.5111907Z inflating: build/bin/c10_Device_test 2024-12-17T23:49:54.5159000Z inflating: build/bin/c10_TypeTraits_test 2024-12-17T23:49:54.5206568Z inflating: build/bin/c10_error_test 2024-12-17T23:49:54.5255295Z inflating: build/bin/c10_DeviceGuard_test 2024-12-17T23:49:54.5306733Z inflating: build/bin/c10_typeid_test 2024-12-17T23:49:54.5357079Z inflating: build/bin/c10_Scalar_test 2024-12-17T23:49:54.5420915Z inflating: build/bin/c10_cow_test 2024-12-17T23:49:54.5469479Z inflating: build/bin/c10_SymInt_test 2024-12-17T23:49:54.5519676Z inflating: build/bin/c10_Bitset_test 2024-12-17T23:49:54.5567036Z inflating: build/bin/c10_ArrayRef_test 2024-12-17T23:49:54.5620255Z inflating: build/bin/c10_SizesAndStrides_test 2024-12-17T23:49:54.5672927Z inflating: build/bin/c10_InlineStreamGuard_test 2024-12-17T23:49:54.5724440Z inflating: build/bin/c10_InlineDeviceGuard_test 2024-12-17T23:49:54.5773694Z inflating: build/bin/c10_accumulate_test 2024-12-17T23:49:54.5822084Z inflating: build/bin/c10_TypeList_test 2024-12-17T23:49:54.5876396Z inflating: build/bin/c10_string_view_test 2024-12-17T23:49:54.5924507Z inflating: build/bin/c10_bit_cast_test 2024-12-17T23:49:54.5976558Z inflating: build/bin/c10_bfloat16_test 2024-12-17T23:49:54.6026906Z inflating: build/bin/c10_exception_test 2024-12-17T23:49:54.6075737Z inflating: build/bin/c10_irange_test 2024-12-17T23:49:54.6127848Z inflating: build/bin/c10_complex_test 2024-12-17T23:49:54.6176440Z inflating: build/bin/c10_flags_test 2024-12-17T23:49:54.6223926Z inflating: build/bin/c10_generic_math_test 2024-12-17T23:49:54.6355316Z inflating: build/bin/c10_intrusive_ptr_test 2024-12-17T23:49:54.6410229Z inflating: build/bin/c10_complex_math_test 2024-12-17T23:49:54.6463015Z inflating: build/bin/c10_logging_test 2024-12-17T23:49:54.6512924Z inflating: build/bin/c10_lazy_test 2024-12-17T23:49:54.6646479Z inflating: build/bin/c10_small_vector_test 2024-12-17T23:49:54.6697299Z inflating: build/bin/c10_registry_test 2024-12-17T23:49:54.6736234Z inflating: build/bin/c10_intrusive_ptr_benchmark 2024-12-17T23:49:54.6785161Z inflating: build/bin/c10_string_util_test 2024-12-17T23:49:54.7141194Z inflating: build/bin/protoc-3.13.0.0 2024-12-17T23:49:54.7496648Z inflating: build/bin/protoc 2024-12-17T23:49:54.7895315Z inflating: build/bin/vec_test_all_types_AVX2 2024-12-17T23:49:54.8280007Z inflating: build/bin/vec_test_all_types_DEFAULT 2024-12-17T23:49:54.8665247Z inflating: build/bin/vec_test_all_types_AVX512 2024-12-17T23:49:54.8715251Z inflating: build/bin/HashStoreTest 2024-12-17T23:49:54.8765482Z inflating: build/bin/FileStoreTest 2024-12-17T23:49:54.8817625Z inflating: build/bin/TCPStoreTest 2024-12-17T23:49:54.8881005Z inflating: build/bin/ProcessGroupGlooTest 2024-12-17T23:49:54.8930617Z inflating: build/bin/BackoffTest 2024-12-17T23:49:54.8933447Z inflating: build/bin/example_allreduce 2024-12-17T23:49:54.8986478Z inflating: build/bin/test_dist_autograd 2024-12-17T23:49:54.8988816Z inflating: build/bin/parallel_benchmark 2024-12-17T23:49:54.8998084Z inflating: build/bin/aot_model_compiler_test 2024-12-17T23:49:54.9062034Z inflating: build/bin/test_mobile_nnc 2024-12-17T23:49:54.9126557Z inflating: build/bin/test_cpp_rpc 2024-12-17T23:49:54.9173272Z inflating: build/bin/op_allowlist_test 2024-12-17T23:49:54.9225678Z inflating: build/bin/backend_fallback_test 2024-12-17T23:49:54.9318752Z inflating: build/bin/make_boxed_from_unboxed_functor_test 2024-12-17T23:49:54.9376537Z inflating: build/bin/kernel_stackbased_test 2024-12-17T23:49:54.9468067Z inflating: build/bin/kernel_function_test 2024-12-17T23:49:54.9590983Z inflating: build/bin/kernel_function_legacy_test 2024-12-17T23:49:54.9652264Z inflating: build/bin/KernelFunction_test 2024-12-17T23:49:54.9708752Z inflating: build/bin/IListRef_test 2024-12-17T23:49:54.9758089Z inflating: build/bin/xla_tensor_test 2024-12-17T23:49:55.0068078Z inflating: build/bin/test_lazy 2024-12-17T23:49:55.0118057Z inflating: build/bin/type_ptr_test 2024-12-17T23:49:55.0188137Z inflating: build/bin/legacy_vmap_test 2024-12-17T23:49:55.0245700Z inflating: build/bin/type_test 2024-12-17T23:49:55.0322330Z inflating: build/bin/tensor_iterator_test 2024-12-17T23:49:55.0371936Z inflating: build/bin/stride_properties_test 2024-12-17T23:49:55.0420686Z inflating: build/bin/StorageUtils_test 2024-12-17T23:49:55.0475595Z inflating: build/bin/apply_utils_test 2024-12-17T23:49:55.0524824Z inflating: build/bin/weakref_test 2024-12-17T23:49:55.0577757Z inflating: build/bin/NamedTensor_test 2024-12-17T23:49:55.0633156Z inflating: build/bin/scalar_test 2024-12-17T23:49:55.0700095Z inflating: build/bin/Dict_test 2024-12-17T23:49:55.0760509Z inflating: build/bin/basic 2024-12-17T23:49:55.0812560Z inflating: build/bin/broadcast_test 2024-12-17T23:49:55.0867344Z inflating: build/bin/cpu_generator_test 2024-12-17T23:49:55.0929822Z inflating: build/bin/MaybeOwned_test 2024-12-17T23:49:55.1028586Z inflating: build/bin/kernel_lambda_test 2024-12-17T23:49:55.1079191Z inflating: build/bin/cpu_profiling_allocator_test 2024-12-17T23:49:55.1130639Z inflating: build/bin/test_parallel 2024-12-17T23:49:55.1180702Z inflating: build/bin/half_test 2024-12-17T23:49:55.1306373Z inflating: build/bin/kernel_lambda_legacy_test 2024-12-17T23:49:55.1355086Z inflating: build/bin/cpu_allocator_test 2024-12-17T23:49:55.1356772Z inflating: build/bin/verify_api_visibility 2024-12-17T23:49:55.1407215Z inflating: build/bin/static_runtime_bench 2024-12-17T23:49:55.1456172Z inflating: build/bin/Dimname_test 2024-12-17T23:49:55.1544231Z inflating: build/bin/cpu_rng_test 2024-12-17T23:49:55.1825563Z inflating: build/bin/static_runtime_test 2024-12-17T23:49:55.1881639Z inflating: build/bin/atest 2024-12-17T23:49:55.1932492Z inflating: build/bin/memory_overlapping_test 2024-12-17T23:49:55.1980416Z inflating: build/bin/dispatch_key_set_test 2024-12-17T23:49:55.1982951Z inflating: build/bin/thread_init_test 2024-12-17T23:49:55.2031782Z inflating: build/bin/operators_test 2024-12-17T23:49:55.2090854Z inflating: build/bin/inline_container_test 2024-12-17T23:49:55.2186845Z inflating: build/bin/List_test 2024-12-17T23:49:55.2235200Z inflating: build/bin/operator_name_test 2024-12-17T23:49:55.2284238Z inflating: build/bin/wrapdim_test 2024-12-17T23:49:55.2332989Z inflating: build/bin/dlconvertor_test 2024-12-17T23:49:55.2388765Z inflating: build/bin/extension_backend_test 2024-12-17T23:49:55.3629920Z inflating: build/bin/test_api 2024-12-17T23:49:55.3679147Z inflating: build/bin/undefined_tensor_test 2024-12-17T23:49:55.3726872Z inflating: build/bin/lazy_tensor_test 2024-12-17T23:49:55.3818976Z inflating: build/bin/ivalue_test 2024-12-17T23:49:55.3867250Z inflating: build/bin/CppSignature_test 2024-12-17T23:49:55.3918346Z inflating: build/bin/mobile_memory_cleanup 2024-12-17T23:49:55.3972207Z inflating: build/bin/scalar_tensor_test 2024-12-17T23:49:55.4279403Z inflating: build/bin/op_registration_test 2024-12-17T23:49:55.4333428Z inflating: build/bin/native_test 2024-12-17T23:49:55.4383960Z inflating: build/bin/math_kernel_test 2024-12-17T23:49:55.4434469Z inflating: build/bin/memory_format_test 2024-12-17T23:49:55.4482367Z inflating: build/bin/reduce_ops_test 2024-12-17T23:49:55.4532032Z inflating: build/bin/packedtensoraccessor_test 2024-12-17T23:49:55.4599875Z inflating: build/bin/pow_test 2024-12-17T23:49:55.4648283Z inflating: build/bin/reportMemoryUsage_test 2024-12-17T23:49:55.4702303Z inflating: build/bin/quantized_test 2024-12-17T23:49:55.4752719Z inflating: build/bin/test_edge_op_registration 2024-12-17T23:49:55.4756918Z inflating: build/bin/torch_shm_manager 2024-12-17T23:49:55.4772363Z inflating: build/bin/tutorial_tensorexpr 2024-12-17T23:49:55.5763260Z inflating: build/bin/test_tensorexpr 2024-12-17T23:49:55.6321706Z inflating: build/bin/test_jit 2024-12-17T23:49:55.6322130Z creating: .additional_ci_files/ 2024-12-17T23:49:55.6408142Z inflating: .additional_ci_files/test-times.json 2024-12-17T23:49:55.6750466Z inflating: .additional_ci_files/test-class-times.json 2024-12-17T23:49:55.6776099Z ##[group]Run rm artifacts.zip 2024-12-17T23:49:55.6776441Z rm artifacts.zip 2024-12-17T23:49:55.6786814Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:55.6787228Z env: 2024-12-17T23:49:55.6787468Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:55.6787755Z ##[endgroup] 2024-12-17T23:49:55.7120706Z ##[group]Run df -H 2024-12-17T23:49:55.7120985Z df -H 2024-12-17T23:49:55.7126350Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:55.7126752Z env: 2024-12-17T23:49:55.7126979Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:55.7127253Z ##[endgroup] 2024-12-17T23:49:55.7286672Z Filesystem Size Used Avail Use% Mounted on 2024-12-17T23:49:55.7287078Z devtmpfs 4.2M 0 4.2M 0% /dev 2024-12-17T23:49:55.7287433Z tmpfs 8.2G 103k 8.2G 1% /dev/shm 2024-12-17T23:49:55.7287942Z tmpfs 3.3G 488k 3.3G 1% /run 2024-12-17T23:49:55.7288285Z /dev/nvme0n1p1 161G 25G 137G 16% / 2024-12-17T23:49:55.7288903Z tmpfs 8.2G 21k 8.2G 1% /tmp 2024-12-17T23:49:55.7289561Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2024-12-17T23:49:55.7367158Z Prepare all required actions 2024-12-17T23:49:55.7367577Z Getting action download info 2024-12-17T23:49:55.9202745Z ##[group]Run ./.github/actions/download-td-artifacts 2024-12-17T23:49:55.9203122Z with: 2024-12-17T23:49:55.9203332Z env: 2024-12-17T23:49:55.9203558Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:55.9203821Z ##[endgroup] 2024-12-17T23:49:55.9307007Z ##[group]Run seemethere/download-artifact-s3@v4 2024-12-17T23:49:55.9307372Z with: 2024-12-17T23:49:55.9307598Z name: td_results 2024-12-17T23:49:55.9307860Z s3-bucket: gha-artifacts 2024-12-17T23:49:55.9308133Z region: us-east-1 2024-12-17T23:49:55.9308371Z env: 2024-12-17T23:49:55.9308617Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:55.9308891Z ##[endgroup] 2024-12-17T23:49:56.3822701Z (node:231746) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2024-12-17T23:49:56.3823230Z 2024-12-17T23:49:56.3823508Z Please migrate your code to use AWS SDK for JavaScript (v3). 2024-12-17T23:49:56.3824074Z For more information, check the migration guide at https://a.co/7PzMCcy 2024-12-17T23:49:56.3824633Z (Use `node --trace-warnings ...` to show where the warning was created) 2024-12-17T23:49:56.4902881Z Found 1 objects with prefix pytorch/pytorch/12383255652/td_results/ 2024-12-17T23:49:56.4903574Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-12-17T23:49:56.5305536Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2024-12-17T23:49:56.5310783Z Artifact download has finished successfully 2024-12-17T23:49:56.5483878Z ##[group]Run mkdir -p .additional_ci_files 2024-12-17T23:49:56.5484524Z mkdir -p .additional_ci_files 2024-12-17T23:49:56.5485273Z mv td_results.json .additional_ci_files/td_results.json || true 2024-12-17T23:49:56.5494971Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:56.5495578Z env: 2024-12-17T23:49:56.5495945Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:56.5496385Z ##[endgroup] 2024-12-17T23:49:56.5693351Z ##[group]Run .github/scripts/parse_ref.py 2024-12-17T23:49:56.5693747Z .github/scripts/parse_ref.py 2024-12-17T23:49:56.5699243Z shell: /usr/bin/bash -e {0} 2024-12-17T23:49:56.5699538Z env: 2024-12-17T23:49:56.5699770Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:56.5700049Z ##[endgroup] 2024-12-17T23:49:56.6021854Z Prepare all required actions 2024-12-17T23:49:56.6086695Z ##[group]Run ./.github/actions/get-workflow-job-id 2024-12-17T23:49:56.6087076Z with: 2024-12-17T23:49:56.6087502Z github-token: *** 2024-12-17T23:49:56.6087738Z env: 2024-12-17T23:49:56.6087974Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:56.6088280Z ##[endgroup] 2024-12-17T23:49:56.6139471Z ##[group]Run set -eux 2024-12-17T23:49:56.6139758Z set -eux 2024-12-17T23:49:56.6140206Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-12-17T23:49:56.6146329Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:56.6146724Z env: 2024-12-17T23:49:56.6146959Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:56.6147532Z GITHUB_TOKEN: *** 2024-12-17T23:49:56.6147889Z ##[endgroup] 2024-12-17T23:49:56.6171498Z + python3 .github/scripts/get_workflow_job_id.py 12383255652 i-0897f70f52bdfd343 2024-12-17T23:49:58.2764397Z setting job-id=34566067022 2024-12-17T23:49:58.2765034Z setting job-name=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:58.2997562Z Prepare all required actions 2024-12-17T23:49:58.2998188Z Getting action download info 2024-12-17T23:49:58.4543440Z ##[group]Run ./.github/actions/filter-test-configs 2024-12-17T23:49:58.4543957Z with: 2024-12-17T23:49:58.4544385Z github-token: *** 2024-12-17T23:49:58.4546461Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]} 2024-12-17T23:49:58.4548803Z job-name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:58.4549283Z env: 2024-12-17T23:49:58.4549498Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:58.4549775Z ##[endgroup] 2024-12-17T23:49:58.4745132Z ##[group]Run nick-fields/retry@v3.0.0 2024-12-17T23:49:58.4745460Z with: 2024-12-17T23:49:58.4745682Z shell: bash 2024-12-17T23:49:58.4745906Z timeout_minutes: 10 2024-12-17T23:49:58.4746163Z max_attempts: 5 2024-12-17T23:49:58.4746412Z retry_wait_seconds: 30 2024-12-17T23:49:58.4747211Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-12-17T23:49:58.4748050Z polling_interval_seconds: 1 2024-12-17T23:49:58.4748343Z warning_on_retry: true 2024-12-17T23:49:58.4748621Z continue_on_error: false 2024-12-17T23:49:58.4748889Z env: 2024-12-17T23:49:58.4749229Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:58.4749823Z GITHUB_TOKEN: *** 2024-12-17T23:49:58.4750079Z ##[endgroup] 2024-12-17T23:49:58.5827483Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2024-12-17T23:49:58.8219539Z Defaulting to user installation because normal site-packages is not writeable 2024-12-17T23:49:58.8373601Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1) 2024-12-17T23:49:58.8378473Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1) 2024-12-17T23:49:58.8496201Z Requirement already satisfied: charset-normalizer~=2.0.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2.0.12) 2024-12-17T23:49:58.8501930Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2024-12-17T23:49:58.8511984Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2024-12-17T23:49:58.8519108Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2024.12.14) 2024-12-17T23:49:59.5504845Z Command completed after 1 attempt(s). 2024-12-17T23:49:59.5631218Z ##[group]Run set -x 2024-12-17T23:49:59.5631517Z set -x 2024-12-17T23:49:59.5631769Z  2024-12-17T23:49:59.5632159Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-12-17T23:49:59.5632662Z # in runner workspace 2024-12-17T23:49:59.5633068Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2024-12-17T23:49:59.5640234Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:59.5640809Z env: 2024-12-17T23:49:59.5641142Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:59.5641543Z ##[endgroup] 2024-12-17T23:49:59.5667112Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2024-12-17T23:49:59.6060851Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2024-12-17T23:49:59.6061307Z echo "Workflow: ${GITHUB_WORKFLOW}" 2024-12-17T23:49:59.6061667Z echo "Job name: ${JOB_NAME}" 2024-12-17T23:49:59.6062074Z  2024-12-17T23:49:59.6062472Z # Use relative path here as this could be checked out anywhere, not necessarily 2024-12-17T23:49:59.6062973Z # in runner workspace 2024-12-17T23:49:59.6063412Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2024-12-17T23:49:59.6063904Z  --workflow "${GITHUB_WORKFLOW}" \ 2024-12-17T23:49:59.6064255Z  --job-name "${JOB_NAME}" \ 2024-12-17T23:49:59.6066406Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]}" \ 2024-12-17T23:49:59.6068578Z  --selected-test-configs "" \ 2024-12-17T23:49:59.6068928Z  --pr-number "${PR_NUMBER}" \ 2024-12-17T23:49:59.6069243Z  --tag "${TAG}" \ 2024-12-17T23:49:59.6069548Z  --event-name "${EVENT_NAME}" \ 2024-12-17T23:49:59.6069887Z  --schedule "${SCHEDULE}" \ 2024-12-17T23:49:59.6070211Z  --branch "${HEAD_BRANCH}" 2024-12-17T23:49:59.6076150Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:49:59.6076546Z env: 2024-12-17T23:49:59.6076762Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:49:59.6077259Z GITHUB_TOKEN: *** 2024-12-17T23:49:59.6077694Z JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:49:59.6078171Z PR_NUMBER: 2024-12-17T23:49:59.6078400Z TAG: 2024-12-17T23:49:59.6078628Z EVENT_NAME: push 2024-12-17T23:49:59.6078859Z SCHEDULE: 2024-12-17T23:49:59.6079087Z HEAD_BRANCH: 2024-12-17T23:49:59.6079324Z ##[endgroup] 2024-12-17T23:49:59.6102135Z Workflow: pull 2024-12-17T23:49:59.6102579Z Job name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:50:00.0379458Z ##[group]Run echo "Filtered matrix:" 2024-12-17T23:50:00.0379835Z echo "Filtered matrix:" 2024-12-17T23:50:00.0381963Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}]}" 2024-12-17T23:50:00.0384088Z  2024-12-17T23:50:00.0384318Z echo 2024-12-17T23:50:00.0384618Z echo "Is the current job unstable? False" 2024-12-17T23:50:00.0384970Z  2024-12-17T23:50:00.0385201Z echo 2024-12-17T23:50:00.0385469Z echo "Is keep-going label set? False" 2024-12-17T23:50:00.0385808Z  2024-12-17T23:50:00.0386031Z echo 2024-12-17T23:50:00.0386283Z echo "Renabled issues? " 2024-12-17T23:50:00.0392014Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:50:00.0392411Z env: 2024-12-17T23:50:00.0392634Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:50:00.0393084Z ##[endgroup] 2024-12-17T23:50:00.0421163Z Filtered matrix: 2024-12-17T23:50:00.0424531Z {include: [{config: default, shard: 1, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: linux.4xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: linux.2xlarge}]} 2024-12-17T23:50:00.0428277Z 2024-12-17T23:50:00.0428490Z Is the current job unstable? False 2024-12-17T23:50:00.0428847Z 2024-12-17T23:50:00.0429059Z Is keep-going label set? False 2024-12-17T23:50:00.0429387Z 2024-12-17T23:50:00.0429581Z Renabled issues? 2024-12-17T23:50:00.0640045Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-12-17T23:50:00.0640588Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2024-12-17T23:50:00.0646174Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-17T23:50:00.0646588Z env: 2024-12-17T23:50:00.0646825Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:50:00.0647107Z JOB_TIMEOUT: 600 2024-12-17T23:50:00.0647355Z ##[endgroup] 2024-12-17T23:50:00.0862699Z ##[group]Run set -x 2024-12-17T23:50:00.0863049Z set -x 2024-12-17T23:50:00.0863298Z  2024-12-17T23:50:00.0863592Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2024-12-17T23:50:00.0864000Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2024-12-17T23:50:00.0864430Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2024-12-17T23:50:00.0864816Z  TEST_COMMAND=.ci/onnx/test.sh 2024-12-17T23:50:00.0865143Z else 2024-12-17T23:50:00.0865422Z  TEST_COMMAND=.ci/pytorch/test.sh 2024-12-17T23:50:00.0865743Z fi 2024-12-17T23:50:00.0865984Z  2024-12-17T23:50:00.0866352Z # detached container should get cleaned up by teardown_ec2_linux 2024-12-17T23:50:00.0866915Z # TODO: Stop building test binaries as part of the build phase 2024-12-17T23:50:00.0867433Z # Used for GPU_FLAG since that doesn't play nice 2024-12-17T23:50:00.0867880Z # shellcheck disable=SC2086,SC2090 2024-12-17T23:50:00.0868244Z container_name=$(docker run \ 2024-12-17T23:50:00.0868566Z  ${GPU_FLAG:-} \ 2024-12-17T23:50:00.0868888Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2024-12-17T23:50:00.0869261Z  -e BUILD_ENVIRONMENT \ 2024-12-17T23:50:00.0869587Z  -e PR_NUMBER \ 2024-12-17T23:50:00.0869880Z  -e GITHUB_ACTIONS \ 2024-12-17T23:50:00.0870195Z  -e GITHUB_REPOSITORY \ 2024-12-17T23:50:00.0870502Z  -e GITHUB_WORKFLOW \ 2024-12-17T23:50:00.0870807Z  -e GITHUB_JOB \ 2024-12-17T23:50:00.0871097Z  -e GITHUB_RUN_ID \ 2024-12-17T23:50:00.0871402Z  -e GITHUB_RUN_NUMBER \ 2024-12-17T23:50:00.0871727Z  -e GITHUB_RUN_ATTEMPT \ 2024-12-17T23:50:00.0872034Z  -e JOB_ID \ 2024-12-17T23:50:00.0872315Z  -e JOB_NAME \ 2024-12-17T23:50:00.0872597Z  -e BASE_SHA \ 2024-12-17T23:50:00.0872873Z  -e BRANCH \ 2024-12-17T23:50:00.0873146Z  -e SHA1 \ 2024-12-17T23:50:00.0873408Z  -e AWS_DEFAULT_REGION \ 2024-12-17T23:50:00.0873722Z  -e IN_WHEEL_TEST \ 2024-12-17T23:50:00.0874019Z  -e SHARD_NUMBER \ 2024-12-17T23:50:00.0874320Z  -e TEST_CONFIG \ 2024-12-17T23:50:00.0874617Z  -e NUM_TEST_SHARDS \ 2024-12-17T23:50:00.0874916Z  -e REENABLED_ISSUES \ 2024-12-17T23:50:00.0875240Z  -e CONTINUE_THROUGH_ERROR \ 2024-12-17T23:50:00.0875574Z  -e VERBOSE_TEST_LOGS \ 2024-12-17T23:50:00.0876008Z  -e TEST_SHOWLOCALS \ 2024-12-17T23:50:00.0876319Z  -e NO_TEST_TIMEOUT \ 2024-12-17T23:50:00.0876609Z  -e NO_TD \ 2024-12-17T23:50:00.0876893Z  -e TD_DISTRIBUTED \ 2024-12-17T23:50:00.0877206Z  -e PR_LABELS \ 2024-12-17T23:50:00.0877677Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2024-12-17T23:50:00.0878036Z  -e SCCACHE_BUCKET \ 2024-12-17T23:50:00.0878334Z  -e SCCACHE_REGION \ 2024-12-17T23:50:00.0878645Z  -e SCCACHE_S3_KEY_PREFIX \ 2024-12-17T23:50:00.0878967Z  -e XLA_CUDA \ 2024-12-17T23:50:00.0879280Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2024-12-17T23:50:00.0879661Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2024-12-17T23:50:00.0880043Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2024-12-17T23:50:00.0880433Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2024-12-17T23:50:00.0880796Z  -e HUGGING_FACE_HUB_TOKEN \ 2024-12-17T23:50:00.0881150Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2024-12-17T23:50:00.0881496Z  -e DASHBOARD_TAG \ 2024-12-17T23:50:00.0881799Z  -e IS_A100_RUNNER \ 2024-12-17T23:50:00.0882101Z  -e ARTIFACTS_FILE_SUFFIX \ 2024-12-17T23:50:00.0882489Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2024-12-17T23:50:00.0882911Z  --security-opt seccomp=unconfined \ 2024-12-17T23:50:00.0883371Z  --cap-add=SYS_PTRACE \ 2024-12-17T23:50:00.0883693Z  --ipc=host \ 2024-12-17T23:50:00.0883973Z  --shm-size="${SHM_SIZE}" \ 2024-12-17T23:50:00.0884290Z  --tty \ 2024-12-17T23:50:00.0884548Z  --detach \ 2024-12-17T23:50:00.0884840Z  --name="${container_name}" \ 2024-12-17T23:50:00.0885176Z  --user jenkins \ 2024-12-17T23:50:00.0885538Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2024-12-17T23:50:00.0885963Z  -w /var/lib/jenkins/workspace \ 2024-12-17T23:50:00.0886304Z  "${DOCKER_IMAGE}" 2024-12-17T23:50:00.0886585Z ) 2024-12-17T23:50:00.0886904Z # Propagate download.pytorch.org IP to container 2024-12-17T23:50:00.0887598Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2024-12-17T23:50:00.0888325Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2024-12-17T23:50:00.0889044Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2024-12-17T23:50:00.0894621Z shell: /usr/bin/bash -e {0} 2024-12-17T23:50:00.0894921Z env: 2024-12-17T23:50:00.0895157Z GIT_DEFAULT_BRANCH: main 2024-12-17T23:50:00.0895500Z BUILD_ENVIRONMENT: linux-focal-py3.13-clang10 2024-12-17T23:50:00.0895847Z PR_NUMBER: 2024-12-17T23:50:00.0896112Z GITHUB_REPOSITORY: pytorch/pytorch 2024-12-17T23:50:00.0896443Z GITHUB_WORKFLOW: pull 2024-12-17T23:50:00.0896716Z GITHUB_JOB: test 2024-12-17T23:50:00.0896975Z GITHUB_RUN_ID: 12383255652 2024-12-17T23:50:00.0897264Z GITHUB_RUN_NUMBER: 276594 2024-12-17T23:50:00.0897558Z GITHUB_RUN_ATTEMPT: 1 2024-12-17T23:50:00.0897826Z JOB_ID: 34566067022 2024-12-17T23:50:00.0898481Z JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:50:00.0898986Z BRANCH: release/2.6 2024-12-17T23:50:00.0899282Z SHA1: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:00.0899701Z BASE_SHA: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:00.0900077Z TEST_CONFIG: dynamo_wrapped 2024-12-17T23:50:00.0900370Z SHARD_NUMBER: 1 2024-12-17T23:50:00.0900634Z NUM_TEST_SHARDS: 3 2024-12-17T23:50:00.0900883Z REENABLED_ISSUES: 2024-12-17T23:50:00.0901160Z CONTINUE_THROUGH_ERROR: False 2024-12-17T23:50:00.0901470Z VERBOSE_TEST_LOGS: False 2024-12-17T23:50:00.0901761Z TEST_SHOWLOCALS: False 2024-12-17T23:50:00.0902048Z NO_TEST_TIMEOUT: False 2024-12-17T23:50:00.0902308Z NO_TD: False 2024-12-17T23:50:00.0902556Z TD_DISTRIBUTED: False 2024-12-17T23:50:00.0902897Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2024-12-17T23:50:00.0903284Z SCCACHE_REGION: us-east-1 2024-12-17T23:50:00.0903581Z SCCACHE_S3_KEY_PREFIX: pull 2024-12-17T23:50:00.0903865Z SHM_SIZE: 1g 2024-12-17T23:50:00.0904686Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:50:00.0905432Z XLA_CUDA: 2024-12-17T23:50:00.0905817Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:50:00.0906302Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2024-12-17T23:50:00.0906647Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2024-12-17T23:50:00.0906963Z DASHBOARD_TAG: 2024-12-17T23:50:00.0907418Z HUGGING_FACE_HUB_TOKEN: *** 2024-12-17T23:50:00.0907875Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2024-12-17T23:50:00.0908192Z IS_A100_RUNNER: 0 2024-12-17T23:50:00.0908583Z ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-17T23:50:00.0909040Z ##[endgroup] 2024-12-17T23:50:00.0931722Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]] 2024-12-17T23:50:00.0932357Z + [[ linux-focal-py3.13-clang10 == *onnx* ]] 2024-12-17T23:50:00.0932722Z + TEST_COMMAND=.ci/pytorch/test.sh 2024-12-17T23:50:00.0939905Z +++ nproc --ignore=2 2024-12-17T23:50:00.1061231Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_REGION -e SCCACHE_S3_KEY_PREFIX -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --env-file=/tmp/github_env_12383255652 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-17T23:50:07.3784696Z + container_name=6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-17T23:50:07.3788761Z + grep download.pytorch.org /etc/hosts 2024-12-17T23:50:07.3789896Z + docker exec -i 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 sudo bash -c '/bin/cat >> /etc/hosts' 2024-12-17T23:50:07.5226325Z + echo DOCKER_CONTAINER_ID=6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-17T23:50:07.5229677Z ++ echo dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl 2024-12-17T23:50:07.5231797Z + docker exec -t 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 sh -c 'python3 -m pip install dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2024-12-17T23:50:08.3465295Z Processing ./dist/torch-2.6.0a0+git0cdf8b1-cp313-cp313-linux_x86_64.whl (from torch==2.6.0a0+git0cdf8b1) 2024-12-17T23:50:08.7700665Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.16.1) 2024-12-17T23:50:08.7703668Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (4.12.2) 2024-12-17T23:50:08.7706587Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2.8.8) 2024-12-17T23:50:08.7709569Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.1.4) 2024-12-17T23:50:08.7712687Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2024.10.0) 2024-12-17T23:50:08.7724322Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (75.1.0) 2024-12-17T23:50:08.7728994Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (1.13.1) 2024-12-17T23:50:08.7744143Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy==1.13.1->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (1.3.0) 2024-12-17T23:50:08.7760189Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.3.0) 2024-12-17T23:50:08.7780499Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (2.1.2) 2024-12-17T23:50:08.7885693Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.6.0a0+git0cdf8b1->torch==2.6.0a0+git0cdf8b1) (3.0.2) 2024-12-17T23:50:09.0179736Z Installing collected packages: torch 2024-12-17T23:50:18.9235842Z Successfully installed torch-2.6.0a0+git0cdf8b1 2024-12-17T23:50:19.0155547Z + export TERM=vt100 2024-12-17T23:50:19.0156109Z + TERM=vt100 2024-12-17T23:50:19.0157790Z ++ dirname .ci/pytorch/test.sh 2024-12-17T23:50:19.0177877Z + source .ci/pytorch/common.sh 2024-12-17T23:50:19.0185994Z +++ dirname .ci/pytorch/common.sh 2024-12-17T23:50:19.0192185Z ++ source .ci/pytorch/common_utils.sh 2024-12-17T23:50:19.0199249Z +++ declare -f -t trap_add 2024-12-17T23:50:19.0205243Z ++ set -ex 2024-12-17T23:50:19.0205521Z ++ [[ linux-focal-py3.13-clang10 == *rocm* ]] 2024-12-17T23:50:19.0205875Z ++ BUILD_TEST_LIBTORCH=0 2024-12-17T23:50:19.0206576Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2024-12-17T23:50:19.0206942Z + [[ -d /var/lib/jenkins/workspace ]] 2024-12-17T23:50:19.0210020Z ++ stat -c %u /var/lib/jenkins/workspace 2024-12-17T23:50:19.0263655Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2024-12-17T23:50:19.0264255Z + trap_add cleanup_workspace EXIT 2024-12-17T23:50:19.0264798Z + trap_add_cmd=cleanup_workspace 2024-12-17T23:50:19.0265136Z + shift 2024-12-17T23:50:19.0265379Z + for trap_add_name in "$@" 2024-12-17T23:50:19.0270104Z +++ trap -p EXIT 2024-12-17T23:50:19.0273022Z ++ eval 'extract_trap_cmd ' 2024-12-17T23:50:19.0273357Z +++ extract_trap_cmd 2024-12-17T23:50:19.0273610Z +++ printf '%s\n' '' 2024-12-17T23:50:19.0273893Z ++ printf '%s\n' cleanup_workspace 2024-12-17T23:50:19.0275442Z + trap -- ' 2024-12-17T23:50:19.0275822Z cleanup_workspace' EXIT 2024-12-17T23:50:19.0276158Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2024-12-17T23:50:19.4881384Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2024-12-17T23:50:19.4906957Z + echo 'Environment variables:' 2024-12-17T23:50:19.4907327Z Environment variables: 2024-12-17T23:50:19.4907602Z + env 2024-12-17T23:50:19.4931316Z INSTALLED_DB=yes 2024-12-17T23:50:19.4932120Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:50:19.4932774Z CONTINUE_THROUGH_ERROR=False 2024-12-17T23:50:19.4933297Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10 2024-12-17T23:50:19.4933874Z HOSTNAME=6707fe6f6119 2024-12-17T23:50:19.4934470Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.4935121Z GITHUB_ACTION=__self 2024-12-17T23:50:19.4935437Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-12-17T23:50:19.4935757Z GITHUB_RUN_NUMBER=276594 2024-12-17T23:50:19.4936043Z TEST_CONFIG=dynamo_wrapped 2024-12-17T23:50:19.4936331Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-12-17T23:50:19.4936674Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-12-17T23:50:19.4937004Z IS_A100_RUNNER=0 2024-12-17T23:50:19.4937778Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2024-12-17T23:50:19.4938312Z GITHUB_TRIGGERING_ACTOR=malfet 2024-12-17T23:50:19.4938872Z GITHUB_REF_TYPE=branch 2024-12-17T23:50:19.4939167Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-12-17T23:50:19.4939508Z BASE_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.4939862Z XLA_CUDA= 2024-12-17T23:50:19.4940217Z HUGGING_FACE_HUB_TOKEN=*** 2024-12-17T23:50:19.4941786Z *** 2024-12-17T23:50:19.4942019Z GITHUB_REPOSITORY_ID=65600975 2024-12-17T23:50:19.4942323Z GITHUB_ACTIONS=true 2024-12-17T23:50:19.4942616Z SHA1=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.4943289Z GITHUB_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.4943857Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/release/2.6 2024-12-17T23:50:19.4944355Z UCC_HOME=/usr 2024-12-17T23:50:19.4944606Z VERBOSE_TEST_LOGS=False 2024-12-17T23:50:19.4944898Z GITHUB_REF=refs/heads/release/2.6 2024-12-17T23:50:19.4945206Z SHARD_NUMBER=1 2024-12-17T23:50:19.4945472Z GITHUB_REF_PROTECTED=true 2024-12-17T23:50:19.4945741Z HOME=/var/lib/jenkins 2024-12-17T23:50:19.4946209Z GITHUB_API_URL=https://api.github.com 2024-12-17T23:50:19.4946568Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-12-17T23:50:19.4946883Z UCX_COMMIT= 2024-12-17T23:50:19.4947125Z SCCACHE_S3_KEY_PREFIX=pull 2024-12-17T23:50:19.4947400Z NUM_TEST_SHARDS=3 2024-12-17T23:50:19.4947648Z UCX_HOME=/usr 2024-12-17T23:50:19.4948233Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.4949073Z JOB_NAME=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:50:19.4949867Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.4950690Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-12-17T23:50:19.4951209Z GITHUB_EVENT_NAME=push 2024-12-17T23:50:19.4951488Z DASHBOARD_TAG= 2024-12-17T23:50:19.4951740Z GITHUB_RUN_ID=12383255652 2024-12-17T23:50:19.4952407Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.4953110Z GITHUB_ACTOR=malfet 2024-12-17T23:50:19.4953351Z PR_NUMBER= 2024-12-17T23:50:19.4953581Z DESIRED_CUDA= 2024-12-17T23:50:19.4953829Z GITHUB_RUN_ATTEMPT=1 2024-12-17T23:50:19.4954113Z ANACONDA_PYTHON_VERSION=3.13 2024-12-17T23:50:19.4954465Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-12-17T23:50:19.4954833Z TERM=vt100 2024-12-17T23:50:19.4955068Z INSTALLED_VISION=yes 2024-12-17T23:50:19.4955331Z BRANCH=release/2.6 2024-12-17T23:50:19.4955591Z SCCACHE_REGION=us-east-1 2024-12-17T23:50:19.4955965Z OPENSSL_ROOT_DIR=/opt/openssl 2024-12-17T23:50:19.4956275Z CUDA_PATH=/usr/local/cuda 2024-12-17T23:50:19.4956817Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-12-17T23:50:19.4957428Z GITHUB_SERVER_URL=https://github.com 2024-12-17T23:50:19.4957749Z UCC_COMMIT= 2024-12-17T23:50:19.4957971Z REENABLED_ISSUES= 2024-12-17T23:50:19.4958219Z DOCS= 2024-12-17T23:50:19.4958447Z SHLVL=1 2024-12-17T23:50:19.4958665Z MAX_JOBS=6 2024-12-17T23:50:19.4958902Z GITHUB_ACTOR_ID=2453524 2024-12-17T23:50:19.4959257Z GITHUB_WORKFLOW_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.4959673Z GITHUB_REF_NAME=release/2.6 2024-12-17T23:50:19.4960111Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:50:19.4960565Z GITHUB_JOB=test 2024-12-17T23:50:19.4960815Z NO_TEST_TIMEOUT=False 2024-12-17T23:50:19.4961071Z TD_DISTRIBUTED=False 2024-12-17T23:50:19.4961357Z GITHUB_REPOSITORY=pytorch/pytorch 2024-12-17T23:50:19.4961678Z GITHUB_RETENTION_DAYS=90 2024-12-17T23:50:19.4961967Z OPENSSL_DIR=/opt/openssl 2024-12-17T23:50:19.4962255Z GITHUB_ACTION_REPOSITORY= 2024-12-17T23:50:19.4963046Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:50:19.4963964Z GITHUB_BASE_REF= 2024-12-17T23:50:19.4964224Z INSTALLED_ACL= 2024-12-17T23:50:19.4964616Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-17T23:50:19.4965065Z CI=true 2024-12-17T23:50:19.4965295Z GITHUB_REPOSITORY_OWNER=pytorch 2024-12-17T23:50:19.4965593Z JOB_ID=34566067022 2024-12-17T23:50:19.4965847Z INSTALLED_PROTOBUF=yes 2024-12-17T23:50:19.4966112Z GITHUB_HEAD_REF= 2024-12-17T23:50:19.4966357Z GITHUB_ACTION_REF= 2024-12-17T23:50:19.4966657Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-12-17T23:50:19.4967030Z TEST_SHOWLOCALS=False 2024-12-17T23:50:19.4967301Z GITHUB_WORKFLOW=pull 2024-12-17T23:50:19.4967578Z DEBIAN_FRONTEND=noninteractive 2024-12-17T23:50:19.4968228Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.4968870Z NO_TD=False 2024-12-17T23:50:19.4969117Z SKIP_SCCACHE_INITIALIZATION=1 2024-12-17T23:50:19.4969412Z _=/usr/bin/env 2024-12-17T23:50:19.4969838Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2024-12-17T23:50:19.5076596Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2024-12-17T23:50:19.5077286Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2024-12-17T23:50:19.5078052Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2024-12-17T23:50:19.5078896Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2024-12-17T23:50:19.5079519Z + BUILD_DIR=build 2024-12-17T23:50:19.5079786Z + BUILD_RENAMED_DIR=build_renamed 2024-12-17T23:50:19.5080097Z + BUILD_BIN_DIR=build/bin 2024-12-17T23:50:19.5080365Z + SHARD_NUMBER=1 2024-12-17T23:50:19.5080636Z + NUM_TEST_SHARDS=3 2024-12-17T23:50:19.5080880Z + export VALGRIND=ON 2024-12-17T23:50:19.5081148Z + VALGRIND=ON 2024-12-17T23:50:19.5081431Z + [[ linux-focal-py3.13-clang10 == *clang9* ]] 2024-12-17T23:50:19.5081827Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2024-12-17T23:50:19.5082149Z + [[ 0 == \1 ]] 2024-12-17T23:50:19.5082394Z + [[ False == \1 ]] 2024-12-17T23:50:19.5082682Z + [[ linux-focal-py3.13-clang10 != *bazel* ]] 2024-12-17T23:50:19.5083595Z ++ realpath build/custom_test_artifacts 2024-12-17T23:50:19.5105052Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2024-12-17T23:50:19.5105610Z + [[ -n '' ]] 2024-12-17T23:50:19.5105872Z + echo 'Environment variables' 2024-12-17T23:50:19.5106180Z Environment variables 2024-12-17T23:50:19.5106492Z + env 2024-12-17T23:50:19.5112201Z INSTALLED_DB=yes 2024-12-17T23:50:19.5112929Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-17T23:50:19.5113703Z CONTINUE_THROUGH_ERROR=False 2024-12-17T23:50:19.5114030Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10 2024-12-17T23:50:19.5114383Z HOSTNAME=6707fe6f6119 2024-12-17T23:50:19.5115128Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.5116003Z GITHUB_ACTION=__self 2024-12-17T23:50:19.5116303Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2024-12-17T23:50:19.5116609Z GITHUB_RUN_NUMBER=276594 2024-12-17T23:50:19.5116895Z TEST_CONFIG=dynamo_wrapped 2024-12-17T23:50:19.5117196Z GITHUB_REPOSITORY_OWNER_ID=21003710 2024-12-17T23:50:19.5117537Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2024-12-17T23:50:19.5117862Z IS_A100_RUNNER=0 2024-12-17T23:50:19.5118330Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2024-12-17T23:50:19.5118644Z GITHUB_TRIGGERING_ACTOR=malfet 2024-12-17T23:50:19.5118941Z GITHUB_REF_TYPE=branch 2024-12-17T23:50:19.5119217Z TORCH_CUDA_ARCH_LIST=Maxwell 2024-12-17T23:50:19.5119552Z BASE_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.5119893Z XLA_CUDA= 2024-12-17T23:50:19.5120242Z HUGGING_FACE_HUB_TOKEN=*** 2024-12-17T23:50:19.5120798Z *** 2024-12-17T23:50:19.5121100Z GITHUB_REPOSITORY_ID=65600975 2024-12-17T23:50:19.5121513Z GITHUB_ACTIONS=true 2024-12-17T23:50:19.5122205Z SHA1=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.5122617Z GITHUB_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.5123186Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/release/2.6 2024-12-17T23:50:19.5123689Z UCC_HOME=/usr 2024-12-17T23:50:19.5123934Z VERBOSE_TEST_LOGS=False 2024-12-17T23:50:19.5124216Z GITHUB_REF=refs/heads/release/2.6 2024-12-17T23:50:19.5124508Z SHARD_NUMBER=1 2024-12-17T23:50:19.5124761Z GITHUB_REF_PROTECTED=true 2024-12-17T23:50:19.5125044Z HOME=/var/lib/jenkins 2024-12-17T23:50:19.5125345Z GITHUB_API_URL=https://api.github.com 2024-12-17T23:50:19.5125696Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2024-12-17T23:50:19.5125991Z UCX_COMMIT= 2024-12-17T23:50:19.5126229Z SCCACHE_S3_KEY_PREFIX=pull 2024-12-17T23:50:19.5126511Z NUM_TEST_SHARDS=3 2024-12-17T23:50:19.5126755Z UCX_HOME=/usr 2024-12-17T23:50:19.5127336Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.5128143Z JOB_NAME=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-17T23:50:19.5129091Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.5129919Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2024-12-17T23:50:19.5130431Z GITHUB_EVENT_NAME=push 2024-12-17T23:50:19.5130701Z DASHBOARD_TAG= 2024-12-17T23:50:19.5130955Z GITHUB_RUN_ID=12383255652 2024-12-17T23:50:19.5131590Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.5132277Z GITHUB_ACTOR=malfet 2024-12-17T23:50:19.5132527Z PR_NUMBER= 2024-12-17T23:50:19.5132759Z DESIRED_CUDA= 2024-12-17T23:50:19.5133000Z GITHUB_RUN_ATTEMPT=1 2024-12-17T23:50:19.5133247Z VALGRIND=ON 2024-12-17T23:50:19.5133487Z ANACONDA_PYTHON_VERSION=3.13 2024-12-17T23:50:19.5133843Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2024-12-17T23:50:19.5134205Z TERM=vt100 2024-12-17T23:50:19.5134435Z INSTALLED_VISION=yes 2024-12-17T23:50:19.5134687Z BRANCH=release/2.6 2024-12-17T23:50:19.5134944Z SCCACHE_REGION=us-east-1 2024-12-17T23:50:19.5135229Z OPENSSL_ROOT_DIR=/opt/openssl 2024-12-17T23:50:19.5135527Z CUDA_PATH=/usr/local/cuda 2024-12-17T23:50:19.5136059Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2024-12-17T23:50:19.5136638Z GITHUB_SERVER_URL=https://github.com 2024-12-17T23:50:19.5136954Z UCC_COMMIT= 2024-12-17T23:50:19.5137183Z REENABLED_ISSUES= 2024-12-17T23:50:19.5137428Z DOCS= 2024-12-17T23:50:19.5137645Z SHLVL=1 2024-12-17T23:50:19.5137851Z MAX_JOBS=6 2024-12-17T23:50:19.5138086Z GITHUB_ACTOR_ID=2453524 2024-12-17T23:50:19.5138445Z GITHUB_WORKFLOW_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-17T23:50:19.5138854Z GITHUB_REF_NAME=release/2.6 2024-12-17T23:50:19.5139268Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2024-12-17T23:50:19.5139724Z GITHUB_JOB=test 2024-12-17T23:50:19.5139970Z NO_TEST_TIMEOUT=False 2024-12-17T23:50:19.5140241Z TD_DISTRIBUTED=False 2024-12-17T23:50:19.5140522Z GITHUB_REPOSITORY=pytorch/pytorch 2024-12-17T23:50:19.5140823Z GITHUB_RETENTION_DAYS=90 2024-12-17T23:50:19.5141107Z OPENSSL_DIR=/opt/openssl 2024-12-17T23:50:19.5141390Z GITHUB_ACTION_REPOSITORY= 2024-12-17T23:50:19.5142184Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:50:19.5143022Z GITHUB_BASE_REF= 2024-12-17T23:50:19.5143357Z INSTALLED_ACL= 2024-12-17T23:50:19.5143729Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-17T23:50:19.5144182Z CI=true 2024-12-17T23:50:19.5144425Z GITHUB_REPOSITORY_OWNER=pytorch 2024-12-17T23:50:19.5144732Z JOB_ID=34566067022 2024-12-17T23:50:19.5144978Z INSTALLED_PROTOBUF=yes 2024-12-17T23:50:19.5145339Z GITHUB_HEAD_REF= 2024-12-17T23:50:19.5145593Z GITHUB_ACTION_REF= 2024-12-17T23:50:19.5145911Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2024-12-17T23:50:19.5146295Z TEST_SHOWLOCALS=False 2024-12-17T23:50:19.5146554Z GITHUB_WORKFLOW=pull 2024-12-17T23:50:19.5146843Z DEBIAN_FRONTEND=noninteractive 2024-12-17T23:50:19.5147488Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_7ef31eb4-2d78-42be-80fe-d0a45c851134 2024-12-17T23:50:19.5148145Z NO_TD=False 2024-12-17T23:50:19.5148394Z SKIP_SCCACHE_INITIALIZATION=1 2024-12-17T23:50:19.5148674Z _=/usr/bin/env 2024-12-17T23:50:19.5148919Z + echo 'Testing pytorch' 2024-12-17T23:50:19.5149191Z Testing pytorch 2024-12-17T23:50:19.5149448Z + export LANG=C.UTF-8 2024-12-17T23:50:19.5149706Z + LANG=C.UTF-8 2024-12-17T23:50:19.5196591Z + PR_NUMBER= 2024-12-17T23:50:19.5196977Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]] 2024-12-17T23:50:19.5197532Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]] 2024-12-17T23:50:19.5198213Z + [[ dynamo_wrapped == \s\l\o\w ]] 2024-12-17T23:50:19.5198787Z + [[ linux-focal-py3.13-clang10 == *slow-gradcheck* ]] 2024-12-17T23:50:19.5199597Z + [[ linux-focal-py3.13-clang10 == *cuda* ]] 2024-12-17T23:50:19.5200159Z + [[ linux-focal-py3.13-clang10 == *rocm* ]] 2024-12-17T23:50:19.5200721Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2024-12-17T23:50:19.5201273Z + [[ dynamo_wrapped == *crossref* ]] 2024-12-17T23:50:19.5201960Z + [[ linux-focal-py3.13-clang10 == *rocm* ]] 2024-12-17T23:50:19.5202328Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2024-12-17T23:50:19.5202698Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]] 2024-12-17T23:50:19.5203064Z + pip_install --user ninja==1.10.2 2024-12-17T23:50:19.5203476Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:50:19.5203980Z + python3 -m pip install --progress-bar off --user ninja==1.10.2 2024-12-17T23:50:19.9679853Z Collecting ninja==1.10.2 2024-12-17T23:50:19.9948978Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2024-12-17T23:50:20.0127742Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2024-12-17T23:50:20.1882362Z Installing collected packages: ninja 2024-12-17T23:50:20.1960029Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2024-12-17T23:50:20.1961014Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2024-12-17T23:50:20.2029408Z Successfully installed ninja-1.10.2 2024-12-17T23:50:20.2764693Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:50:20.2766312Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:50:20.2767321Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]] 2024-12-17T23:50:20.2767678Z + install_tlparse 2024-12-17T23:50:20.2767979Z + pip_install --user tlparse==0.3.25 2024-12-17T23:50:20.2768399Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:50:20.2768915Z + python3 -m pip install --progress-bar off --user tlparse==0.3.25 2024-12-17T23:50:20.6562515Z Collecting tlparse==0.3.25 2024-12-17T23:50:20.6843296Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB) 2024-12-17T23:50:20.6987561Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB) 2024-12-17T23:50:20.8774303Z Installing collected packages: tlparse 2024-12-17T23:50:20.9127669Z Successfully installed tlparse-0.3.25 2024-12-17T23:50:20.9865381Z ++ python -m site --user-base 2024-12-17T23:50:21.0010987Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2024-12-17T23:50:21.0012741Z + [[ linux-focal-py3.13-clang10 == *asan* ]] 2024-12-17T23:50:21.0013248Z + [[ linux-focal-py3.13-clang10 == *-debug* ]] 2024-12-17T23:50:21.0013639Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]] 2024-12-17T23:50:21.0014179Z + echo 'We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass' 2024-12-17T23:50:21.0014843Z We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass 2024-12-17T23:50:21.0016564Z + cd test 2024-12-17T23:50:21.0016997Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2024-12-17T23:50:22.6769889Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2024-12-17T23:50:22.6770347Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2024-12-17T23:50:22.6774651Z + DYNAMO_BENCHMARK_FLAGS=() 2024-12-17T23:50:22.6776171Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]] 2024-12-17T23:50:22.6776648Z + [[ dynamo_wrapped == *dynamo_eager* ]] 2024-12-17T23:50:22.6776999Z + [[ dynamo_wrapped == *aot_eager* ]] 2024-12-17T23:50:22.6777597Z + [[ dynamo_wrapped == *aot_inductor* ]] 2024-12-17T23:50:22.6777943Z + [[ dynamo_wrapped == *inductor* ]] 2024-12-17T23:50:22.6778272Z + [[ dynamo_wrapped == *dynamic* ]] 2024-12-17T23:50:22.6778581Z + [[ dynamo_wrapped == *cpu* ]] 2024-12-17T23:50:22.6778923Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2024-12-17T23:50:22.6807650Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]] 2024-12-17T23:50:22.6808101Z + [[ linux-focal-py3.13-clang10 == *-bazel-* ]] 2024-12-17T23:50:22.6811251Z + cd test 2024-12-17T23:50:22.6811623Z + python -c 'import torch; print(torch.__config__.show())' 2024-12-17T23:50:23.8292601Z PyTorch built with: 2024-12-17T23:50:23.8293103Z - GCC 4.2 2024-12-17T23:50:23.8293348Z - C++ Version: 201703 2024-12-17T23:50:23.8293620Z - clang 10.0.0 2024-12-17T23:50:23.8294171Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-12-17T23:50:23.8294940Z - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2024-12-17T23:50:23.8295419Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-12-17T23:50:23.8295779Z - LAPACK is enabled (usually provided by MKL) 2024-12-17T23:50:23.8296135Z - NNPACK is enabled 2024-12-17T23:50:23.8296401Z - CPU capability usage: AVX512 2024-12-17T23:50:23.8302094Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=0cdf8b1d09254cfda66191d1bd01e3041c3c76f7, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Wno-missing-braces -Qunused-arguments -fcolor-diagnostics -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.6.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 2024-12-17T23:50:23.8307636Z 2024-12-17T23:50:24.0664090Z + cd test 2024-12-17T23:50:24.0664503Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2024-12-17T23:50:25.2070572Z ATen/Parallel: 2024-12-17T23:50:25.2071080Z at::get_num_threads() : 4 2024-12-17T23:50:25.2071758Z at::get_num_interop_threads() : 4 2024-12-17T23:50:25.2072092Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2024-12-17T23:50:25.2072425Z omp_get_max_threads() : 4 2024-12-17T23:50:25.2073006Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2024-12-17T23:50:25.2073609Z mkl_get_max_threads() : 4 2024-12-17T23:50:25.2074015Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2024-12-17T23:50:25.2074470Z std::thread::hardware_concurrency() : 8 2024-12-17T23:50:25.2074799Z Environment variables: 2024-12-17T23:50:25.2075072Z OMP_NUM_THREADS : [not set] 2024-12-17T23:50:25.2075366Z MKL_NUM_THREADS : [not set] 2024-12-17T23:50:25.2075782Z ATen parallel backend: OpenMP 2024-12-17T23:50:25.2075979Z 2024-12-17T23:50:25.4535287Z + [[ dynamo_wrapped == *numpy_2* ]] 2024-12-17T23:50:25.4535753Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]] 2024-12-17T23:50:25.4536126Z + [[ dynamo_wrapped == *backward* ]] 2024-12-17T23:50:25.4536463Z + [[ dynamo_wrapped == *xla* ]] 2024-12-17T23:50:25.4536790Z + [[ dynamo_wrapped == *executorch* ]] 2024-12-17T23:50:25.4537404Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]] 2024-12-17T23:50:25.4537784Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]] 2024-12-17T23:50:25.4538152Z + [[ dynamo_wrapped == distributed ]] 2024-12-17T23:50:25.4538560Z + [[ dynamo_wrapped == *inductor_distributed* ]] 2024-12-17T23:50:25.4539020Z + [[ dynamo_wrapped == *inductor-halide* ]] 2024-12-17T23:50:25.4539485Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]] 2024-12-17T23:50:25.4539939Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]] 2024-12-17T23:50:25.4540381Z + [[ dynamo_wrapped == *huggingface* ]] 2024-12-17T23:50:25.4540709Z + [[ dynamo_wrapped == *timm* ]] 2024-12-17T23:50:25.4541008Z + [[ dynamo_wrapped == *torchbench* ]] 2024-12-17T23:50:25.4541360Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]] 2024-12-17T23:50:25.4541716Z + [[ dynamo_wrapped == *inductor* ]] 2024-12-17T23:50:25.4542052Z + [[ dynamo_wrapped == *dynamo_wrapped* ]] 2024-12-17T23:50:25.4542391Z + install_torchvision 2024-12-17T23:50:25.4542642Z + local orig_preload 2024-12-17T23:50:25.4543010Z + local commit 2024-12-17T23:50:25.4543270Z ++ get_pinned_commit vision 2024-12-17T23:50:25.4543580Z ++ cat .github/ci_commit_pins/vision.txt 2024-12-17T23:50:25.4567907Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:50:25.4568568Z + orig_preload= 2024-12-17T23:50:25.4568820Z + '[' -n '' ']' 2024-12-17T23:50:25.4569416Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:50:25.4570136Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2024-12-17T23:50:25.4570958Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:50:25.7960149Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:50:25.7965012Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-gb17nwb1 2024-12-17T23:50:25.7984171Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-gb17nwb1 2024-12-17T23:50:27.2253282Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2024-12-17T23:50:27.2271484Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:50:28.5779148Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:50:28.8948767Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2024-12-17T23:50:30.9882185Z Preparing metadata (setup.py) ... [?25l- \ done 2024-12-17T23:50:30.9915972Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.1.2) 2024-12-17T23:50:30.9919643Z Requirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.6.0a0+git0cdf8b1) 2024-12-17T23:50:30.9923378Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0) 2024-12-17T23:50:30.9988655Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1) 2024-12-17T23:50:30.9992077Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2024-12-17T23:50:30.9994861Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2024-12-17T23:50:30.9997835Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4) 2024-12-17T23:50:31.0001604Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0) 2024-12-17T23:50:31.0012028Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (75.1.0) 2024-12-17T23:50:31.0016526Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1) 2024-12-17T23:50:31.0030095Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy==1.13.1->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2024-12-17T23:50:31.0130501Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2) 2024-12-17T23:50:31.0225982Z Building wheels for collected packages: torchvision 2024-12-17T23:51:35.4386945Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2024-12-17T23:51:35.4423578Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp313-cp313-linux_x86_64.whl size=1117900 sha256=61d7c96702f1db43db1103acddeb9bae6b90ac920c378c5270f548be2b4d3263 2024-12-17T23:51:35.4425676Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/81/60/73/f2acb628a45eebe28dd9ff5468e774a0d5e194728570f8ff6f 2024-12-17T23:51:35.4459743Z Successfully built torchvision 2024-12-17T23:51:35.5735874Z Installing collected packages: torchvision 2024-12-17T23:51:36.0183574Z Successfully installed torchvision-0.19.0a0+d23a6e1 2024-12-17T23:51:36.1225152Z + '[' -n '' ']' 2024-12-17T23:51:36.1225609Z + test_dynamo_wrapped_shard 1 2024-12-17T23:51:36.1226076Z + [[ -z 3 ]] 2024-12-17T23:51:36.1226347Z + python tools/dynamo/verify_dynamo.py 2024-12-17T23:51:37.2700729Z Python version: 3.13.0 2024-12-17T23:51:37.2701207Z `torch` version: 2.6.0a0+git0cdf8b1 2024-12-17T23:51:37.2701586Z CUDA version: None 2024-12-17T23:51:37.2702075Z ROCM version: None 2024-12-17T23:51:37.2702368Z 2024-12-17T23:51:37.2703052Z /var/lib/jenkins/workspace/tools/dynamo/verify_dynamo.py:220: UserWarning: Dynamo not yet supported in Python 3.13. Skipping check. 2024-12-17T23:51:37.2703933Z warnings.warn("Dynamo not yet supported in Python 3.13. Skipping check.") 2024-12-17T23:51:37.2704402Z All required checks passed 2024-12-17T23:51:37.5026326Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --shard 1 3 --verbose --upload-artifacts-while-running 2024-12-17T23:51:37.6075497Z /var/lib/jenkins/workspace/test/run_test.py:22: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-12-17T23:51:37.6089114Z import pkg_resources 2024-12-17T23:51:41.6564863Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-17T23:51:41.7036571Z Ignoring disabled issues: [''] 2024-12-17T23:51:41.7195875Z Found test times from artifacts 2024-12-17T23:51:41.7806765Z Found test times from artifacts 2024-12-17T23:51:41.7828931Z Running all tests 2024-12-17T23:51:41.7945620Z Running parallel tests on 3 processes 2024-12-17T23:51:41.7949507Z Name: tests to run (est. time: 68.25min) 2024-12-17T23:51:41.7950163Z Serial tests (27): 2024-12-17T23:51:41.7950644Z test_reductions 1/1 2024-12-17T23:51:41.7951150Z test_cuda_nvml_based_avail 1/1 2024-12-17T23:51:41.7951717Z test_cuda_primary_ctx 1/1 2024-12-17T23:51:41.7952289Z test_cpp_extensions_aot_ninja 1/1 2024-12-17T23:51:41.7952916Z test_spectral_ops 1/1 2024-12-17T23:51:41.7953461Z test_cpp_extensions_aot_no_ninja 1/1 2024-12-17T23:51:41.7954071Z test_show_pickle 1/1 2024-12-17T23:51:41.7954929Z test_namedtuple_return_api 1/1 2024-12-17T23:51:41.7955373Z test_jit_disabled 1/1 2024-12-17T23:51:41.7955856Z test_autocast 1/1 2024-12-17T23:51:41.7956231Z test_torch 1/1 2024-12-17T23:51:41.7956629Z test_multiprocessing 1/1 2024-12-17T23:51:41.7957062Z test_native_mha 1/1 2024-12-17T23:51:41.7957477Z test_sort_and_select 1/1 2024-12-17T23:51:41.7957915Z nn/test_pooling 1/1 2024-12-17T23:51:41.7958256Z test_python_dispatch 1/1 2024-12-17T23:51:41.7958547Z test_mobile_optimizer 1/1 2024-12-17T23:51:41.7958844Z nn/test_convolution 1/1 2024-12-17T23:51:41.7959128Z test_nn 1/2 2024-12-17T23:51:41.7959370Z test_nn 2/2 2024-12-17T23:51:41.7959629Z test_multiprocessing_spawn 1/1 2024-12-17T23:51:41.7959936Z test_overrides 1/1 2024-12-17T23:51:41.7960223Z distributions/test_distributions 1/2 2024-12-17T23:51:41.7960605Z distributions/test_distributions 2/2 2024-12-17T23:51:41.7960945Z test_autoload_disable 1/1 2024-12-17T23:51:41.7961242Z doctests 1/1 2024-12-17T23:51:41.7961487Z test_autoload_enable 1/1 2024-12-17T23:51:41.7961778Z Parallel tests (31): 2024-12-17T23:51:41.7962065Z test_cuda_expandable_segments 1/1 2024-12-17T23:51:41.7962403Z dynamo/test_higher_order_ops 1/1 2024-12-17T23:51:41.7962719Z dynamo/test_misc 1/1 2024-12-17T23:51:41.7962982Z dynamo/test_frame_init 1/1 2024-12-17T23:51:41.7963273Z dynamo/test_nops 1/1 2024-12-17T23:51:41.7963568Z dynamo/test_fx_passes_pre_grad 1/1 2024-12-17T23:51:41.7963899Z dynamo/test_skip_non_tensor 1/1 2024-12-17T23:51:41.7964221Z dynamo/test_reconstruct 1/1 2024-12-17T23:51:41.7964506Z dynamo/test_sdpa 1/1 2024-12-17T23:51:41.7964788Z dynamo/test_recompiles 1/1 2024-12-17T23:51:41.7965098Z dynamo/test_pre_dispatch 1/1 2024-12-17T23:51:41.7965468Z dynamo/test_cudagraphs 1/1 2024-12-17T23:51:41.7965926Z dynamo/test_graph_region_tracker 1/1 2024-12-17T23:51:41.7966423Z dynamo/test_deviceguard 1/1 2024-12-17T23:51:41.7966887Z dynamo/test_sources 1/1 2024-12-17T23:51:41.7967322Z dynamo/test_structured_trace 1/1 2024-12-17T23:51:41.7967802Z dynamo/test_modes 1/1 2024-12-17T23:51:41.7968234Z dynamo/test_graph_deduplication 1/1 2024-12-17T23:51:41.7968761Z dynamo/test_ctx_manager 1/1 2024-12-17T23:51:41.7969261Z dynamo/test_activation_checkpointing 1/1 2024-12-17T23:51:41.7969810Z dynamo/test_trace_rules 1/1 2024-12-17T23:51:41.7970246Z dynamo/test_debug_utils 1/1 2024-12-17T23:51:41.7970741Z dynamo/test_bytecode_utils 1/1 2024-12-17T23:51:41.7971262Z dynamo/test_recompile_ux 1/1 2024-12-17T23:51:41.7971785Z dynamo/test_minifier 1/1 2024-12-17T23:51:41.7972308Z dynamo/test_comptime 1/1 2024-12-17T23:51:41.7972722Z test_hub 1/1 2024-12-17T23:51:41.7973071Z optim/test_swa_utils 1/1 2024-12-17T23:51:41.7973511Z test_quantization 1/4 2024-12-17T23:51:41.7974250Z profiler/test_record_function 1/1 2024-12-17T23:51:41.7974703Z profiler/test_execution_trace 1/1 2024-12-17T23:51:41.7975058Z Name: excluded (est. time: 0.0min) 2024-12-17T23:51:41.7975351Z Serial tests (0): 2024-12-17T23:51:41.7975615Z Parallel tests (0): 2024-12-17T23:51:41.8074206Z Running test_reductions 1/1 ... [2024-12-17 23:51:41.807110] 2024-12-17T23:51:41.8075004Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-17T23:51:41.8079508Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-17 23:51:41.807557] 2024-12-18T00:40:05.4062939Z 2024-12-18T00:40:05.4063954Z PRINTING LOG FILE of test_reductions 1/1 (test/test-reports/test_reductions_1.1_7af9e76d08b9736e_.log) 2024-12-18T00:40:05.4065519Z Test results will be stored in test-reports/python-pytest/test_reductions/test_reductions-cb40866da58c9eaf.xml 2024-12-18T00:40:05.4066730Z ============================= test session starts ============================== 2024-12-18T00:40:05.4068065Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:40:05.4069021Z cachedir: .pytest_cache 2024-12-18T00:40:05.4070150Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:40:05.4071409Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:40:05.4071952Z configfile: pytest.ini 2024-12-18T00:40:05.4072908Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:40:05.4074075Z collecting ... collected 4625 items 2024-12-18T00:40:05.4074652Z stepcurrent: Cannot find last run test, not skipping 2024-12-18T00:40:05.5734140Z Running 4625 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_amin_amax_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_axis_with_dim_one_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_large_axis_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_bincount_cpu, test/test_reductions.py::TestReductionsCPU::test_bucketization_cpu, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_cumprod_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_cumsum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_lastdim_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_lastdim_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_less_than_64_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_all_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_any_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_var_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_all_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_any_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_var_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_histc_cpu, test/test_reductions.py::TestReductionsCPU::test_histc_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_histc_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_histogram_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_histogram_error_handling_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_histogramdd_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_invalid_0dim_aminmax_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_invalid_0dim_aminmax_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_logcumsumexp_complex_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_logcumsumexp_complex_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_logsumexp_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_logsumexp_integral_promotion_cpu, test/test_reductions.py::TestReductionsCPU::test_max_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_max_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_max_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_max_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_max_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_max_elementwise_cpu, test/test_reductions.py::TestReductionsCPU::test_max_mixed_devices_cpu, test/test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_mean_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_mean_int_with_optdtype_cpu, test/test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_median_corner_cases_cpu, test/test_reductions.py::TestReductionsCPU::test_median_nan_values_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_median_nan_values_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_min_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_min_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_min_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_min_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_min_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_min_elementwise_cpu, test/test_reductions.py::TestReductionsCPU::test_min_max_nan_cpu, test/test_reductions.py::TestReductionsCPU::test_min_mixed_devices_cpu, test/test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_minmax_illegal_dtype_cpu, test/test_reductions.py::TestReductionsCPU::test_mode_boolean_cpu, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_mode_large_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_mode_wrong_device_cpu, test/test_reductions.py::TestReductionsCPU::test_mode_wrong_dtype_cpu, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_nansum_complex_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_nansum_complex_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_numpy_named_args_cpu, test/test_reductions.py::TestReductionsCPU::test_prod_bool_cpu, test/test_reductions.py::TestReductionsCPU::test_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_prod_gpu_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_prod_gpu_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_prod_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_prod_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_prod_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_quantile_backward_cpu, test/test_reductions.py::TestReductionsCPU::test_quantile_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_quantile_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_quantile_error_cpu, test/test_reductions.py::TestReductionsCPU::test_reduce_dtype_cpu, test/test_reductions.py::TestReductionsCPU::test_reduction_empty_any_all_cpu, test/test_reductions.py::TestReductionsCPU::test_reduction_split_cpu, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reductions_large_half_tensors_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reductions_large_half_tensors_cpu_complex32, test/test_reductions.py::TestReductionsCPU::test_reductions_large_half_tensors_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_repeated_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2024-12-18T00:40:05.7363036Z 2024-12-18T00:40:05.7363850Z test_reductions.py::TestReductionsCPU::test_accreal_type_cpu W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] Graph break from `Tensor.item()`, consider setting: 2024-12-18T00:40:05.7365116Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] torch._dynamo.config.capture_scalar_outputs = True 2024-12-18T00:40:05.7365946Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] or: 2024-12-18T00:40:05.7366741Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1 2024-12-18T00:40:05.7368018Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] to include these operations in the captured graph. 2024-12-18T00:40:05.7368830Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] 2024-12-18T00:40:05.7369572Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] Graph break: from user code at: 2024-12-18T00:40:05.7370680Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] File "/var/lib/jenkins/workspace/test/test_reductions.py", line 1104, in test_accreal_type 2024-12-18T00:40:05.7371867Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] self.assertIsInstance(x.double().sum().item(), float) 2024-12-18T00:40:05.7372689Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] 2024-12-18T00:40:05.7373314Z W1217 23:51:46.763000 904 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] 2024-12-18T00:40:05.7373778Z PASSED [0.7521s] [ 0%] 2024-12-18T00:40:05.7374219Z test_reductions.py::TestReductionsCPU::test_all_any_cpu PASSED [0.8616s] [ 0%] 2024-12-18T00:40:05.7374880Z test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu PASSED [0.0919s] [ 0%] 2024-12-18T00:40:05.7375949Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool W1217 23:51:51.043000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7377272Z W1217 23:51:51.043000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7378398Z W1217 23:51:51.043000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7379450Z W1217 23:51:51.043000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7380684Z W1217 23:51:51.043000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7381834Z W1217 23:51:51.490000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7382928Z W1217 23:51:51.490000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7384091Z W1217 23:51:51.490000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7385145Z W1217 23:51:51.490000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7386532Z W1217 23:51:51.490000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7387775Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] failed while attempting to run meta for aten.resize_.default 2024-12-18T00:40:05.7388749Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] Traceback (most recent call last): 2024-12-18T00:40:05.7389984Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:05.7391308Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] r = func(*args, **kwargs) 2024-12-18T00:40:05.7392463Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:05.7393582Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] return self._op(*args, **kwargs) 2024-12-18T00:40:05.7394455Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:05.7395439Z E1217 23:51:51.802000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] RuntimeError: tried to directly modify sizes for customized tensor 2024-12-18T00:40:05.7396823Z SKIPPED [3.7222s] (Failed running call_function (*(FakeTensor(..., size=(1, s0, 1), dtype=torch.bool), 1), **{'out': FakeTensor(..., size=(1, s0, 1), dtype=torch.bool)}): 2024-12-18T00:40:05.7397789Z tried to directly modify sizes for customized tensor 2024-12-18T00:40:05.7398217Z 2024-12-18T00:40:05.7398333Z from user code: 2024-12-18T00:40:05.7398797Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 1921, in _test_out_variant 2024-12-18T00:40:05.7399343Z torch.all(x, dim, out=out) 2024-12-18T00:40:05.7399545Z 2024-12-18T00:40:05.7399769Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:05.7400110Z 2024-12-18T00:40:05.7400115Z 2024-12-18T00:40:05.7400323Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:05.7400752Z import torch._dynamo 2024-12-18T00:40:05.7401077Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:05.7401324Z 2024-12-18T00:40:05.7401327Z 2024-12-18T00:40:05.7401528Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:05.7402189Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_all_any_vs_numpy_cpu_bool 2024-12-18T00:40:05.7402685Z 2024-12-18T00:40:05.7402951Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 0%] 2024-12-18T00:40:05.7404051Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128 W1217 23:51:55.193000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7405405Z W1217 23:51:55.193000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7406534Z W1217 23:51:55.193000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7407583Z W1217 23:51:55.193000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7408807Z W1217 23:51:55.193000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7410117Z W1217 23:51:55.962000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7411214Z W1217 23:51:55.962000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7412380Z W1217 23:51:55.962000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] last reason: 14/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7413435Z W1217 23:51:55.962000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7414643Z W1217 23:51:55.962000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7415912Z W1217 23:51:56.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7417568Z W1217 23:51:56.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '_generate_input' (/var/lib/jenkins/workspace/test/test_reductions.py:32) 2024-12-18T00:40:05.7418713Z W1217 23:51:56.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: not L['shape'] 2024-12-18T00:40:05.7419750Z W1217 23:51:56.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7420964Z W1217 23:51:56.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7421827Z PASSED [4.9000s] [ 0%] 2024-12-18T00:40:05.7422712Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64 W1217 23:51:59.786000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7424058Z W1217 23:51:59.786000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7425171Z W1217 23:51:59.786000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7426215Z W1217 23:51:59.786000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7427425Z W1217 23:51:59.786000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7428587Z W1217 23:52:00.538000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7429686Z W1217 23:52:00.538000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7430853Z W1217 23:52:00.538000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] last reason: 14/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7431906Z W1217 23:52:00.538000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7433129Z W1217 23:52:00.538000 904 site-packages/torch/_dynamo/convert_frame.py:906] [14/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7434292Z W1217 23:52:00.797000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7435413Z W1217 23:52:00.797000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '_generate_input' (/var/lib/jenkins/workspace/test/test_reductions.py:32) 2024-12-18T00:40:05.7436611Z W1217 23:52:00.797000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: not L['shape'] 2024-12-18T00:40:05.7437651Z W1217 23:52:00.797000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7438865Z W1217 23:52:00.797000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7439721Z PASSED [4.5540s] [ 0%] 2024-12-18T00:40:05.7440570Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16 W1217 23:52:04.231000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7441964Z W1217 23:52:04.231000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7443092Z W1217 23:52:04.231000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7444127Z W1217 23:52:04.231000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7445339Z W1217 23:52:04.231000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7446525Z W1217 23:52:04.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7447627Z W1217 23:52:04.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7448797Z W1217 23:52:04.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7449844Z W1217 23:52:04.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7451064Z W1217 23:52:04.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7452222Z W1217 23:52:05.118000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7453287Z W1217 23:52:05.118000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '_generate_input' (/var/lib/jenkins/workspace/test/test_reductions.py:32) 2024-12-18T00:40:05.7454435Z W1217 23:52:05.118000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: not L['shape'] 2024-12-18T00:40:05.7455472Z W1217 23:52:05.118000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7456682Z W1217 23:52:05.118000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7457540Z PASSED [4.3108s] [ 0%] 2024-12-18T00:40:05.7460882Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32 W1217 23:52:08.749000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7463452Z W1217 23:52:08.749000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7464725Z W1217 23:52:08.749000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7465776Z W1217 23:52:08.749000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7466977Z W1217 23:52:08.749000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7468143Z W1217 23:52:09.380000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7469241Z W1217 23:52:09.380000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7470467Z W1217 23:52:09.380000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7471521Z W1217 23:52:09.380000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7472737Z W1217 23:52:09.380000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7473904Z W1217 23:52:09.637000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7474978Z W1217 23:52:09.637000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '_generate_input' (/var/lib/jenkins/workspace/test/test_reductions.py:32) 2024-12-18T00:40:05.7476225Z W1217 23:52:09.637000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: not L['shape'] 2024-12-18T00:40:05.7477267Z W1217 23:52:09.637000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7478485Z W1217 23:52:09.637000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7479341Z PASSED [4.5208s] [ 0%] 2024-12-18T00:40:05.7480197Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64 W1217 23:52:12.970000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7481524Z W1217 23:52:12.970000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7482650Z W1217 23:52:12.970000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7483689Z W1217 23:52:12.970000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7484905Z W1217 23:52:12.970000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7486065Z W1217 23:52:13.587000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7487148Z W1217 23:52:13.587000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7488310Z W1217 23:52:13.587000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7489448Z W1217 23:52:13.587000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7490666Z W1217 23:52:13.587000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7491933Z W1217 23:52:13.848000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7492991Z W1217 23:52:13.848000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '_generate_input' (/var/lib/jenkins/workspace/test/test_reductions.py:32) 2024-12-18T00:40:05.7494116Z W1217 23:52:13.848000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: not L['shape'] 2024-12-18T00:40:05.7495215Z W1217 23:52:13.848000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7496427Z W1217 23:52:13.848000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7497262Z PASSED [4.2187s] [ 0%] 2024-12-18T00:40:05.7498267Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16 W1217 23:52:17.076000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7499587Z W1217 23:52:17.076000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7500704Z W1217 23:52:17.076000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7501759Z W1217 23:52:17.076000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7502973Z W1217 23:52:17.076000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7504129Z W1217 23:52:17.562000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7505223Z W1217 23:52:17.562000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7506369Z W1217 23:52:17.562000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7507419Z W1217 23:52:17.562000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7508657Z W1217 23:52:17.562000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7509516Z PASSED [3.8832s] [ 0%] 2024-12-18T00:40:05.7510360Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32 W1217 23:52:20.994000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7511675Z W1217 23:52:20.994000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7512794Z W1217 23:52:20.994000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7513957Z W1217 23:52:20.994000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7515171Z W1217 23:52:20.994000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7516397Z W1217 23:52:21.476000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7517493Z W1217 23:52:21.476000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7518660Z W1217 23:52:21.476000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7519714Z W1217 23:52:21.476000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7521018Z W1217 23:52:21.476000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7521881Z PASSED [3.9117s] [ 0%] 2024-12-18T00:40:05.7522722Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64 W1217 23:52:25.300000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7524049Z W1217 23:52:25.300000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7525154Z W1217 23:52:25.300000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7526204Z W1217 23:52:25.300000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7527427Z W1217 23:52:25.300000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7528584Z W1217 23:52:25.804000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7529671Z W1217 23:52:25.804000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7530831Z W1217 23:52:25.804000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7531877Z W1217 23:52:25.804000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7533104Z W1217 23:52:25.804000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7533968Z PASSED [4.3417s] [ 0%] 2024-12-18T00:40:05.7534789Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8 W1217 23:52:29.268000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7536104Z W1217 23:52:29.268000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7537226Z W1217 23:52:29.268000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7538264Z W1217 23:52:29.268000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7539543Z W1217 23:52:29.268000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7540705Z W1217 23:52:29.757000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7541802Z W1217 23:52:29.757000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7542962Z W1217 23:52:29.757000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7544004Z W1217 23:52:29.757000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7545266Z W1217 23:52:29.757000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7546127Z PASSED [3.9327s] [ 0%] 2024-12-18T00:40:05.7546965Z test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8 W1217 23:52:33.372000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7548287Z W1217 23:52:33.372000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: '_test_all_any' (/var/lib/jenkins/workspace/test/test_reductions.py:1904) 2024-12-18T00:40:05.7549403Z W1217 23:52:33.372000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7550440Z W1217 23:52:33.372000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7551663Z W1217 23:52:33.372000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7552821Z W1217 23:52:33.864000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7553906Z W1217 23:52:33.864000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] function: '_test_all_any_with_dim' (/var/lib/jenkins/workspace/test/test_reductions.py:1908) 2024-12-18T00:40:05.7555076Z W1217 23:52:33.864000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] last reason: 11/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7556197Z W1217 23:52:33.864000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7557421Z W1217 23:52:33.864000 904 site-packages/torch/_dynamo/convert_frame.py:906] [11/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7558668Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] failed while attempting to run meta for aten.resize_.default 2024-12-18T00:40:05.7559643Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] Traceback (most recent call last): 2024-12-18T00:40:05.7560872Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:05.7562074Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] r = func(*args, **kwargs) 2024-12-18T00:40:05.7563159Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:05.7564318Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] return self._op(*args, **kwargs) 2024-12-18T00:40:05.7565186Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:05.7566169Z E1217 23:52:34.216000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [14/4] RuntimeError: tried to directly modify sizes for customized tensor 2024-12-18T00:40:05.7567496Z SKIPPED [3.8015s] (Failed running call_function (*(FakeTensor(..., size=(1, s0, 1), dtype=torch.uint8), 1), **{'out': FakeTensor(..., size=(1, s0, 1), dtype=torch.uint8)}): 2024-12-18T00:40:05.7568492Z tried to directly modify sizes for customized tensor 2024-12-18T00:40:05.7568771Z 2024-12-18T00:40:05.7568865Z from user code: 2024-12-18T00:40:05.7569335Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 1921, in _test_out_variant 2024-12-18T00:40:05.7569881Z torch.all(x, dim, out=out) 2024-12-18T00:40:05.7570072Z 2024-12-18T00:40:05.7570360Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:05.7570703Z 2024-12-18T00:40:05.7570707Z 2024-12-18T00:40:05.7570915Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:05.7571342Z import torch._dynamo 2024-12-18T00:40:05.7571663Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:05.7571913Z 2024-12-18T00:40:05.7571917Z 2024-12-18T00:40:05.7572126Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:05.7572805Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_all_any_vs_numpy_cpu_uint8 2024-12-18T00:40:05.7573288Z 2024-12-18T00:40:05.7573569Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 0%] 2024-12-18T00:40:05.7574236Z test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu PASSED [0.2437s] [ 0%] 2024-12-18T00:40:05.7574936Z test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu PASSED [0.0690s] [ 0%] 2024-12-18T00:40:05.7575605Z test_reductions.py::TestReductionsCPU::test_amax_cpu_bool PASSED [0.4215s] [ 0%] 2024-12-18T00:40:05.7576261Z test_reductions.py::TestReductionsCPU::test_amax_cpu_float16 PASSED [0.4182s] [ 0%] 2024-12-18T00:40:05.7576931Z test_reductions.py::TestReductionsCPU::test_amax_cpu_float32 PASSED [0.4064s] [ 0%] 2024-12-18T00:40:05.7577604Z test_reductions.py::TestReductionsCPU::test_amax_cpu_float64 PASSED [0.4070s] [ 0%] 2024-12-18T00:40:05.7578253Z test_reductions.py::TestReductionsCPU::test_amax_cpu_int32 PASSED [0.3819s] [ 0%] 2024-12-18T00:40:05.7578909Z test_reductions.py::TestReductionsCPU::test_amax_cpu_int64 PASSED [0.3767s] [ 0%] 2024-12-18T00:40:05.7579595Z test_reductions.py::TestReductionsCPU::test_amin_amax_some_dims_cpu PASSED [0.0352s] [ 0%] 2024-12-18T00:40:05.7580281Z test_reductions.py::TestReductionsCPU::test_amin_cpu_bool PASSED [0.3742s] [ 0%] 2024-12-18T00:40:05.7580939Z test_reductions.py::TestReductionsCPU::test_amin_cpu_float16 PASSED [0.3959s] [ 0%] 2024-12-18T00:40:05.7581738Z test_reductions.py::TestReductionsCPU::test_amin_cpu_float32 PASSED [0.3894s] [ 0%] 2024-12-18T00:40:05.7582394Z test_reductions.py::TestReductionsCPU::test_amin_cpu_float64 PASSED [0.3888s] [ 0%] 2024-12-18T00:40:05.7583056Z test_reductions.py::TestReductionsCPU::test_amin_cpu_int32 PASSED [0.3660s] [ 0%] 2024-12-18T00:40:05.7583703Z test_reductions.py::TestReductionsCPU::test_amin_cpu_int64 PASSED [0.3705s] [ 0%] 2024-12-18T00:40:05.7584382Z test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16 PASSED [1.6153s] [ 0%] 2024-12-18T00:40:05.7585077Z test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float16 PASSED [1.1919s] [ 0%] 2024-12-18T00:40:05.7585762Z test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float32 PASSED [1.1449s] [ 0%] 2024-12-18T00:40:05.7586467Z test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float64 PASSED [1.1311s] [ 0%] 2024-12-18T00:40:05.7587282Z test_reductions.py::TestReductionsCPU::test_argminmax_axis_with_dim_one_cpu PASSED [0.5071s] [ 0%] 2024-12-18T00:40:05.7588118Z test_reductions.py::TestReductionsCPU::test_argminmax_large_axis_cpu SKIPPED [0.0162s] (Only runs on cuda) [ 0%] 2024-12-18T00:40:05.7588959Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float16 PASSED [0.8092s] [ 0%] 2024-12-18T00:40:05.7590127Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32 W1217 23:52:46.847000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7591565Z W1217 23:52:46.847000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1856) 2024-12-18T00:40:05.7592721Z W1217 23:52:46.847000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7593864Z W1217 23:52:46.847000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7595092Z W1217 23:52:46.847000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7596318Z W1217 23:52:46.852000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7597365Z W1217 23:52:46.852000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1855) 2024-12-18T00:40:05.7598638Z W1217 23:52:46.852000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7599682Z W1217 23:52:46.852000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7600906Z W1217 23:52:46.852000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7602069Z W1217 23:52:46.857000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7603118Z W1217 23:52:46.857000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1858) 2024-12-18T00:40:05.7604258Z W1217 23:52:46.857000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7605341Z W1217 23:52:46.857000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7606562Z W1217 23:52:46.857000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7607718Z W1217 23:52:46.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7608761Z W1217 23:52:46.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1857) 2024-12-18T00:40:05.7609872Z W1217 23:52:46.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7610915Z W1217 23:52:46.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7612124Z W1217 23:52:46.861000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7613094Z PASSED [1.8939s] [ 0%] 2024-12-18T00:40:05.7613960Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float64 W1217 23:52:48.726000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7615307Z W1217 23:52:48.726000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1856) 2024-12-18T00:40:05.7616461Z W1217 23:52:48.726000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7617547Z W1217 23:52:48.726000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7618837Z W1217 23:52:48.726000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7620002Z W1217 23:52:48.731000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7621053Z W1217 23:52:48.731000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1855) 2024-12-18T00:40:05.7622172Z W1217 23:52:48.731000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7623222Z W1217 23:52:48.731000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7624438Z W1217 23:52:48.731000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7625593Z W1217 23:52:48.736000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7626642Z W1217 23:52:48.736000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1858) 2024-12-18T00:40:05.7627799Z W1217 23:52:48.736000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7628885Z W1217 23:52:48.736000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7630107Z W1217 23:52:48.736000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7631277Z W1217 23:52:48.740000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7632325Z W1217 23:52:48.740000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1857) 2024-12-18T00:40:05.7633444Z W1217 23:52:48.740000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['x']' rank mismatch. expected 1, actual 3 2024-12-18T00:40:05.7634489Z W1217 23:52:48.740000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7635765Z W1217 23:52:48.740000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7636628Z PASSED [1.8780s] [ 0%] 2024-12-18T00:40:05.7637566Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int16 W1217 23:52:50.519000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7638897Z W1217 23:52:50.519000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1856) 2024-12-18T00:40:05.7640051Z W1217 23:52:50.519000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7641131Z W1217 23:52:50.519000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7642340Z W1217 23:52:50.519000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7643496Z W1217 23:52:50.524000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7644581Z W1217 23:52:50.524000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1855) 2024-12-18T00:40:05.7645688Z W1217 23:52:50.524000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7646726Z W1217 23:52:50.524000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7647941Z W1217 23:52:50.524000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7649098Z W1217 23:52:50.529000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7650138Z W1217 23:52:50.529000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1858) 2024-12-18T00:40:05.7651295Z W1217 23:52:50.529000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7652380Z W1217 23:52:50.529000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7653589Z W1217 23:52:50.529000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7654737Z W1217 23:52:50.533000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7655778Z W1217 23:52:50.533000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1857) 2024-12-18T00:40:05.7656898Z W1217 23:52:50.533000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7657939Z W1217 23:52:50.533000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7659146Z W1217 23:52:50.533000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7659994Z PASSED [1.6713s] [ 0%] 2024-12-18T00:40:05.7660857Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int32 W1217 23:52:52.171000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7662183Z W1217 23:52:52.171000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1856) 2024-12-18T00:40:05.7663400Z W1217 23:52:52.171000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7664470Z W1217 23:52:52.171000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7665681Z W1217 23:52:52.171000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7666837Z W1217 23:52:52.175000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7667883Z W1217 23:52:52.175000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1855) 2024-12-18T00:40:05.7669131Z W1217 23:52:52.175000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7670180Z W1217 23:52:52.175000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7671391Z W1217 23:52:52.175000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7672549Z W1217 23:52:52.180000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7673580Z W1217 23:52:52.180000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1858) 2024-12-18T00:40:05.7674733Z W1217 23:52:52.180000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7675890Z W1217 23:52:52.180000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7677105Z W1217 23:52:52.180000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7678261Z W1217 23:52:52.185000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7679303Z W1217 23:52:52.185000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1857) 2024-12-18T00:40:05.7680415Z W1217 23:52:52.185000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7681459Z W1217 23:52:52.185000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7682676Z W1217 23:52:52.185000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7683513Z PASSED [1.6600s] [ 0%] 2024-12-18T00:40:05.7684374Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int64 W1217 23:52:53.867000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7685703Z W1217 23:52:53.867000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1856) 2024-12-18T00:40:05.7686852Z W1217 23:52:53.867000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7688005Z W1217 23:52:53.867000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7689213Z W1217 23:52:53.867000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7690363Z W1217 23:52:53.872000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7691496Z W1217 23:52:53.872000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1855) 2024-12-18T00:40:05.7692614Z W1217 23:52:53.872000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7693703Z W1217 23:52:53.872000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7694920Z W1217 23:52:53.872000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7696070Z W1217 23:52:53.877000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7697107Z W1217 23:52:53.877000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1858) 2024-12-18T00:40:05.7698413Z W1217 23:52:53.877000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7699493Z W1217 23:52:53.877000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7700712Z W1217 23:52:53.877000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7701870Z W1217 23:52:53.881000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7702913Z W1217 23:52:53.881000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1857) 2024-12-18T00:40:05.7704009Z W1217 23:52:53.881000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7705053Z W1217 23:52:53.881000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7706274Z W1217 23:52:53.881000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7707134Z PASSED [1.6832s] [ 0%] 2024-12-18T00:40:05.7708002Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int8 W1217 23:52:56.011000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7709327Z W1217 23:52:56.011000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1856) 2024-12-18T00:40:05.7710480Z W1217 23:52:56.011000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7711562Z W1217 23:52:56.011000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7712889Z W1217 23:52:56.011000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7714050Z W1217 23:52:56.016000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7715088Z W1217 23:52:56.016000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1855) 2024-12-18T00:40:05.7716277Z W1217 23:52:56.016000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7717415Z W1217 23:52:56.016000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7719110Z W1217 23:52:56.016000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7720392Z W1217 23:52:56.021000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7721443Z W1217 23:52:56.021000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1858) 2024-12-18T00:40:05.7722602Z W1217 23:52:56.021000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7723680Z W1217 23:52:56.021000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7724889Z W1217 23:52:56.021000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7726062Z W1217 23:52:56.025000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7727113Z W1217 23:52:56.025000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1857) 2024-12-18T00:40:05.7728227Z W1217 23:52:56.025000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7729274Z W1217 23:52:56.025000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7730484Z W1217 23:52:56.025000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7731346Z PASSED [2.1613s] [ 0%] 2024-12-18T00:40:05.7732215Z test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_uint8 W1217 23:52:57.682000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7733549Z W1217 23:52:57.682000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1856) 2024-12-18T00:40:05.7734704Z W1217 23:52:57.682000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7735788Z W1217 23:52:57.682000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7736997Z W1217 23:52:57.682000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7738212Z W1217 23:52:57.687000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7739258Z W1217 23:52:57.687000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1855) 2024-12-18T00:40:05.7740359Z W1217 23:52:57.687000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7741405Z W1217 23:52:57.687000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7742602Z W1217 23:52:57.687000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7743755Z W1217 23:52:57.692000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7744850Z W1217 23:52:57.692000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1858) 2024-12-18T00:40:05.7746003Z W1217 23:52:57.692000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['x'])' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7747079Z W1217 23:52:57.692000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7748289Z W1217 23:52:57.692000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7749439Z W1217 23:52:57.696000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.7750487Z W1217 23:52:57.696000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:1857) 2024-12-18T00:40:05.7751594Z W1217 23:52:57.696000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['x']' rank mismatch. expected 1, actual 4 2024-12-18T00:40:05.7752619Z W1217 23:52:57.696000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.7753834Z W1217 23:52:57.696000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.7754696Z PASSED [1.6681s] [ 0%] 2024-12-18T00:40:05.7755142Z test_reductions.py::TestReductionsCPU::test_bincount_cpu PASSED [0.1906s] [ 0%] 2024-12-18T00:40:05.7755890Z test_reductions.py::TestReductionsCPU::test_bucketization_cpu PASSED [1.0145s] [ 0%] 2024-12-18T00:40:05.7756627Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex128 PASSED [0.7281s] [ 0%] 2024-12-18T00:40:05.7757392Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex64 PASSED [0.7096s] [ 1%] 2024-12-18T00:40:05.7758127Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float16 PASSED [0.3245s] [ 1%] 2024-12-18T00:40:05.7758865Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float32 PASSED [0.3103s] [ 1%] 2024-12-18T00:40:05.7759595Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float64 PASSED [0.3244s] [ 1%] 2024-12-18T00:40:05.7760324Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int16 PASSED [0.1889s] [ 1%] 2024-12-18T00:40:05.7761046Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int32 PASSED [0.1895s] [ 1%] 2024-12-18T00:40:05.7761762Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int64 PASSED [0.1863s] [ 1%] 2024-12-18T00:40:05.7762466Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int8 PASSED [0.1879s] [ 1%] 2024-12-18T00:40:05.7763289Z test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_uint8 PASSED [0.1899s] [ 1%] 2024-12-18T00:40:05.7764467Z test_reductions.py::TestReductionsCPU::test_cumprod_integer_upcast_cpu E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] failed while attempting to run meta for aten.cumprod.out 2024-12-18T00:40:05.7765710Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] Traceback (most recent call last): 2024-12-18T00:40:05.7766941Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:05.7768140Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] r = func(*args, **kwargs) 2024-12-18T00:40:05.7769219Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:05.7770420Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return self._op(*args, **kwargs) 2024-12-18T00:40:05.7771281Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:05.7772421Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 4656, in cumprod 2024-12-18T00:40:05.7773734Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return _cumsumprod_common(func=prod, init=1, a=a, dim=dim, dtype=dtype, out=out) 2024-12-18T00:40:05.7775084Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 4634, in _cumsumprod_common 2024-12-18T00:40:05.7776338Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return func(masked_a, dim=dim, dtype=dtype, out=out) 2024-12-18T00:40:05.7777532Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2381, in prod 2024-12-18T00:40:05.7778617Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return _reduction( 2024-12-18T00:40:05.7779359Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] a, 2024-12-18T00:40:05.7780067Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ...<5 lines>... 2024-12-18T00:40:05.7780938Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, 2024-12-18T00:40:05.7781766Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ) 2024-12-18T00:40:05.7782815Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2210, in _reduction 2024-12-18T00:40:05.7783937Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] raise RuntimeError( 2024-12-18T00:40:05.7784843Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] "dtype argument and out dtype must match in reduction" 2024-12-18T00:40:05.7785674Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ) 2024-12-18T00:40:05.7786551Z E1217 23:53:03.238000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] RuntimeError: dtype argument and out dtype must match in reduction 2024-12-18T00:40:05.7787309Z PASSED [0.3592s] [ 1%] 2024-12-18T00:40:05.7788209Z test_reductions.py::TestReductionsCPU::test_cumsum_integer_upcast_cpu E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] failed while attempting to run meta for aten.cumsum.out 2024-12-18T00:40:05.7789428Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] Traceback (most recent call last): 2024-12-18T00:40:05.7790650Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:05.7791950Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] r = func(*args, **kwargs) 2024-12-18T00:40:05.7793023Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:05.7794126Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return self._op(*args, **kwargs) 2024-12-18T00:40:05.7795051Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:05.7796227Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 4645, in cumsum 2024-12-18T00:40:05.7797525Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return _cumsumprod_common(func=sum, init=0, a=a, dim=dim, dtype=dtype, out=out) 2024-12-18T00:40:05.7799033Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 4634, in _cumsumprod_common 2024-12-18T00:40:05.7800285Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return func(masked_a, dim=dim, dtype=dtype, out=out) 2024-12-18T00:40:05.7801481Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2329, in sum 2024-12-18T00:40:05.7802558Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] return _reduction( 2024-12-18T00:40:05.7803304Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] a, 2024-12-18T00:40:05.7804024Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ...<5 lines>... 2024-12-18T00:40:05.7804881Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, 2024-12-18T00:40:05.7805702Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ) 2024-12-18T00:40:05.7806756Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2210, in _reduction 2024-12-18T00:40:05.7807874Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] raise RuntimeError( 2024-12-18T00:40:05.7808791Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] "dtype argument and out dtype must match in reduction" 2024-12-18T00:40:05.7809649Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] ) 2024-12-18T00:40:05.7810537Z E1217 23:53:03.602000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [8/0] RuntimeError: dtype argument and out dtype must match in reduction 2024-12-18T00:40:05.7811253Z PASSED [0.3598s] [ 1%] 2024-12-18T00:40:05.7811778Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_bfloat16 PASSED [0.7090s] [ 1%] 2024-12-18T00:40:05.7812731Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float16 PASSED [0.6990s] [ 1%] 2024-12-18T00:40:05.7813552Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float32 PASSED [0.6804s] [ 1%] 2024-12-18T00:40:05.7814367Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float64 PASSED [0.6828s] [ 1%] 2024-12-18T00:40:05.7815295Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int16 PASSED [0.6849s] [ 1%] 2024-12-18T00:40:05.7816104Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int32 PASSED [0.6762s] [ 1%] 2024-12-18T00:40:05.7816892Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int64 PASSED [0.6634s] [ 1%] 2024-12-18T00:40:05.7817699Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int8 PASSED [0.6712s] [ 1%] 2024-12-18T00:40:05.7818498Z test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_uint8 PASSED [0.6623s] [ 1%] 2024-12-18T00:40:05.7819508Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_all_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7820579Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_amax_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7821645Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_amin_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7822709Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_any_cpu SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7823789Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_count_nonzero_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7824947Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_linalg_vector_norm_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7826077Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7827140Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_prod_cpu SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7828199Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_std_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7829252Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_sum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7830301Z test_reductions.py::TestReductionsCPU::test_dim_default__refs_var_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7831333Z test_reductions.py::TestReductionsCPU::test_dim_default_all_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7832341Z test_reductions.py::TestReductionsCPU::test_dim_default_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7833365Z test_reductions.py::TestReductionsCPU::test_dim_default_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7834379Z test_reductions.py::TestReductionsCPU::test_dim_default_any_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7835405Z test_reductions.py::TestReductionsCPU::test_dim_default_argmax_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7836519Z test_reductions.py::TestReductionsCPU::test_dim_default_argmin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7837585Z test_reductions.py::TestReductionsCPU::test_dim_default_count_nonzero_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7838766Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7839886Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amax_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7841009Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7842132Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_any_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7843173Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_count_nonzero_cpu SKIPPED [0.0121s] (Skipped!) [ 1%] 2024-12-18T00:40:05.7844275Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_linalg_vector_norm_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7845521Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7846652Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_prod_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 1%] 2024-12-18T00:40:05.7847779Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7848891Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_sum_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7850000Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_var_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7851085Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_all_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7852173Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_amax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7853263Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_amin_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7854344Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_any_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7855434Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_argmax_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7856539Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_argmin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7857554Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_count_nonzero_cpu SKIPPED [0.0130s] (Skipped!) [ 2%] 2024-12-18T00:40:05.7858617Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_linalg_vector_norm_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7859774Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_amax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7860913Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7862065Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_argmax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7863222Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_argmin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7864398Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_logsumexp_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7865659Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7866810Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_norm_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7867950Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7869069Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_std_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7870205Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_sum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7871389Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_var_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7872386Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_mean_cpu SKIPPED [0.0121s] (Skipped!) [ 2%] 2024-12-18T00:40:05.7873369Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_nanmean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7874478Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_nansum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7875458Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_prod_cpu SKIPPED [0.0128s] (Skipped!) [ 2%] 2024-12-18T00:40:05.7876378Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_std_cpu SKIPPED [0.0121s] (Skipped!) [ 2%] 2024-12-18T00:40:05.7877363Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_std_unbiased_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7878369Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_sum_cpu SKIPPED [0.0122s] (Skipped!) [ 2%] 2024-12-18T00:40:05.7879204Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_var_cpu SKIPPED [0.0137s] (Skipped!) [ 2%] 2024-12-18T00:40:05.7880203Z test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_var_unbiased_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7881346Z test_reductions.py::TestReductionsCPU::test_dim_default_linalg_vector_norm_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7882447Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7883523Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_amin_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7884620Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_argmax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7885705Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_argmin_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7886818Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_logsumexp_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7887918Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_mean_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7888992Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_norm_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7890077Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7891313Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_std_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7892466Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_sum_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7893535Z test_reductions.py::TestReductionsCPU::test_dim_default_masked_var_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7894567Z test_reductions.py::TestReductionsCPU::test_dim_default_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7895608Z test_reductions.py::TestReductionsCPU::test_dim_default_nanmean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7896655Z test_reductions.py::TestReductionsCPU::test_dim_default_nansum_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 2%] 2024-12-18T00:40:05.7897757Z test_reductions.py::TestReductionsCPU::test_dim_default_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7898988Z test_reductions.py::TestReductionsCPU::test_dim_default_std_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7900039Z test_reductions.py::TestReductionsCPU::test_dim_default_std_unbiased_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7901092Z test_reductions.py::TestReductionsCPU::test_dim_default_sum_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7902105Z test_reductions.py::TestReductionsCPU::test_dim_default_var_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7903144Z test_reductions.py::TestReductionsCPU::test_dim_default_var_unbiased_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7904214Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7905253Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_amax_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7906299Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_amin_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7907342Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_any_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7908299Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_count_nonzero_cpu SKIPPED [0.0122s] (Skipped!) [ 3%] 2024-12-18T00:40:05.7909322Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_linalg_vector_norm_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7910432Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7911476Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_prod_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7912519Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7913449Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_sum_cpu SKIPPED [0.0128s] (Skipped!) [ 3%] 2024-12-18T00:40:05.7914372Z test_reductions.py::TestReductionsCPU::test_dim_empty__refs_var_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7915398Z test_reductions.py::TestReductionsCPU::test_dim_empty_all_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7916602Z test_reductions.py::TestReductionsCPU::test_dim_empty_amax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7917621Z test_reductions.py::TestReductionsCPU::test_dim_empty_amin_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7918630Z test_reductions.py::TestReductionsCPU::test_dim_empty_any_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7919542Z test_reductions.py::TestReductionsCPU::test_dim_empty_count_nonzero_cpu SKIPPED [0.0123s] (Skipped!) [ 3%] 2024-12-18T00:40:05.7920516Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_all_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7921631Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_amax_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7922741Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7923935Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_any_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7924959Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_count_nonzero_cpu SKIPPED [0.0122s] (Skipped!) [ 3%] 2024-12-18T00:40:05.7926033Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_linalg_vector_norm_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7927196Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7928294Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_prod_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7929395Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_std_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7930386Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_sum_cpu SKIPPED [0.0138s] (Skipped!) [ 3%] 2024-12-18T00:40:05.7931364Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_var_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7932440Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7933511Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7934580Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_amin_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7935649Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_any_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7936625Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_count_nonzero_cpu SKIPPED [0.0121s] (Skipped!) [ 3%] 2024-12-18T00:40:05.7937666Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_linalg_vector_norm_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7938818Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_amax_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7939942Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_amin_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7940977Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_logsumexp_cpu SKIPPED [0.0122s] (Skipped!) [ 3%] 2024-12-18T00:40:05.7942006Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7943194Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_norm_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7944313Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7945419Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 3%] 2024-12-18T00:40:05.7946528Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_sum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7947649Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_var_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7948624Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_mean_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7949551Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_nanmean_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7950527Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_nansum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7951485Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_std_cpu SKIPPED [0.0128s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7952332Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_std_unbiased_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7953204Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_sum_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7954030Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_var_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7954887Z test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_var_unbiased_cpu SKIPPED [0.0129s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7955992Z test_reductions.py::TestReductionsCPU::test_dim_empty_linalg_vector_norm_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7957080Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_amax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7958138Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7959090Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_logsumexp_cpu SKIPPED [0.0132s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7960050Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_mean_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7961115Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_norm_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7962176Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_prod_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7963229Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_std_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7964277Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_sum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7965325Z test_reductions.py::TestReductionsCPU::test_dim_empty_masked_var_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7966236Z test_reductions.py::TestReductionsCPU::test_dim_empty_mean_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7967010Z test_reductions.py::TestReductionsCPU::test_dim_empty_nanmean_cpu SKIPPED [0.0127s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7967918Z test_reductions.py::TestReductionsCPU::test_dim_empty_nansum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7968907Z test_reductions.py::TestReductionsCPU::test_dim_empty_std_cpu SKIPPED [0.0120s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7969709Z test_reductions.py::TestReductionsCPU::test_dim_empty_std_unbiased_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7970502Z test_reductions.py::TestReductionsCPU::test_dim_empty_sum_cpu SKIPPED [0.0127s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7971274Z test_reductions.py::TestReductionsCPU::test_dim_empty_var_cpu SKIPPED [0.0121s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7972067Z test_reductions.py::TestReductionsCPU::test_dim_empty_var_unbiased_cpu SKIPPED [0.0122s] (Skipped!) [ 4%] 2024-12-18T00:40:05.7972982Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_all_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.7999199Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_amax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8000549Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8001609Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_any_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8002694Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_count_nonzero_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8003844Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_linalg_vector_norm_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8004946Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8005972Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8006991Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8008000Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_sum_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8009036Z test_reductions.py::TestReductionsCPU::test_dim_multi__refs_var_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8010059Z test_reductions.py::TestReductionsCPU::test_dim_multi_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8011067Z test_reductions.py::TestReductionsCPU::test_dim_multi_amax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8012075Z test_reductions.py::TestReductionsCPU::test_dim_multi_amin_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8013092Z test_reductions.py::TestReductionsCPU::test_dim_multi_any_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8014129Z test_reductions.py::TestReductionsCPU::test_dim_multi_count_nonzero_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8015219Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8016350Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_amax_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8017482Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 4%] 2024-12-18T00:40:05.8018612Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_any_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8020769Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_count_nonzero_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8022666Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_linalg_vector_norm_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8023859Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8024994Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_prod_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8026119Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_std_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8027333Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_sum_cpu SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8028451Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_var_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8029538Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_all_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8030623Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_amax_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8031714Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_amin_cpu SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8032798Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_any_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8033926Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_count_nonzero_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8035107Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_linalg_vector_norm_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8036349Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_amax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8037491Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_amin_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8038658Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_logsumexp_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8039822Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8040975Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_norm_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8042118Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_prod_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8043256Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8044398Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_sum_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8045534Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_var_cpu SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8046650Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8047834Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_nanmean_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8048941Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_nansum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8050036Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_std_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8051156Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_std_unbiased_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8052276Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_sum_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8053367Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_var_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8054546Z test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_var_unbiased_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8055674Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8056776Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8057881Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_amin_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8058986Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_any_cpu SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8060021Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_count_nonzero_cpu SKIPPED [0.0125s] (Skipped!) [ 5%] 2024-12-18T00:40:05.8061114Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_linalg_vector_norm_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8062290Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8063404Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_prod_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8064511Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_std_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8065618Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_sum_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8066728Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_var_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8067806Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_all_cpu SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8068874Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_amax_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8069953Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8071023Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_any_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 5%] 2024-12-18T00:40:05.8072005Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_count_nonzero_cpu SKIPPED [0.0137s] (Skipped!) [ 6%] 2024-12-18T00:40:05.8073134Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_linalg_vector_norm_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8074293Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_amax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8075417Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8076654Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_logsumexp_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8077804Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8078925Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_norm_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8080117Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_prod_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8081242Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_std_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8082360Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_sum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8083473Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_var_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8084567Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8085644Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_nanmean_cpu SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8086748Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_nansum_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8087824Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_std_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8088926Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_std_unbiased_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8090024Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_sum_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8091151Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_var_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8092303Z test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_var_unbiased_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8093442Z test_reductions.py::TestReductionsCPU::test_dim_multi_linalg_vector_norm_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8094514Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_amax_cpu SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8095574Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8096656Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_logsumexp_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8097745Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8099015Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_norm_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8100270Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_prod_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8101329Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_std_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8102384Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_sum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8103419Z test_reductions.py::TestReductionsCPU::test_dim_multi_masked_var_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8104453Z test_reductions.py::TestReductionsCPU::test_dim_multi_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8105477Z test_reductions.py::TestReductionsCPU::test_dim_multi_nanmean_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8106579Z test_reductions.py::TestReductionsCPU::test_dim_multi_nansum_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8107602Z test_reductions.py::TestReductionsCPU::test_dim_multi_std_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8108635Z test_reductions.py::TestReductionsCPU::test_dim_multi_std_unbiased_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8109672Z test_reductions.py::TestReductionsCPU::test_dim_multi_sum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8110724Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_all_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8111828Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_amax_cpu SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8112962Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_amin_cpu SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8114082Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_any_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8115420Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_count_nonzero_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8117008Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_linalg_vector_norm_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8118183Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8119555Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_prod_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8120676Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_std_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8121889Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_sum_cpu SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8123246Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_var_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 6%] 2024-12-18T00:40:05.8124325Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8125391Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_amax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8126553Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_amin_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8127619Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_any_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8128724Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_count_nonzero_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8129879Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_all_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8131048Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_amax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8132211Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8133452Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_any_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8134548Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_count_nonzero_cpu SKIPPED [0.0124s] (Skipped!) [ 7%] 2024-12-18T00:40:05.8135699Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_linalg_vector_norm_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8136932Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_mean_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8138111Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_prod_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8139302Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8140482Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_sum_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8141634Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_var_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8142785Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_all_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8143923Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_amax_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8145060Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_amin_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8146200Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_any_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8147247Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_count_nonzero_cpu SKIPPED [0.0121s] (Skipped!) [ 7%] 2024-12-18T00:40:05.8148351Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_linalg_vector_norm_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8149566Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_amax_cpu SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8150740Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_amin_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8151952Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_logsumexp_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8153228Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8154408Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_norm_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8155600Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_prod_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8156860Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_std_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8158035Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_sum_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8159272Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_var_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8160428Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8161567Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_nanmean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8162733Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_nansum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8163872Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_std_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8165036Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_std_unbiased_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8166208Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_sum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8167336Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_var_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8168501Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_var_unbiased_cpu SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8169690Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_linalg_vector_norm_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8170842Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8171962Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8173116Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_logsumexp_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8174259Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8175382Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_norm_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8176504Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_prod_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 7%] 2024-12-18T00:40:05.8177630Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_std_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8178830Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_sum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8179947Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_var_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8181035Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8182129Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_nanmean_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8183228Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_nansum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8184315Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8185473Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_std_unbiased_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8186575Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_sum_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8187638Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_var_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8188733Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_var_unbiased_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8189866Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_argmax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8191006Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_argmin_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8192243Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_masked_argmax_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8193420Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_masked_argmin_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8194564Z test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_prod_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8195610Z test_reductions.py::TestReductionsCPU::test_dim_multi_var_cpu SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8196706Z test_reductions.py::TestReductionsCPU::test_dim_multi_var_unbiased_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8197798Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_all_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8199567Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_amax_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8201713Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_amin_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8203836Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_any_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8206050Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_count_nonzero_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8208383Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_linalg_vector_norm_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8210796Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_mean_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8212949Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_prod_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8215082Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_std_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8217202Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_sum_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8219334Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_var_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8221406Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_all_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8223485Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_amax_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8225649Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_amin_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8227703Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_any_cpu SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8229780Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_argmax_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8231885Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_argmin_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8234049Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_count_nonzero_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8236386Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_linalg_vector_norm_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8238626Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_amax_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8240792Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_amin_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8242992Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_argmax_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8245203Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_argmin_cpu SKIPPED [0.0172s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8247456Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_logsumexp_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8249690Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8251863Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_norm_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8254033Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_prod_cpu SKIPPED [0.0168s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8256212Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_std_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8258349Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_sum_cpu SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 8%] 2024-12-18T00:40:05.8260508Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_var_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8262691Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_mean_cpu SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8264782Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_nanmean_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8266894Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_nansum_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8268994Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_prod_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8271043Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_std_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8273171Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_std_unbiased_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8275346Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_sum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8277439Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_var_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8279556Z test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_var_unbiased_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8281675Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_all_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8283711Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8285766Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_amin_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8287820Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_any_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8289943Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_count_nonzero_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8292066Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_linalg_vector_norm_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8294227Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8296285Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_prod_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8298452Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8300500Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_sum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8302518Z test_reductions.py::TestReductionsCPU::test_dim_none__refs_var_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8304506Z test_reductions.py::TestReductionsCPU::test_dim_none_all_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8306479Z test_reductions.py::TestReductionsCPU::test_dim_none_amax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8308447Z test_reductions.py::TestReductionsCPU::test_dim_none_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8310414Z test_reductions.py::TestReductionsCPU::test_dim_none_any_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8312538Z test_reductions.py::TestReductionsCPU::test_dim_none_argmax_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8314553Z test_reductions.py::TestReductionsCPU::test_dim_none_argmin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8316688Z test_reductions.py::TestReductionsCPU::test_dim_none_count_nonzero_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8318831Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_all_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8321019Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_amax_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8323216Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_amin_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8325504Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_any_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8327519Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_count_nonzero_cpu SKIPPED [0.0121s] (Skipped!) [ 9%] 2024-12-18T00:40:05.8329659Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_linalg_vector_norm_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8331958Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8334159Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8336329Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_std_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8338515Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_sum_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8340691Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_var_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8342801Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_all_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8344905Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_amax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8346992Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_amin_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8349080Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_any_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8351192Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_argmax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8353330Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_argmin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 9%] 2024-12-18T00:40:05.8355286Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_count_nonzero_cpu SKIPPED [0.0127s] (Skipped!) [ 9%] 2024-12-18T00:40:05.8357412Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_linalg_vector_norm_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8359674Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_amax_cpu SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8361895Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8364745Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_argmax_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8367005Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_argmin_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8369290Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_logsumexp_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8371554Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8373772Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_norm_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8375989Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_prod_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8378267Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_std_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8380476Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_sum_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8382667Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_var_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8384809Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8386939Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_nanmean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8389091Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_nansum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8390985Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_prod_cpu SKIPPED [0.0127s] (Skipped!) [ 10%] 2024-12-18T00:40:05.8392781Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_std_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8394940Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_std_unbiased_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8397180Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_sum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8399427Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_var_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8401591Z test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_var_unbiased_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8403824Z test_reductions.py::TestReductionsCPU::test_dim_none_linalg_vector_norm_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8405968Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8408061Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_amin_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8410182Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_argmax_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8412283Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_argmin_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8414429Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_logsumexp_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8416717Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_mean_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8418817Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_norm_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8420899Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_prod_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8422966Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8425041Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_sum_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8427095Z test_reductions.py::TestReductionsCPU::test_dim_none_masked_var_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8429201Z test_reductions.py::TestReductionsCPU::test_dim_none_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8431216Z test_reductions.py::TestReductionsCPU::test_dim_none_nanmean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8433244Z test_reductions.py::TestReductionsCPU::test_dim_none_nansum_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8434991Z test_reductions.py::TestReductionsCPU::test_dim_none_prod_cpu SKIPPED [0.0121s] (Skipped!) [ 10%] 2024-12-18T00:40:05.8436771Z test_reductions.py::TestReductionsCPU::test_dim_none_std_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8438785Z test_reductions.py::TestReductionsCPU::test_dim_none_std_unbiased_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8440818Z test_reductions.py::TestReductionsCPU::test_dim_none_sum_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8442777Z test_reductions.py::TestReductionsCPU::test_dim_none_var_cpu SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8444776Z test_reductions.py::TestReductionsCPU::test_dim_none_var_unbiased_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8446890Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_all_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8449025Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_amax_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8451180Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_amin_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8453327Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_any_cpu SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 10%] 2024-12-18T00:40:05.8455530Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_count_nonzero_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8457882Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_linalg_vector_norm_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8460150Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8462287Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8464425Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_std_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8466629Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_sum_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8468755Z test_reductions.py::TestReductionsCPU::test_dim_offbounds__refs_var_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8470851Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_all_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8472910Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8474993Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_amin_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8477110Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_any_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8479175Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_argmax_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8481365Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_argmin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8483528Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_count_nonzero_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8485794Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_linalg_vector_norm_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8488039Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_amax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8490217Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8492335Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_argmax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8494568Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_argmin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8496810Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_logsumexp_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8499254Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8501440Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_norm_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8503635Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_prod_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8505856Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_std_cpu SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8508018Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_sum_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8510189Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_var_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8512321Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_mean_cpu SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8514414Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_nanmean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8516600Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_nansum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8518839Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_prod_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8520915Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_std_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8523041Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_std_unbiased_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8525162Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_sum_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8527215Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_var_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8529330Z test_reductions.py::TestReductionsCPU::test_dim_offbounds_var_unbiased_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 11%] 2024-12-18T00:40:05.8531156Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_bfloat16 PASSED [10.3530s] [ 11%] 2024-12-18T00:40:05.8532723Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_float16 PASSED [9.7874s] [ 11%] 2024-12-18T00:40:05.8534165Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_float32 PASSED [10.3216s] [ 11%] 2024-12-18T00:40:05.8535608Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_float64 PASSED [10.3589s] [ 11%] 2024-12-18T00:40:05.8537039Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int16 PASSED [10.2092s] [ 11%] 2024-12-18T00:40:05.8538451Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int32 PASSED [10.1952s] [ 11%] 2024-12-18T00:40:05.8539865Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int64 PASSED [10.7581s] [ 11%] 2024-12-18T00:40:05.8541268Z test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int8 PASSED [10.1562s] [ 11%] 2024-12-18T00:40:05.8542832Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_bfloat16 PASSED [0.7935s] [ 11%] 2024-12-18T00:40:05.8544550Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_float16 PASSED [0.7879s] [ 11%] 2024-12-18T00:40:05.8546246Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_float32 PASSED [0.7962s] [ 11%] 2024-12-18T00:40:05.8547942Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_float64 PASSED [0.7980s] [ 12%] 2024-12-18T00:40:05.8549616Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int16 PASSED [0.8041s] [ 12%] 2024-12-18T00:40:05.8551260Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int32 PASSED [0.8059s] [ 12%] 2024-12-18T00:40:05.8552922Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int64 PASSED [0.7875s] [ 12%] 2024-12-18T00:40:05.8554573Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amax_cpu_int8 PASSED [0.7985s] [ 12%] 2024-12-18T00:40:05.8556330Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_bfloat16 PASSED [0.7873s] [ 12%] 2024-12-18T00:40:05.8558033Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_float16 PASSED [0.7824s] [ 12%] 2024-12-18T00:40:05.8559722Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_float32 PASSED [0.7838s] [ 12%] 2024-12-18T00:40:05.8561405Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_float64 PASSED [0.7936s] [ 12%] 2024-12-18T00:40:05.8563077Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int16 PASSED [0.8351s] [ 12%] 2024-12-18T00:40:05.8564736Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int32 PASSED [0.7967s] [ 12%] 2024-12-18T00:40:05.8566410Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int64 PASSED [0.7925s] [ 12%] 2024-12-18T00:40:05.8568142Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_amin_cpu_int8 PASSED [0.8052s] [ 12%] 2024-12-18T00:40:05.8569833Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_bfloat16 PASSED [0.9451s] [ 12%] 2024-12-18T00:40:05.8571529Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_float16 PASSED [0.9294s] [ 12%] 2024-12-18T00:40:05.8573211Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_float32 PASSED [0.9258s] [ 12%] 2024-12-18T00:40:05.8574866Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_float64 PASSED [0.9357s] [ 12%] 2024-12-18T00:40:05.8576517Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int16 PASSED [0.9159s] [ 12%] 2024-12-18T00:40:05.8578164Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int32 PASSED [0.9267s] [ 12%] 2024-12-18T00:40:05.8579820Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int64 PASSED [1.5024s] [ 12%] 2024-12-18T00:40:05.8581519Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_max_cpu_int8 PASSED [0.9324s] [ 12%] 2024-12-18T00:40:05.8583202Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_bfloat16 PASSED [0.8218s] [ 12%] 2024-12-18T00:40:05.8584891Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_float16 PASSED [0.8043s] [ 12%] 2024-12-18T00:40:05.8586591Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_float32 PASSED [0.8189s] [ 12%] 2024-12-18T00:40:05.8588279Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_float64 PASSED [0.8255s] [ 12%] 2024-12-18T00:40:05.8589965Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int16 PASSED [0.8347s] [ 12%] 2024-12-18T00:40:05.8591529Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int32 PASSED [0.8195s] [ 12%] 2024-12-18T00:40:05.8593194Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int64 PASSED [0.8100s] [ 12%] 2024-12-18T00:40:05.8594844Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mean_cpu_int8 PASSED [0.7962s] [ 12%] 2024-12-18T00:40:05.8596592Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_bfloat16 PASSED [0.8339s] [ 12%] 2024-12-18T00:40:05.8598443Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_float16 PASSED [0.8279s] [ 12%] 2024-12-18T00:40:05.8600170Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_float32 PASSED [0.8477s] [ 12%] 2024-12-18T00:40:05.8601889Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_float64 PASSED [0.8266s] [ 12%] 2024-12-18T00:40:05.8603594Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int16 PASSED [0.8308s] [ 12%] 2024-12-18T00:40:05.8605301Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int32 PASSED [0.8218s] [ 12%] 2024-12-18T00:40:05.8606994Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int64 PASSED [0.8316s] [ 12%] 2024-12-18T00:40:05.8608657Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_median_cpu_int8 PASSED [0.8601s] [ 12%] 2024-12-18T00:40:05.8610337Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_bfloat16 PASSED [0.9378s] [ 12%] 2024-12-18T00:40:05.8612028Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_float16 PASSED [0.9006s] [ 12%] 2024-12-18T00:40:05.8613704Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_float32 PASSED [0.9218s] [ 12%] 2024-12-18T00:40:05.8615376Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_float64 PASSED [0.9100s] [ 12%] 2024-12-18T00:40:05.8617159Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int16 PASSED [0.9116s] [ 12%] 2024-12-18T00:40:05.8618814Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int32 PASSED [0.9147s] [ 12%] 2024-12-18T00:40:05.8620444Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int64 PASSED [0.9543s] [ 12%] 2024-12-18T00:40:05.8622079Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_min_cpu_int8 PASSED [1.5202s] [ 12%] 2024-12-18T00:40:05.8623746Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_bfloat16 PASSED [0.9431s] [ 12%] 2024-12-18T00:40:05.8625451Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_float16 PASSED [0.9431s] [ 12%] 2024-12-18T00:40:05.8627142Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_float32 PASSED [0.9746s] [ 13%] 2024-12-18T00:40:05.8628830Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_float64 PASSED [0.9548s] [ 13%] 2024-12-18T00:40:05.8630597Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int16 PASSED [0.9417s] [ 13%] 2024-12-18T00:40:05.8632241Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int32 PASSED [0.9243s] [ 13%] 2024-12-18T00:40:05.8633895Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int64 PASSED [0.9282s] [ 13%] 2024-12-18T00:40:05.8635550Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_mode_cpu_int8 PASSED [0.9259s] [ 13%] 2024-12-18T00:40:05.8637331Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_bfloat16 PASSED [0.8386s] [ 13%] 2024-12-18T00:40:05.8639107Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_float16 PASSED [0.8251s] [ 13%] 2024-12-18T00:40:05.8640870Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_float32 PASSED [0.8387s] [ 13%] 2024-12-18T00:40:05.8642624Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_float64 PASSED [0.8252s] [ 13%] 2024-12-18T00:40:05.8644385Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int16 PASSED [0.8439s] [ 13%] 2024-12-18T00:40:05.8646120Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int32 PASSED [0.8447s] [ 13%] 2024-12-18T00:40:05.8647847Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int64 PASSED [0.8554s] [ 13%] 2024-12-18T00:40:05.8649580Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_nanmedian_cpu_int8 PASSED [0.8252s] [ 13%] 2024-12-18T00:40:05.8651303Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_bfloat16 PASSED [0.8501s] [ 13%] 2024-12-18T00:40:05.8653005Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_float16 PASSED [0.8413s] [ 13%] 2024-12-18T00:40:05.8654695Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_float32 PASSED [0.8378s] [ 13%] 2024-12-18T00:40:05.8656399Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_float64 PASSED [0.8323s] [ 13%] 2024-12-18T00:40:05.8658074Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int16 PASSED [0.8450s] [ 13%] 2024-12-18T00:40:05.8659729Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int32 PASSED [0.8328s] [ 13%] 2024-12-18T00:40:05.8661388Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int64 PASSED [0.8463s] [ 13%] 2024-12-18T00:40:05.8663041Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_norm_cpu_int8 PASSED [0.8528s] [ 13%] 2024-12-18T00:40:05.8664716Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_bfloat16 PASSED [1.4335s] [ 13%] 2024-12-18T00:40:05.8666404Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_float16 PASSED [0.7993s] [ 13%] 2024-12-18T00:40:05.8668201Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_float32 PASSED [0.8046s] [ 13%] 2024-12-18T00:40:05.8669892Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_float64 PASSED [0.8022s] [ 13%] 2024-12-18T00:40:05.8671584Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int16 PASSED [0.8038s] [ 13%] 2024-12-18T00:40:05.8673244Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int32 PASSED [0.8057s] [ 13%] 2024-12-18T00:40:05.8674902Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int64 PASSED [0.7883s] [ 13%] 2024-12-18T00:40:05.8676655Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_prod_cpu_int8 PASSED [0.8255s] [ 13%] 2024-12-18T00:40:05.8678316Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_bfloat16 PASSED [0.8540s] [ 13%] 2024-12-18T00:40:05.8680013Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_float16 PASSED [0.8057s] [ 13%] 2024-12-18T00:40:05.8681758Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_float32 PASSED [0.8099s] [ 13%] 2024-12-18T00:40:05.8683432Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_float64 PASSED [0.8120s] [ 13%] 2024-12-18T00:40:05.8685092Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int16 PASSED [0.8220s] [ 13%] 2024-12-18T00:40:05.8686741Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int32 PASSED [0.8118s] [ 13%] 2024-12-18T00:40:05.8688385Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int64 PASSED [0.7989s] [ 13%] 2024-12-18T00:40:05.8690006Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_std_cpu_int8 PASSED [0.7891s] [ 13%] 2024-12-18T00:40:05.8691596Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_bfloat16 PASSED [0.8083s] [ 13%] 2024-12-18T00:40:05.8693300Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_float16 PASSED [0.7989s] [ 13%] 2024-12-18T00:40:05.8694974Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_float32 PASSED [0.8074s] [ 13%] 2024-12-18T00:40:05.8696634Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_float64 PASSED [0.7910s] [ 13%] 2024-12-18T00:40:05.8698427Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int16 PASSED [0.7999s] [ 13%] 2024-12-18T00:40:05.8700061Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int32 PASSED [0.8019s] [ 13%] 2024-12-18T00:40:05.8700775Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int64 PASSED [0.8068s] [ 13%] 2024-12-18T00:40:05.8701478Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_cpu_int8 PASSED [0.8033s] [ 13%] 2024-12-18T00:40:05.8702231Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_bfloat16 PASSED [0.8200s] [ 14%] 2024-12-18T00:40:05.8702950Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_float16 PASSED [0.8143s] [ 14%] 2024-12-18T00:40:05.8703678Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_float32 PASSED [0.8288s] [ 14%] 2024-12-18T00:40:05.8704396Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_float64 PASSED [1.4643s] [ 14%] 2024-12-18T00:40:05.8705104Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int16 PASSED [0.7923s] [ 14%] 2024-12-18T00:40:05.8705799Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int32 PASSED [0.8011s] [ 14%] 2024-12-18T00:40:05.8706512Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int64 PASSED [0.8142s] [ 14%] 2024-12-18T00:40:05.8707389Z test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_var_cpu_int8 PASSED [0.7974s] [ 14%] 2024-12-18T00:40:05.8708067Z test_reductions.py::TestReductionsCPU::test_dim_reduction_lastdim_cpu_bfloat16 PASSED [0.0505s] [ 14%] 2024-12-18T00:40:05.8708749Z test_reductions.py::TestReductionsCPU::test_dim_reduction_lastdim_cpu_float32 PASSED [0.0482s] [ 14%] 2024-12-18T00:40:05.8709389Z test_reductions.py::TestReductionsCPU::test_dim_reduction_less_than_64_cpu PASSED [0.0256s] [ 14%] 2024-12-18T00:40:05.8710333Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_all_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8711269Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_amax_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8712217Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_amin_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8713239Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_any_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8714263Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_count_nonzero_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8715310Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_linalg_vector_norm_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8716323Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_mean_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8717255Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_prod_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8718179Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_std_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8719116Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_sum_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8720030Z test_reductions.py::TestReductionsCPU::test_dim_single__refs_var_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8720919Z test_reductions.py::TestReductionsCPU::test_dim_single_all_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8721803Z test_reductions.py::TestReductionsCPU::test_dim_single_amax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8722708Z test_reductions.py::TestReductionsCPU::test_dim_single_amin_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8723585Z test_reductions.py::TestReductionsCPU::test_dim_single_any_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8724509Z test_reductions.py::TestReductionsCPU::test_dim_single_argmax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8725426Z test_reductions.py::TestReductionsCPU::test_dim_single_argmin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8726414Z test_reductions.py::TestReductionsCPU::test_dim_single_count_nonzero_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8727406Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_all_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8728428Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_amax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8729430Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8730528Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_any_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8731366Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_count_nonzero_cpu SKIPPED [0.0126s] (Skipped!) [ 14%] 2024-12-18T00:40:05.8732469Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_linalg_vector_norm_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8733476Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8734477Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_prod_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8735483Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_std_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8736611Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_sum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8737613Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_var_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8738564Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_all_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8739544Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_amax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8740493Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8741450Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_any_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8742433Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_argmax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8743420Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_argmin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 14%] 2024-12-18T00:40:05.8744213Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_count_nonzero_cpu SKIPPED [0.0122s] (Skipped!) [ 15%] 2024-12-18T00:40:05.8745303Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_linalg_vector_norm_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8746325Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_amax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8747344Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8748412Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_argmax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8749445Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_argmin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8750524Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_logsumexp_cpu SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8751535Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8752554Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_norm_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8753575Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_prod_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8754665Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_std_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8755739Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_sum_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8756760Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_var_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8757713Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_mean_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8758712Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_nanmean_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8759696Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_nansum_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8760732Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_prod_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8761671Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_std_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8762700Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_std_unbiased_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8763644Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_sum_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8764583Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_var_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8765628Z test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_var_unbiased_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8766629Z test_reductions.py::TestReductionsCPU::test_dim_single_linalg_vector_norm_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8767581Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_amax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8768522Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8769493Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_argmax_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8770464Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_argmin_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8771476Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_logsumexp_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8772411Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8773370Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_norm_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8774319Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_prod_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8775264Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_std_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8776196Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_sum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8777213Z test_reductions.py::TestReductionsCPU::test_dim_single_masked_var_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8778099Z test_reductions.py::TestReductionsCPU::test_dim_single_mean_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8779025Z test_reductions.py::TestReductionsCPU::test_dim_single_nanmean_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8779948Z test_reductions.py::TestReductionsCPU::test_dim_single_nansum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8780836Z test_reductions.py::TestReductionsCPU::test_dim_single_prod_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8781724Z test_reductions.py::TestReductionsCPU::test_dim_single_std_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8782689Z test_reductions.py::TestReductionsCPU::test_dim_single_std_unbiased_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8783637Z test_reductions.py::TestReductionsCPU::test_dim_single_sum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8784518Z test_reductions.py::TestReductionsCPU::test_dim_single_var_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8785486Z test_reductions.py::TestReductionsCPU::test_dim_single_var_unbiased_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8786527Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_all_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8787587Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_amax_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8788641Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_amin_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 15%] 2024-12-18T00:40:05.8789685Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_any_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8790793Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_count_nonzero_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8791851Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_linalg_vector_norm_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8792893Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_mean_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8793943Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_prod_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8794997Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_std_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8796087Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_sum_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8797130Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice__refs_var_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8798441Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_all_cpu SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8799459Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_amax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8800624Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_amin_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8801641Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_any_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8802660Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_argmax_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8803694Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_argmin_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8804775Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_count_nonzero_cpu SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8805903Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_linalg_vector_norm_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8807060Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_amax_cpu SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8808141Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_amin_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8809221Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_argmax_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8810325Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_argmin_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8811193Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_logsumexp_cpu SKIPPED [0.0131s] (Skipped!) [ 16%] 2024-12-18T00:40:05.8812251Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8813333Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_norm_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8814380Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_prod_cpu SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8815439Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_std_cpu SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8816483Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_sum_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8817540Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_var_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8818551Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_mean_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8819586Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_nanmean_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8820342Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_nansum_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8822250Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_prod_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8823993Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_std_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8826202Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_std_unbiased_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8828623Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_sum_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8830788Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_var_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8833021Z test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_var_unbiased_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8834761Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_all_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8836643Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_amax_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8838640Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_amin_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8840819Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_any_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8843015Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_count_nonzero_cpu SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8845396Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_linalg_vector_norm_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8847900Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8850305Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_prod_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8852710Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_std_cpu SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8855084Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_sum_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8857440Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_var_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 16%] 2024-12-18T00:40:05.8859748Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_all_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8862028Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_amax_cpu SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8864339Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_amin_cpu SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8866638Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_any_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8868949Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_argmax_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8871303Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_argmin_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8873709Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_count_nonzero_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8876314Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_linalg_vector_norm_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8878929Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_amax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8881325Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_amin_cpu SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8883755Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_argmax_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8886196Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_argmin_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8888679Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_logsumexp_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8891167Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_mean_cpu SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8893528Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_norm_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8895947Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_prod_cpu SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8898393Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_std_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8900233Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_sum_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8902320Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_var_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8904132Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_mean_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8906151Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_nanmean_cpu SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8908521Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_nansum_cpu SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8910841Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_prod_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8913123Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_std_cpu SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8915495Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_std_unbiased_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8917963Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_sum_cpu SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8920249Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_var_cpu SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8922578Z test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_var_unbiased_cpu SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 17%] 2024-12-18T00:40:05.8924420Z test_reductions.py::TestReductionsCPU::test_histc_cpu PASSED [1.0905s] [ 17%] 2024-12-18T00:40:05.8925741Z test_reductions.py::TestReductionsCPU::test_histc_lowp_cpu_bfloat16 PASSED [0.0984s] [ 17%] 2024-12-18T00:40:05.8927149Z test_reductions.py::TestReductionsCPU::test_histc_lowp_cpu_float16 PASSED [0.0955s] [ 17%] 2024-12-18T00:40:05.8928844Z test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cpu_float32 PASSED [0.0553s] [ 17%] 2024-12-18T00:40:05.8930477Z test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cpu_float64 PASSED [0.0571s] [ 17%] 2024-12-18T00:40:05.8932284Z test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_int32 SKIPPED [0.0150s] (Only runs on cuda) [ 17%] 2024-12-18T00:40:05.8934256Z test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_int64 SKIPPED [0.0149s] (Only runs on cuda) [ 17%] 2024-12-18T00:40:05.8936232Z test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_int8 SKIPPED [0.0156s] (Only runs on cuda) [ 17%] 2024-12-18T00:40:05.8938214Z test_reductions.py::TestReductionsCPU::test_histc_min_max_corner_cases_cuda_cpu_uint8 SKIPPED [0.0199s] (Only runs on cuda) [ 17%] 2024-12-18T00:40:05.8939969Z test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_float32 PASSED [0.0334s] [ 17%] 2024-12-18T00:40:05.8941638Z test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_float64 PASSED [0.0342s] [ 17%] 2024-12-18T00:40:05.8943167Z test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_int32 PASSED [0.0344s] [ 17%] 2024-12-18T00:40:05.8944688Z test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_int64 PASSED [0.0341s] [ 17%] 2024-12-18T00:40:05.8946183Z test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_int8 PASSED [0.0339s] [ 17%] 2024-12-18T00:40:05.8947692Z test_reductions.py::TestReductionsCPU::test_histc_min_max_errors_cpu_uint8 PASSED [0.0346s] [ 17%] 2024-12-18T00:40:05.8949907Z test_reductions.py::TestReductionsCPU::test_histogram_cpu_float32 W1217 23:56:16.285000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.8952625Z W1217 23:56:16.285000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '_test_histogram_numpy' (/var/lib/jenkins/workspace/test/test_reductions.py:3147) 2024-12-18T00:40:05.8954962Z W1217 23:56:16.285000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: ___check_obj_id(L['bin_range'], 8975440) 2024-12-18T00:40:05.8957126Z W1217 23:56:16.285000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.8959564Z W1217 23:56:16.285000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.8961857Z W1217 23:56:16.805000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.8964240Z W1217 23:56:16.805000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: 'torch_dynamo_resume_in__test_histogram_numpy_at_3207' (/var/lib/jenkins/workspace/test/test_reductions.py:3207) 2024-12-18T00:40:05.8966740Z W1217 23:56:16.805000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: L['bins'] == 1 2024-12-18T00:40:05.8968781Z W1217 23:56:16.805000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.8971193Z W1217 23:56:16.805000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.8973551Z SKIPPED [9.5714s] (This test passed, maybe we can remove `test/dynamo_skips/TestReductionsCPU.test_histogram_cpu_float32`) [ 17%] 2024-12-18T00:40:05.8975334Z test_reductions.py::TestReductionsCPU::test_histogram_error_handling_cpu_float32 PASSED [0.0895s] [ 17%] 2024-12-18T00:40:05.8977636Z test_reductions.py::TestReductionsCPU::test_histogramdd_cpu_float32 W1217 23:56:25.233000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.8980461Z W1217 23:56:25.233000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: 'reference_histogramdd' (/var/lib/jenkins/workspace/test/test_reductions.py:3319) 2024-12-18T00:40:05.8982766Z W1217 23:56:25.233000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: L['bins'] == 4 2024-12-18T00:40:05.8984809Z W1217 23:56:25.233000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.8987185Z W1217 23:56:25.233000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.8989475Z W1217 23:56:25.704000 904 site-packages/torch/_dynamo/convert_frame.py:906] [1/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.8991635Z W1217 23:56:25.704000 904 site-packages/torch/_dynamo/convert_frame.py:906] [1/8] function: '_test_histogramdd_numpy' (/var/lib/jenkins/workspace/test/test_reductions.py:3310) 2024-12-18T00:40:05.8993924Z W1217 23:56:25.704000 904 site-packages/torch/_dynamo/convert_frame.py:906] [1/8] last reason: 1/0: L['bins'] == 4 2024-12-18T00:40:05.8996072Z W1217 23:56:25.704000 904 site-packages/torch/_dynamo/convert_frame.py:906] [1/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.8998687Z W1217 23:56:25.704000 904 site-packages/torch/_dynamo/convert_frame.py:906] [1/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9001469Z SKIPPED [5.2822s] (Failed running call_function >(*(FakeTensor(..., size=(3, 5)), s1), **{'range': None, 'weights': FakeTensor(..., size=(3,)), 'density': True}): 2024-12-18T00:40:05.9003175Z 'SymInt' object is not iterable 2024-12-18T00:40:05.9003534Z 2024-12-18T00:40:05.9003702Z from user code: 2024-12-18T00:40:05.9004866Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 3341, in torch_dynamo_resume_in__test_histogramdd_numpy_at_3340 2024-12-18T00:40:05.9006758Z (expected_hist, expected_bin_edges) = reference_histogramdd(t, bins, bin_range, weights, density, actual_hist.dtype) 2024-12-18T00:40:05.9008399Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 3335, in reference_histogramdd 2024-12-18T00:40:05.9009607Z (np_hist, np_bin_edges) = np.histogramdd(reshaped_t, np_bins, 2024-12-18T00:40:05.9010153Z 2024-12-18T00:40:05.9010565Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:05.9011199Z 2024-12-18T00:40:05.9011208Z 2024-12-18T00:40:05.9011603Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:05.9012388Z import torch._dynamo 2024-12-18T00:40:05.9012957Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:05.9013440Z 2024-12-18T00:40:05.9013452Z 2024-12-18T00:40:05.9013807Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:05.9015103Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_histogramdd_cpu_float32 2024-12-18T00:40:05.9016050Z 2024-12-18T00:40:05.9016560Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 17%] 2024-12-18T00:40:05.9018289Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_bfloat16 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9020461Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_bool SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9022637Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_complex128 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9025011Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_complex64 SKIPPED [0.0175s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9027222Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9029418Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9031607Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9033768Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int16 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9035992Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9038231Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9040348Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9042489Z test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_uint8 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9044678Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9046853Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9049046Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9051287Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_complex64 SKIPPED [0.0190s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9053505Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9055692Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9057859Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9060025Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9062174Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9064335Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9066486Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9068634Z test_reductions.py::TestReductionsCPU::test_identity__refs_any_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9070915Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9073264Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9075757Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9078164Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9080541Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9082914Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9085271Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9087609Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9090000Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9092263Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9094095Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9095240Z test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_uint8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9096432Z test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9097668Z test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9099133Z test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9100361Z test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9101577Z test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9102787Z test_reductions.py::TestReductionsCPU::test_identity__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9103952Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9105066Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_bool SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9106173Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9107317Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 18%] 2024-12-18T00:40:05.9108439Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9109548Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9110655Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9111887Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9112982Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9114078Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9115168Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9116315Z test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9117417Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9118603Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_bool SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9119713Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9120836Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9121942Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9123036Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9124132Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9125204Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9126286Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9127364Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int64 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9128435Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9129505Z test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9130575Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9131627Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_bool SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9132688Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9133755Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9134822Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9135878Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9136993Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9138042Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9139080Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9140119Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9141152Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9142174Z test_reductions.py::TestReductionsCPU::test_identity_all_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9143230Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9144337Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9145394Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9146482Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9147553Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9148619Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_float32 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9149676Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9150728Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9151769Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9152814Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int64 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9153846Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9154879Z test_reductions.py::TestReductionsCPU::test_identity_any_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9156047Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9157195Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_bool SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 19%] 2024-12-18T00:40:05.9158346Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9159500Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9160671Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9161814Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9163021Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9164157Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9165288Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9166416Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9167536Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9168646Z test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9169861Z test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9171068Z test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_complex128 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9172273Z test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9173458Z test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9174640Z test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9175814Z test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_norm_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9176968Z test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9178093Z test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9179205Z test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9180331Z test_reductions.py::TestReductionsCPU::test_identity_masked_norm_cpu_float64 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9181460Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_bfloat16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9182581Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_bool SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9183705Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9184853Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_complex64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9185976Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9187095Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9188198Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9189365Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9190466Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9191653Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9192752Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9193856Z test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9194974Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9196227Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_bool SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9197350Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9198653Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9199781Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9200893Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9202001Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9203110Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9204204Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9205294Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9206388Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9207465Z test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_uint8 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9208562Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9209652Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9210730Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 20%] 2024-12-18T00:40:05.9211817Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9212910Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9213984Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9215051Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9216226Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9217293Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9218360Z test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_uint8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9219436Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9220501Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_bool SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9221578Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9222737Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9223813Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9224869Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_float32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9225932Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9226996Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9228049Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9229108Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9230157Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9231203Z test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_uint8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9232263Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9233304Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_bool SKIPPED [0.0171s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9234359Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9235446Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9236590Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9237650Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_float32 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9238709Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9239755Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9240796Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9241887Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int64 SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9242924Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9243958Z test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 21%] 2024-12-18T00:40:05.9244897Z test_reductions.py::TestReductionsCPU::test_invalid_0dim_aminmax_cpu_complex128 PASSED [0.0354s] [ 21%] 2024-12-18T00:40:05.9245718Z test_reductions.py::TestReductionsCPU::test_invalid_0dim_aminmax_cpu_complex64 PASSED [0.0342s] [ 21%] 2024-12-18T00:40:05.9246742Z test_reductions.py::TestReductionsCPU::test_logcumsumexp_complex_cpu_complex128 SKIPPED [1.4200s] (Unexpected exception when running generated GraphModule 2024-12-18T00:40:05.9247411Z 2024-12-18T00:40:05.9247621Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:05.9248379Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_logcumsumexp_complex_cpu_complex128 2024-12-18T00:40:05.9248923Z 2024-12-18T00:40:05.9249186Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 21%] 2024-12-18T00:40:05.9250118Z test_reductions.py::TestReductionsCPU::test_logcumsumexp_complex_cpu_complex64 SKIPPED [1.4024s] (Unexpected exception when running generated GraphModule 2024-12-18T00:40:05.9250779Z 2024-12-18T00:40:05.9264139Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:05.9264894Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_logcumsumexp_complex_cpu_complex64 2024-12-18T00:40:05.9265429Z 2024-12-18T00:40:05.9265710Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 21%] 2024-12-18T00:40:05.9266417Z test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_complex128 PASSED [0.4370s] [ 21%] 2024-12-18T00:40:05.9267160Z test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_complex64 PASSED [0.4279s] [ 21%] 2024-12-18T00:40:05.9267882Z test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_float32 PASSED [0.4307s] [ 21%] 2024-12-18T00:40:05.9268591Z test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_float64 PASSED [0.4179s] [ 21%] 2024-12-18T00:40:05.9269665Z test_reductions.py::TestReductionsCPU::test_logsumexp_dim_cpu W1217 23:56:39.222000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9270962Z W1217 23:56:39.222000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:876) 2024-12-18T00:40:05.9272115Z W1217 23:56:39.222000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['n'])' rank mismatch. expected 2, actual 3 2024-12-18T00:40:05.9273225Z W1217 23:56:39.222000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9274448Z W1217 23:56:39.222000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9275616Z W1217 23:56:39.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [13/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9276799Z W1217 23:56:39.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [13/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:875) 2024-12-18T00:40:05.9277918Z W1217 23:56:39.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [13/8] last reason: 13/0: tensor 'L['t']' rank mismatch. expected 2, actual 3 2024-12-18T00:40:05.9278975Z W1217 23:56:39.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [13/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9280338Z W1217 23:56:39.227000 904 site-packages/torch/_dynamo/convert_frame.py:906] [13/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9281512Z W1217 23:56:39.237000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9282789Z W1217 23:56:39.237000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] function: 'torch_dynamo_resume_in_logsumexp_at_96' (/opt/conda/envs/py_3.13/lib/python3.13/site-packages/scipy/special/_logsumexp.py:96) 2024-12-18T00:40:05.9284197Z W1217 23:56:39.237000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] last reason: 10/0: tensor '___from_numpy(L['___stack0'])' rank mismatch. expected 2, actual 3 2024-12-18T00:40:05.9285312Z W1217 23:56:39.237000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9286663Z W1217 23:56:39.237000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9287834Z W1217 23:56:39.628000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9288909Z W1217 23:56:39.628000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: 'make_contiguous' (/var/lib/jenkins/workspace/test/test_reductions.py:749) 2024-12-18T00:40:05.9290047Z W1217 23:56:39.628000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: len(L['shape']) == 2 2024-12-18T00:40:05.9291171Z W1217 23:56:39.628000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9292416Z W1217 23:56:39.628000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9293274Z PASSED [3.5954s] [ 21%] 2024-12-18T00:40:05.9293787Z test_reductions.py::TestReductionsCPU::test_logsumexp_integral_promotion_cpu PASSED [0.3194s] [ 21%] 2024-12-18T00:40:05.9294518Z test_reductions.py::TestReductionsCPU::test_max_cpu_bool PASSED [0.3851s] [ 21%] 2024-12-18T00:40:05.9295172Z test_reductions.py::TestReductionsCPU::test_max_cpu_float16 PASSED [0.4107s] [ 21%] 2024-12-18T00:40:05.9295832Z test_reductions.py::TestReductionsCPU::test_max_cpu_float32 PASSED [0.4082s] [ 21%] 2024-12-18T00:40:05.9296496Z test_reductions.py::TestReductionsCPU::test_max_cpu_float64 PASSED [0.4332s] [ 21%] 2024-12-18T00:40:05.9297154Z test_reductions.py::TestReductionsCPU::test_max_cpu_int64 PASSED [0.4020s] [ 21%] 2024-12-18T00:40:05.9297810Z test_reductions.py::TestReductionsCPU::test_max_elementwise_cpu PASSED [0.1010s] [ 22%] 2024-12-18T00:40:05.9298694Z test_reductions.py::TestReductionsCPU::test_max_mixed_devices_cpu PASSED [0.0231s] [ 22%] 2024-12-18T00:40:05.9299416Z test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_bfloat16 PASSED [0.2167s] [ 22%] 2024-12-18T00:40:05.9300146Z test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_float16 PASSED [0.2140s] [ 22%] 2024-12-18T00:40:05.9300874Z test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_float32 PASSED [0.2156s] [ 22%] 2024-12-18T00:40:05.9301594Z test_reductions.py::TestReductionsCPU::test_max_with_inf_cpu_float64 PASSED [0.2139s] [ 22%] 2024-12-18T00:40:05.9302645Z test_reductions.py::TestReductionsCPU::test_mean_dim_cpu W1217 23:56:50.040000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9303895Z W1217 23:56:50.040000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:850) 2024-12-18T00:40:05.9305195Z W1217 23:56:50.040000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] last reason: 8/0: tensor '___from_numpy(L['n'])' size mismatch at index 0. expected 5, actual 1 2024-12-18T00:40:05.9306316Z W1217 23:56:50.040000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9307537Z W1217 23:56:50.040000 904 site-packages/torch/_dynamo/convert_frame.py:906] [8/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9308696Z W1217 23:56:50.046000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9309746Z W1217 23:56:50.046000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:849) 2024-12-18T00:40:05.9310951Z W1217 23:56:50.046000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] last reason: 9/0: tensor 'L['t']' size mismatch at index 0. expected 5, actual 1 2024-12-18T00:40:05.9312025Z W1217 23:56:50.046000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9313240Z W1217 23:56:50.046000 904 site-packages/torch/_dynamo/convert_frame.py:906] [9/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9314390Z W1217 23:56:51.440000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9315465Z W1217 23:56:51.440000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: 'make_contiguous' (/var/lib/jenkins/workspace/test/test_reductions.py:749) 2024-12-18T00:40:05.9316683Z W1217 23:56:51.440000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: L['dtype'] == torch.float64 2024-12-18T00:40:05.9317745Z W1217 23:56:51.440000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9318959Z W1217 23:56:51.440000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9320120Z W1217 23:56:51.615000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9321215Z W1217 23:56:51.615000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] function: '_assert_matches_numpy' (/var/lib/jenkins/workspace/test/test_reductions.py:790) 2024-12-18T00:40:05.9322388Z W1217 23:56:51.615000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] last reason: 10/0: tensor 'L['t']' rank mismatch. expected 1, actual 2 2024-12-18T00:40:05.9323450Z W1217 23:56:51.615000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9324660Z W1217 23:56:51.615000 904 site-packages/torch/_dynamo/convert_frame.py:906] [10/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9325826Z W1217 23:56:51.642000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9326906Z W1217 23:56:51.642000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] function: 'make_non_contiguous' (/var/lib/jenkins/workspace/test/test_reductions.py:760) 2024-12-18T00:40:05.9328053Z W1217 23:56:51.642000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] last reason: 3/0: len(L['shape']) == 2 2024-12-18T00:40:05.9329096Z W1217 23:56:51.642000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9330390Z W1217 23:56:51.642000 904 site-packages/torch/_dynamo/convert_frame.py:906] [3/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9331254Z PASSED [11.0736s] [ 22%] 2024-12-18T00:40:05.9331759Z test_reductions.py::TestReductionsCPU::test_mean_int_with_optdtype_cpu PASSED [0.1081s] [ 22%] 2024-12-18T00:40:05.9332538Z test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_bfloat16 PASSED [0.1079s] [ 22%] 2024-12-18T00:40:05.9333369Z test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_float16 PASSED [0.1053s] [ 22%] 2024-12-18T00:40:05.9334195Z test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_float32 PASSED [0.1026s] [ 22%] 2024-12-18T00:40:05.9335015Z test_reductions.py::TestReductionsCPU::test_mean_out_is_alias_of_return_cpu_float64 PASSED [0.1023s] [ 22%] 2024-12-18T00:40:05.9336218Z test_reductions.py::TestReductionsCPU::test_median_corner_cases_cpu W1217 23:56:56.527000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:05.9337536Z W1217 23:56:56.527000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: 'check' (/var/lib/jenkins/workspace/test/test_reductions.py:2572) 2024-12-18T00:40:05.9338649Z W1217 23:56:56.527000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: ___check_obj_id(L['op'], 139905357975120) 2024-12-18T00:40:05.9339689Z W1217 23:56:56.527000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:05.9340896Z W1217 23:56:56.527000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:05.9341764Z PASSED [2.7753s] [ 22%] 2024-12-18T00:40:05.9342272Z test_reductions.py::TestReductionsCPU::test_median_nan_values_cpu_float32 PASSED [0.0818s] [ 22%] 2024-12-18T00:40:05.9343042Z test_reductions.py::TestReductionsCPU::test_median_nan_values_cpu_float64 PASSED [0.0670s] [ 22%] 2024-12-18T00:40:05.9343816Z test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_float32 PASSED [0.0434s] [ 22%] 2024-12-18T00:40:05.9344597Z test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_float64 PASSED [0.0428s] [ 22%] 2024-12-18T00:40:05.9345347Z test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_int32 PASSED [0.0370s] [ 22%] 2024-12-18T00:40:05.9346107Z test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_int64 PASSED [0.0361s] [ 22%] 2024-12-18T00:40:05.9346814Z test_reductions.py::TestReductionsCPU::test_min_cpu_bool PASSED [0.3841s] [ 22%] 2024-12-18T00:40:05.9347469Z test_reductions.py::TestReductionsCPU::test_min_cpu_float16 PASSED [0.4157s] [ 22%] 2024-12-18T00:40:05.9348139Z test_reductions.py::TestReductionsCPU::test_min_cpu_float32 PASSED [0.4175s] [ 22%] 2024-12-18T00:40:05.9348809Z test_reductions.py::TestReductionsCPU::test_min_cpu_float64 PASSED [0.4193s] [ 22%] 2024-12-18T00:40:05.9349454Z test_reductions.py::TestReductionsCPU::test_min_cpu_int64 PASSED [0.4005s] [ 22%] 2024-12-18T00:40:05.9350123Z test_reductions.py::TestReductionsCPU::test_min_elementwise_cpu PASSED [0.0997s] [ 22%] 2024-12-18T00:40:05.9350874Z test_reductions.py::TestReductionsCPU::test_min_max_nan_cpu SKIPPED [0.0152s] (Only runs on cuda) [ 22%] 2024-12-18T00:40:05.9351640Z test_reductions.py::TestReductionsCPU::test_min_mixed_devices_cpu PASSED [0.0223s] [ 22%] 2024-12-18T00:40:05.9352369Z test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_bfloat16 PASSED [0.2193s] [ 22%] 2024-12-18T00:40:05.9353103Z test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_float16 PASSED [0.2145s] [ 22%] 2024-12-18T00:40:05.9353817Z test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_float32 PASSED [0.2170s] [ 22%] 2024-12-18T00:40:05.9354598Z test_reductions.py::TestReductionsCPU::test_min_with_inf_cpu_float64 PASSED [0.2246s] [ 22%] 2024-12-18T00:40:05.9355329Z test_reductions.py::TestReductionsCPU::test_minmax_illegal_dtype_cpu PASSED [0.0852s] [ 22%] 2024-12-18T00:40:05.9356111Z test_reductions.py::TestReductionsCPU::test_mode_boolean_cpu PASSED [0.0521s] [ 22%] 2024-12-18T00:40:05.9356786Z test_reductions.py::TestReductionsCPU::test_mode_cpu_float32 PASSED [0.6802s] [ 22%] 2024-12-18T00:40:05.9357457Z test_reductions.py::TestReductionsCPU::test_mode_cpu_float64 PASSED [0.6844s] [ 22%] 2024-12-18T00:40:05.9358109Z test_reductions.py::TestReductionsCPU::test_mode_cpu_int16 PASSED [0.6848s] [ 22%] 2024-12-18T00:40:05.9358761Z test_reductions.py::TestReductionsCPU::test_mode_cpu_int32 PASSED [0.6879s] [ 22%] 2024-12-18T00:40:05.9359407Z test_reductions.py::TestReductionsCPU::test_mode_cpu_int64 PASSED [0.7136s] [ 22%] 2024-12-18T00:40:05.9360061Z test_reductions.py::TestReductionsCPU::test_mode_cpu_int8 PASSED [0.6782s] [ 22%] 2024-12-18T00:40:05.9360774Z test_reductions.py::TestReductionsCPU::test_mode_cpu_uint8 PASSED [0.6713s] [ 22%] 2024-12-18T00:40:05.9361534Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_bfloat16 SKIPPED [0.0159s] (Only runs on cuda) [ 22%] 2024-12-18T00:40:05.9362405Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_float16 SKIPPED [0.0152s] (Only runs on cuda) [ 22%] 2024-12-18T00:40:05.9363249Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_float32 SKIPPED [0.0161s] (Only runs on cuda) [ 22%] 2024-12-18T00:40:05.9364110Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_float64 SKIPPED [0.0159s] (Only runs on cuda) [ 22%] 2024-12-18T00:40:05.9364960Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int16 SKIPPED [0.0150s] (Only runs on cuda) [ 22%] 2024-12-18T00:40:05.9365800Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int32 SKIPPED [0.0147s] (Only runs on cuda) [ 22%] 2024-12-18T00:40:05.9366649Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int64 SKIPPED [0.0150s] (Only runs on cuda) [ 23%] 2024-12-18T00:40:05.9367492Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_int8 SKIPPED [0.0159s] (Only runs on cuda) [ 23%] 2024-12-18T00:40:05.9368318Z test_reductions.py::TestReductionsCPU::test_mode_large_cpu_uint8 SKIPPED [0.0148s] (Only runs on cuda) [ 23%] 2024-12-18T00:40:05.9369172Z test_reductions.py::TestReductionsCPU::test_mode_wrong_device_cpu SKIPPED [0.0148s] (Only runs on cuda) [ 23%] 2024-12-18T00:40:05.9370107Z test_reductions.py::TestReductionsCPU::test_mode_wrong_dtype_cpu SKIPPED [0.0147s] (test doesn't currently work with dynamo) [ 23%] 2024-12-18T00:40:05.9371197Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_bfloat16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9372347Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9373497Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9374638Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9375774Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9376893Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9378021Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9379151Z test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9380401Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9381614Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9382797Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9383995Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amax_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9385193Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9386452Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9387651Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9388852Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_amin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9390105Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9391528Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9392866Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9394170Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9395477Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9396840Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9398264Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9399482Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9400709Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9401915Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9403112Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9404301Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_mean_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9405505Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_bfloat16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9406819Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9408043Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9409255Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9410452Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_float32 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9411656Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_prod_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9412945Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9414152Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9415364Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_complex64 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9416565Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9417741Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9418936Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9420132Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_bfloat16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 23%] 2024-12-18T00:40:05.9421340Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_complex128 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9422552Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9423753Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9424945Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_float32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9426142Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_sum_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9427335Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9428528Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9429746Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9430943Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9432133Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9433382Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9434560Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_bfloat16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9435801Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9436962Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9438111Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amax_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9439336Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_bfloat16 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9440502Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9441658Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9442824Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_amin_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9444042Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9445337Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9446640Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9447919Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9449186Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9450449Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9451685Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9452914Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9454129Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9455346Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amax_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9456560Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9457779Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9459055Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9460261Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_amin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9461502Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9462776Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9464063Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_complex64 SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9465336Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9466660Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9467916Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_logsumexp_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9469153Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9470378Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9471614Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9472840Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9474037Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9475248Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_mean_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9476531Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9477739Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 24%] 2024-12-18T00:40:05.9478949Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9480162Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_norm_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9481373Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9482603Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9483835Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9485042Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9486324Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9487536Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9488749Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9489964Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9491278Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9492554Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9493756Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9494953Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_std_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9496148Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9497369Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9498749Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_complex64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9499977Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9501189Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9502399Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9503614Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9504837Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9506067Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9507277Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9508478Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9509031Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_var_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9509559Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9510109Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9510740Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_complex64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9511270Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9511788Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9512319Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9512844Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9513471Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9514000Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9514529Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9515047Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_float32 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9515574Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9516162Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9516701Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9517238Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_complex64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9517755Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9518287Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9518805Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9519378Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9519946Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 25%] 2024-12-18T00:40:05.9520517Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9521065Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9521622Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9522171Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_std_unbiased_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9522758Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9523285Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9523820Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_complex64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9524337Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9524857Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9525418Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_sum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9525934Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9526470Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9526994Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9527523Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9528038Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9528572Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9529126Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9529702Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9530259Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_complex64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9530819Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9531371Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9531931Z test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_var_unbiased_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 26%] 2024-12-18T00:40:05.9532267Z test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_bool PASSED [0.0396s] [ 26%] 2024-12-18T00:40:05.9532623Z test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int16 PASSED [0.0386s] [ 26%] 2024-12-18T00:40:05.9532964Z test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int32 PASSED [0.0385s] [ 26%] 2024-12-18T00:40:05.9533303Z test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int64 PASSED [0.0393s] [ 26%] 2024-12-18T00:40:05.9533649Z test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_int8 PASSED [0.0386s] [ 26%] 2024-12-18T00:40:05.9534037Z test_reductions.py::TestReductionsCPU::test_nanmean_integral_types_cpu_uint8 PASSED [0.0389s] [ 26%] 2024-12-18T00:40:05.9534377Z test_reductions.py::TestReductionsCPU::test_nansum_complex_cpu_complex128 PASSED [0.0390s] [ 26%] 2024-12-18T00:40:05.9534696Z test_reductions.py::TestReductionsCPU::test_nansum_complex_cpu_complex64 PASSED [0.0385s] [ 26%] 2024-12-18T00:40:05.9534992Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_bfloat16 PASSED [0.0252s] [ 26%] 2024-12-18T00:40:05.9535274Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_float16 PASSED [0.0252s] [ 26%] 2024-12-18T00:40:05.9535565Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_float32 PASSED [0.0252s] [ 26%] 2024-12-18T00:40:05.9535844Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_float64 PASSED [0.0248s] [ 26%] 2024-12-18T00:40:05.9536119Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_int16 PASSED [0.0251s] [ 26%] 2024-12-18T00:40:05.9536404Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_int32 PASSED [0.0246s] [ 26%] 2024-12-18T00:40:05.9536681Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_int64 PASSED [0.0245s] [ 26%] 2024-12-18T00:40:05.9537015Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_int8 PASSED [0.0254s] [ 26%] 2024-12-18T00:40:05.9537293Z test_reductions.py::TestReductionsCPU::test_nansum_cpu_uint8 PASSED [0.0247s] [ 26%] 2024-12-18T00:40:05.9537628Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float16 PASSED [0.3154s] [ 26%] 2024-12-18T00:40:05.9537949Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float32 PASSED [0.2853s] [ 26%] 2024-12-18T00:40:05.9538277Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float64 PASSED [0.2871s] [ 26%] 2024-12-18T00:40:05.9538589Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int16 PASSED [0.1568s] [ 26%] 2024-12-18T00:40:05.9538899Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int32 PASSED [0.1394s] [ 26%] 2024-12-18T00:40:05.9539221Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int64 PASSED [0.1407s] [ 26%] 2024-12-18T00:40:05.9539532Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int8 PASSED [0.1445s] [ 26%] 2024-12-18T00:40:05.9539853Z test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_uint8 PASSED [0.1404s] [ 27%] 2024-12-18T00:40:05.9540170Z test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_float16 PASSED [0.4671s] [ 27%] 2024-12-18T00:40:05.9540496Z test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_float32 PASSED [0.4572s] [ 27%] 2024-12-18T00:40:05.9540808Z test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_float64 PASSED [0.4628s] [ 27%] 2024-12-18T00:40:05.9541133Z test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int16 PASSED [0.2860s] [ 27%] 2024-12-18T00:40:05.9541441Z test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int32 PASSED [0.2906s] [ 27%] 2024-12-18T00:40:05.9541748Z test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int64 PASSED [0.2826s] [ 27%] 2024-12-18T00:40:05.9542077Z test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_int8 PASSED [0.2884s] [ 27%] 2024-12-18T00:40:05.9542617Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_bfloat16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9543146Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9543692Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9544239Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_complex64 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9544770Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9545363Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9545887Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9546418Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9546937Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9547474Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9548036Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9548556Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_all_cpu_uint8 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9549101Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_bfloat16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9549621Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_bool SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9550165Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9550696Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float32 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9551249Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9551771Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9552307Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9552827Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9553358Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_int8 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9553881Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_uint8 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9554436Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9554953Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_bool SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9555503Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9556102Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9556649Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_float64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9557246Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int16 SKIPPED [0.0184s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9557779Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9558302Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9558818Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9559350Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amin_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9559881Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_bfloat16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9560455Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_bool SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9560998Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9561541Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9562067Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9562604Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 27%] 2024-12-18T00:40:05.9563134Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9563659Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int16 SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9564177Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9564703Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9565216Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9565741Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_any_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9566319Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_bfloat16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9566884Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_bool SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9567466Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9568042Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9568618Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9569247Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9569822Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9570380Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int16 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9570945Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9571496Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9572057Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9572659Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_uint8 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9573257Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9573853Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9574462Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9575046Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9575646Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9576231Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9576779Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9577325Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_complex128 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9577880Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9578416Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9578955Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9579484Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_mean_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9580017Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9580549Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_bool SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9581141Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9581697Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_complex64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9582225Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9582765Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9583295Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9583828Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int16 SKIPPED [0.0170s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9584401Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9584935Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9585454Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9585987Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9586515Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9587064Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_complex128 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9587605Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9588145Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9588668Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 28%] 2024-12-18T00:40:05.9589208Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_std_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9589735Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9590254Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9590808Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9591435Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9591976Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9592500Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9593035Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9593688Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9594217Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9594730Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9595257Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9595842Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9596441Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9596982Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9597529Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9598407Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9598954Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9599478Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9599998Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9600510Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9601034Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9601561Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9602065Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9602580Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9603090Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9603597Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9604090Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9604593Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9605084Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9605723Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_all_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9606241Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9606751Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9607259Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9607787Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9608298Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9608872Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9609390Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9609897Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9610407Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9610911Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amax_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9611442Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9611949Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9612475Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9612985Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9613508Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 29%] 2024-12-18T00:40:05.9614017Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9614527Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9615033Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9615544Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9616045Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9616553Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9617059Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9617633Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9618160Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9618666Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9619181Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9619685Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9620194Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9620742Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9621251Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9621746Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9622255Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_any_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9622778Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9623307Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9623840Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9624371Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9624882Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9625393Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9625921Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9626427Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9626956Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9627476Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9628005Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9628527Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9629057Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9629625Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9630146Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9630654Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9631174Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9631681Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9632245Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9632842Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_bool SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9633418Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9633975Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9634525Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9635087Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9635638Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9636255Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int16 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9636795Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9637344Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9637876Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 30%] 2024-12-18T00:40:05.9638430Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9639005Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9639597Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_complex128 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9640178Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9640764Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9641329Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9641974Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_linalg_vector_norm_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9642520Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9643074Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9643613Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9644167Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9644701Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9645306Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9645852Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9646381Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9646929Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9647471Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9648027Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9648562Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9649113Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_float64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9649643Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9650187Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9650713Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9651255Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9651785Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amin_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9652348Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9652894Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9653454Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9654052Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_float64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9654597Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9655130Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9655668Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9656211Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9656745Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9657384Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9657931Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9658490Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9659033Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9659582Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9660122Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9660667Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9661202Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9661748Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_uint8 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9662313Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9662896Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9663475Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 31%] 2024-12-18T00:40:05.9664047Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9664605Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9665181Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9665737Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9666349Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9666910Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9667456Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9668020Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9668561Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9669106Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9669706Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9670269Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9670807Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9671352Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9671885Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9672431Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9672958Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9673498Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9674019Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9674558Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9675098Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9675639Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9676262Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9676804Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9677359Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_bfloat16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9677884Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9678518Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9679065Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9679613Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9680149Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9680698Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9681226Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9681821Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9682350Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9682885Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9683413Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9683962Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9684511Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9685064Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9685600Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9686133Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9686673Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9687194Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9687737Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9688261Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9688788Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9689309Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 32%] 2024-12-18T00:40:05.9689856Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9690435Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9691015Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9691620Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9692169Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9692703Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9693252Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9693854Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9694395Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9694921Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9695453Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9695978Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9696525Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9697090Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9697633Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9698376Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9698912Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9699459Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9699992Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9700532Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9701055Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9701585Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9702109Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9702736Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9703262Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9703796Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9704305Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9704826Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9705333Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_mean_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9705929Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9706467Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9706990Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9707526Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9708048Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9708569Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9709095Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9709625Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9710145Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9710669Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9711179Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9711703Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9712212Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9712734Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9713245Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9713757Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9714280Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_complex128 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 33%] 2024-12-18T00:40:05.9714860Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9715386Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9715963Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9716490Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9716993Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9717511Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9718071Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9718586Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9719088Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9719608Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9720128Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9720656Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9721170Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9721691Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9722196Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9722740Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9723306Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9723863Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9724414Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9724956Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9725506Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_std_unbiased_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9726015Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9726525Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9727103Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_complex128 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9727630Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9728133Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9728650Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9729153Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9729664Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9730219Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9730729Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9731221Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9731718Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9732241Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9732769Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_complex128 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9733300Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_complex64 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9733803Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9734323Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9734828Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9735388Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_bfloat16 SKIPPED [0.0169s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9735956Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_complex128 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9736522Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_complex64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9737066Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9737623Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_float32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9738163Z test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_unbiased_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9738798Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 34%] 2024-12-18T00:40:05.9739336Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_bool SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9739916Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_complex128 SKIPPED [0.0173s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9740474Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9741043Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9741596Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9742196Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_float64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9742751Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9743293Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9743850Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9744385Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_int8 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9744951Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_all_cpu_uint8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9745508Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9746062Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9746615Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_float16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9747181Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9747733Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9748301Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9748844Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int32 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9749399Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9749938Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9750492Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9751141Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_bfloat16 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9751694Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9752244Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9752796Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9753362Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9753905Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9754510Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9755053Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9755605Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9756225Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9756795Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9757337Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9757915Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_complex128 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9758473Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9759033Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9759580Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9760148Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9760687Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9761238Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9761775Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9762329Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9762871Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9763529Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9764122Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9764735Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_complex128 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 35%] 2024-12-18T00:40:05.9765356Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9765950Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9766613Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9767207Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_float64 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9767804Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9768389Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9768986Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9769575Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9770173Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_count_nonzero_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9770787Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9771423Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9772044Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9772671Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9773275Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9773891Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9774450Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9775036Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9775657Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9776223Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9776775Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9777340Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_mean_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9777899Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9778442Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9779076Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9779644Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9780211Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9780761Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9781328Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9781881Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9782440Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9782985Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9783537Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9784084Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9784646Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9785220Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9785788Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9786335Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9786897Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9787448Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_std_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9788146Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9788681Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9789241Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9789812Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9790362Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9790984Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9791604Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9792151Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 36%] 2024-12-18T00:40:05.9792689Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9793242Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9793775Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9794336Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9794889Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_bfloat16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9795462Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9796081Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9796646Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9797207Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9797771Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9798428Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9798962Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9799513Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9800057Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9800703Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9801234Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9801781Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9802305Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9802847Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int32 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9803374Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9803969Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9804495Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9805047Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9805571Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9806115Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9806655Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9807201Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_float64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9807727Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9808266Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9808794Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9809315Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9809860Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9810399Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9810933Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9811465Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9812009Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9812602Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9813147Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9813671Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9814214Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9814737Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9815273Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9815854Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9816386Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9816928Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9817485Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 37%] 2024-12-18T00:40:05.9818013Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9818559Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9819090Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9819609Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9820143Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9820664Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9821193Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int8 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9821721Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9822284Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9822825Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9823382Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9823925Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9824478Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9825068Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9825615Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9826146Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9826693Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9827245Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9827869Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9828414Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9828958Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9829509Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9830046Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9830594Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9831131Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9831688Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9832266Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9832842Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9833429Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9834033Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9834607Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_float16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9835191Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9835829Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9836407Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9837028Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9837607Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9838166Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9838742Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9839342Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9839946Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9840614Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9841204Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9841803Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9842389Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9842970Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9843540Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 38%] 2024-12-18T00:40:05.9844116Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9844679Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9845246Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9845802Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9846373Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9846921Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9847487Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9848057Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9848626Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_float16 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9849188Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9849814Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9850367Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9850926Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int32 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9851474Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9852024Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9852641Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9853215Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9853799Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9854374Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9854955Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9855520Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9856091Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9856650Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9857220Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9857779Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9858370Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9858947Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9859531Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9860100Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9860670Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9861227Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9861858Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9862414Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9862989Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9863579Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9864180Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9864848Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9865435Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9866032Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9866620Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9867205Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9867784Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9868380Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9868948Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9869535Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9870103Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 39%] 2024-12-18T00:40:05.9870668Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9871254Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9871840Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_complex64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9872399Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9872976Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9873537Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9874162Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9874719Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9875287Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9875939Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9876500Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_uint8 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9877087Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9877717Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9878290Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9878850Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9879429Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9879976Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9880578Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9881148Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9881719Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9882278Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9882852Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9883413Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9883977Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9884526Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9885085Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9885640Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9886211Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9886838Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9887414Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9887969Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9888523Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9889092Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9889689Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9890252Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9890801Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9891432Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9891982Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9892555Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9893107Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9893689Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9894252Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9894820Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9895375Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9895951Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9896495Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9897052Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 40%] 2024-12-18T00:40:05.9897597Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9898294Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9898945Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9899507Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9900088Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9900651Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_complex64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9901224Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9901778Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9902409Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9902957Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9903515Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9904067Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int64 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9904624Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9905178Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_var_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9905731Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9906282Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9906843Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9907380Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9907929Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9908471Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_mean_cpu_float64 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9909039Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9909588Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9910144Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9910708Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_float64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9911315Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9911861Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9912404Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9912962Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9913504Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9914048Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9914631Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9915182Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9915782Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9916337Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9916879Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9917422Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_bool SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9917971Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9918527Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9919062Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9919610Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9920147Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9920681Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9921220Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9921744Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9922282Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9922805Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 41%] 2024-12-18T00:40:05.9923351Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9923961Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9924514Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9925044Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9925586Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9926119Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9926758Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_bfloat16 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9927341Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9927933Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9928500Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9929083Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9929651Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_unbiased_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9930202Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9930723Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_bool SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9931274Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9931829Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9932359Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9932912Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9933440Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9933979Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9934502Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9935040Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9935556Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9936153Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9936688Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9937252Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9937794Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9938338Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9938918Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9939462Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9940035Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9940619Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9941211Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9941777Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9942357Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9942923Z test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9943496Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9944039Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_bool SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9944615Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9945183Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9945746Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9946299Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_float32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9946861Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9947402Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9948010Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9948555Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 42%] 2024-12-18T00:40:05.9949109Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9949653Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9950227Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9950771Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_bool SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9951392Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9951951Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9952509Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9953070Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int16 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9953617Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9954185Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9954729Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9955292Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9955923Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9956483Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9957041Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9957620Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_float32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9958177Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9958743Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9959297Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9959859Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9960461Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9961024Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9961588Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9962143Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_bool SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9962717Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9963333Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9963898Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9964448Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float32 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9965010Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9965555Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9966110Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9966660Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9967213Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9967762Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9968376Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9968956Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9969583Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9970189Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9970799Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9971393Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9972000Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9972651Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9973245Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9973831Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9974426Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9975012Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_count_nonzero_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 43%] 2024-12-18T00:40:05.9975744Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9976391Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9977016Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9977648Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9978267Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9978905Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9979474Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9980067Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9980643Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9981222Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9981784Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9982368Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9982935Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9983499Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9984076Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9984657Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9985267Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9985836Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9986392Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_float64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9986957Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9987506Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int32 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9988120Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9988664Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9989212Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9989781Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9990351Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9990927Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9991565Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9992133Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9992683Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9993253Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9993794Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9994385Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9994951Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_complex64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9995512Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9996136Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9996704Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9997311Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9998019Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9998562Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9999114Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:05.9999659Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:06.0000216Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:06.0000884Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:06.0001447Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:06.0002017Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:06.0002569Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 44%] 2024-12-18T00:40:06.0003132Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0003680Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0004216Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_bool SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0004763Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0005322Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0005854Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0006402Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0006941Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0007479Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0008000Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0008539Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0009061Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0009676Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0010223Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0010751Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_bool SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0011309Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0011848Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0012401Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0012998Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int16 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0013547Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0014076Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0014618Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0015150Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amax_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0015712Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0016249Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0016801Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0017336Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0017884Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0018415Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0018965Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0019493Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0020019Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0020557Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_amin_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0021095Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0021643Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0022245Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0022801Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0023337Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0023888Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0024427Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0025021Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0025546Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0026083Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0026604Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0027147Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0027698Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 45%] 2024-12-18T00:40:06.0028269Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0028816Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0029382Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0029922Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0030461Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0031021Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0031557Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0032112Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0032663Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0033230Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0033776Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0034399Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0034940Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0035498Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0036117Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0036669Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0037261Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0037856Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0038418Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0039022Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0039612Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_complex64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0040198Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0040779Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0041353Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0041926Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0042490Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0043064Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0043631Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0044204Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0044802Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0045422Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0046024Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0046688Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0047277Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0047881Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_linalg_vector_norm_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0048449Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0049024Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0049636Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0050209Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0050763Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0051331Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0051887Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0052449Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0053010Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0053581Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0054157Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 46%] 2024-12-18T00:40:06.0054721Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0055298Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0055858Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0056427Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0056984Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0057551Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0058108Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_amin_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0058702Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0059327Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0059914Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0060491Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0061068Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0061634Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0062274Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0062834Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0063410Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0063989Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0064579Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0065162Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0065733Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0066310Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0066881Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0067457Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0068029Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0068606Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmin_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0069200Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0069817Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0070414Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0071019Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0071663Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0072265Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0072844Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0073434Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0074014Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0074653Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0075234Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0075894Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0076461Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0077055Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0077638Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0078201Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0078776Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0079340Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0079910Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0080469Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0081043Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 47%] 2024-12-18T00:40:06.0081593Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0082160Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_uint8 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0082734Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0083316Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0083940Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0084520Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0085094Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0085661Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0086245Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0086890Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0087459Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0088037Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0088602Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0089172Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0089725Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0090288Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0090855Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0091491Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0092074Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0092651Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0093241Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0093800Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0094366Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0094927Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0095489Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0096099Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int32 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0096661Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0097208Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0097770Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0098506Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0099070Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0099737Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0100321Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0100876Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0101448Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0102005Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0102566Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0103132Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0103683Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0104242Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0104791Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0105378Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0105951Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0106532Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0107090Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0107668Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_float32 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0108230Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 48%] 2024-12-18T00:40:06.0108880Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0109434Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0109997Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0110544Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0111108Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0111704Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0112267Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0112814Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0113353Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0113908Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0114449Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0115023Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0115573Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0116212Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0116769Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0117335Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0117883Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0118447Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0126135Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0126782Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0127335Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0127896Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0128565Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0129123Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0129665Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0130225Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_bfloat16 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0130751Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0131379Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0131928Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0132476Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0133013Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0133566Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0134098Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0134647Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0135175Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0135701Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0136248Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0136789Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0137356Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0137898Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0138449Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0138989Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0139538Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0140114Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0140769Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0141347Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 49%] 2024-12-18T00:40:06.0141933Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0142504Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0143089Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0143686Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0144225Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0144773Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0145330Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0145865Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0146402Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float32 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0146956Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0147479Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0148019Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0148546Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0149082Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0149612Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0150163Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0150712Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0151268Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0151805Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0152352Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0152939Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0153527Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0154119Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0154710Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0155277Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0156023Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0156614Z test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0157174Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_bfloat16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0157732Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0158304Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0158882Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0159441Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0160009Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0160566Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0161126Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0161673Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0162234Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0162775Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0163335Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0163898Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0164459Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0165088Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0165656Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0166211Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0166773Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0167325Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 50%] 2024-12-18T00:40:06.0167872Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0168487Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0169038Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0169619Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0170166Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0170737Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0171299Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0171867Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0172420Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0172983Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0173536Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0174095Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0174650Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amin_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0175228Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0175773Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0176359Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0176921Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_complex64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0177537Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0178085Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0178638Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0179197Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0179741Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0180422Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0180962Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0181522Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_any_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0182123Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0182723Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0183330Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0183955Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0184548Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_float16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0185161Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0185757Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0186352Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0186940Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0187535Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0188118Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0188715Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0189330Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0190030Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0190651Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0191386Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0192000Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0192613Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0193253Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0193831Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0194416Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 51%] 2024-12-18T00:40:06.0194978Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0195549Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0196183Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0196764Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0197317Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0198043Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0198621Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0199203Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0199769Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0200347Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0200902Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0201469Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0202025Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0202700Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0203256Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0203829Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0204402Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0204968Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0205615Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0206168Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0206735Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0207293Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0207851Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0208420Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0209010Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0209564Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0210132Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0210687Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0211243Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0211797Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0212352Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0212891Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int8 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0213447Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0214000Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0214635Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0215196Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0215759Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0216309Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0216865Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0217416Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0217988Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0218551Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0219093Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0219640Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0220175Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0220732Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0221257Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 52%] 2024-12-18T00:40:06.0221795Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0222319Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0222859Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0223384Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0223948Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0224476Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0225027Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0225565Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0226102Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0226645Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0227227Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0227769Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0228292Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0228833Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0229379Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0229972Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0230516Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0231068Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0231614Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0232158Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0232690Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0233243Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0233770Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0234315Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0234857Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0235383Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0236028Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0236579Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0237133Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0237671Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0238220Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0238747Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0239350Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int32 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0239875Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0240412Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0240938Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_uint8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0241505Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_bfloat16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0242055Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0242665Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0243217Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0243770Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0244310Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0244861Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0245402Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0245942Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0246508Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 53%] 2024-12-18T00:40:06.0247055Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0247615Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0248161Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0248716Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0249255Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0249808Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0250342Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0250891Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0251528Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0252102Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0252699Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0253300Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0253873Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0254532Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0255107Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_float64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0255686Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0256251Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0256813Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0257388Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int8 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0257959Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0258571Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0259173Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0259791Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_complex64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0260384Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0260998Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0261589Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0262179Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_bfloat16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0262746Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0263319Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0263932Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0264500Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0265059Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0265625Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0266176Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0266791Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_uint8 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0267363Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0267939Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0268503Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0269080Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0269634Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0270204Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0270757Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0271318Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int8 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0271871Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_uint8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0272447Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0273035Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 54%] 2024-12-18T00:40:06.0273605Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float32 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0274196Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0274760Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0275336Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0276028Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0276600Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0277161Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0277750Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0278326Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0278908Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0279535Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0280109Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0280675Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int32 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0281246Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0281803Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0282386Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0282978Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_bfloat16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0283593Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0284190Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_complex64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0284783Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0285390Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0285979Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0286574Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0287151Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0287745Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int64 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0288373Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0288971Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_uint8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0289542Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0290111Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_bool SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0290697Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_complex128 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0291370Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0291996Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0292572Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_float32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0293136Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0293704Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0294263Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0294835Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int64 SKIPPED [0.0172s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0295386Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0295955Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0296523Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0297088Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_float16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0297665Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0298411Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0298994Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0299549Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_bool SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0300142Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 55%] 2024-12-18T00:40:06.0300721Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0301417Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0301980Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0302559Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0303123Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0303703Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0304328Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0304903Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_int8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0305462Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0306042Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0306622Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0307210Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0307779Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0308358Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0308918Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0309472Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0310040Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0310596Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0311154Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0311707Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0312281Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0312831Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0313476Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0314047Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0314620Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0315177Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0315824Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0316381Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0317003Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0317553Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0318114Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0318665Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0319242Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0319830Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0320412Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0320973Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0321551Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0322110Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0322683Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0323235Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0323798Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0324344Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0324893Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0325454Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0326061Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0326621Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0327162Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 56%] 2024-12-18T00:40:06.0327720Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0328263Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0328891Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0329446Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0330007Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0330560Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0331124Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0331664Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0332233Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0332783Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0333347Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0333886Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0334440Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0334982Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0335516Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0336069Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0336621Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0337159Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0337714Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0338330Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0338871Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0339429Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0339970Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0340515Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0341101Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0341651Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0342184Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0342732Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0343269Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0343827Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0344375Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0344906Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0345449Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0345981Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_float64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0346565Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0347157Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0347747Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0348318Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0348899Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0349467Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_float64 SKIPPED [0.0175s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0350018Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_bfloat16 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0350607Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_bool SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0351169Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_complex128 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0351718Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_complex64 SKIPPED [0.0170s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0352265Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_float16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0352802Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 57%] 2024-12-18T00:40:06.0353401Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0353925Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0354465Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0354990Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0355515Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_int8 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0356124Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_sum_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0356670Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0357229Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_complex128 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0357775Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0358320Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0358852Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0359404Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0359976Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0360573Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0361152Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0361732Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0362297Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0362937Z test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0363499Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0364055Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0364626Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0365207Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0365816Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0366377Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0366950Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0367501Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0368068Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0368627Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0369186Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0369737Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0370318Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0370874Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0371446Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0372015Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0372590Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0373147Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0373712Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0374266Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0374947Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0375500Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0376083Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0376634Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_bool SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0377213Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0377774Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_float32 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0378390Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0378959Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 58%] 2024-12-18T00:40:06.0379512Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0380079Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0380630Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0381201Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0381763Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0382322Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0382895Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0383480Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0384042Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0384608Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0385165Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0385725Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0386275Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0386835Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0387427Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0387985Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0388593Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0389180Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0389807Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0390476Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0391155Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0391763Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0392374Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0392963Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0393570Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0394158Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0394756Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0395345Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_count_nonzero_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0396047Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0396685Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0397326Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0398090Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0398726Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0399338Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0400027Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0400603Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_complex128 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0401190Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0401754Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0402331Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0402957Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0403540Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0404092Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0404670Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0405259Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_complex64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0405821Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0406402Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 59%] 2024-12-18T00:40:06.0406963Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0407527Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0408083Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0408651Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0409214Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0409784Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0410350Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0410943Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0411515Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_complex64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0412141Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0412700Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0413271Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0413835Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0414397Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0414973Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0415607Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0416166Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0416721Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0417288Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0417837Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0418406Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0418954Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0419511Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0420061Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0420640Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0421215Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0421804Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0422360Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0422931Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0423491Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0424048Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0424633Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0425201Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0425748Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0426303Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_float16 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0426842Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0427435Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0427981Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0428513Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0429058Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0429587Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0430132Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0430688Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0431234Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0431776Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0432332Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_float32 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0432873Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 60%] 2024-12-18T00:40:06.0433427Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0433965Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0434510Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0435039Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0435589Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0436197Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0436808Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0437351Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0437894Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0438451Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0438984Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0439532Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0440136Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0440681Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0441216Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0441770Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0442300Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0442872Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0443423Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0443979Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0444527Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0445084Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0445617Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0446171Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0446702Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0447231Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0447776Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0448338Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0448960Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float16 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0449508Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0450071Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0450615Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0451167Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0451708Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0452351Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0452893Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0453467Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0454018Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0454585Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0455150Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0455707Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0456249Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0456802Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0457341Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0457883Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0458489Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 61%] 2024-12-18T00:40:06.0459055Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_bool SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0459660Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_complex128 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0460249Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0460842Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0461477Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0462068Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0462638Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0463220Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0463789Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0464414Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0464984Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0465597Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0466211Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0466828Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0467433Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0468042Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0468637Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0469227Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0469796Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0470376Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0470950Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0471510Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0472087Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0472647Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0473214Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0473821Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0474404Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0474973Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0475556Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0476227Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0476872Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0477435Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0478007Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0478566Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0479137Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0479724Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0480319Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0480896Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0481485Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0482057Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0482639Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0483216Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0483781Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0484362Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0484947Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0485535Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 62%] 2024-12-18T00:40:06.0486178Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0486768Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0487337Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0487915Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0488482Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0489108Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0489676Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmin_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0490288Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0490897Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0491598Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0492193Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0492808Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0493403Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0494000Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0494582Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0495176Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0495765Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0496349Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0496932Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0497490Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_bool SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0498240Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0498918Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0499499Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0500066Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0500645Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0501210Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0501848Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0502413Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0502980Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0503539Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0504126Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0504692Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0505284Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0505856Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_norm_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0506442Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0506999Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0507603Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0508194Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0508769Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0509350Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0509918Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0510491Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0511105Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0511679Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0512235Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0512810Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 63%] 2024-12-18T00:40:06.0513378Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0513970Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0514600Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0515176Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0515806Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0516388Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0516947Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0517526Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0518088Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0518673Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0519232Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0519821Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0520386Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0520970Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0521564Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0522134Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0522715Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0523285Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0523922Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0524481Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0525050Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0525603Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0526174Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0526796Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0527391Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0527967Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0528546Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0529112Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0529701Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0530259Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0530830Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0531385Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0531952Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0532513Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0533071Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0533650Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0534205Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0534762Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0535306Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0535926Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0536491Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0537066Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0537623Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0538189Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0538746Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0539354Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0539905Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 64%] 2024-12-18T00:40:06.0540468Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0541018Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0541578Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0542127Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0542682Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0543219Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int8 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0543760Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0544319Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0544857Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0545438Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0545989Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0546543Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0547083Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0547641Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0548249Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0548800Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0549338Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0549882Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_int8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0550422Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0550980Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0551586Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0552150Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0552691Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0553233Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0553787Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0554378Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_bfloat16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0554987Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0555577Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0556235Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0556815Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0557404Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0557956Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0558495Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0559046Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0559612Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0560152Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0560762Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0561300Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0561842Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0562374Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0562917Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0563442Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0564027Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0564583Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0565135Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0565697Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 65%] 2024-12-18T00:40:06.0566242Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0566803Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0567341Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0567932Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0568522Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0569118Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0569699Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0570289Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_float32 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0570862Z test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0571169Z test_reductions.py::TestReductionsCPU::test_numpy_named_args_cpu PASSED [0.0985s] [ 66%] 2024-12-18T00:40:06.0571438Z test_reductions.py::TestReductionsCPU::test_prod_bool_cpu PASSED [0.0216s] [ 66%] 2024-12-18T00:40:06.0571726Z test_reductions.py::TestReductionsCPU::test_prod_cpu_float32 PASSED [0.0727s] [ 66%] 2024-12-18T00:40:06.0572090Z test_reductions.py::TestReductionsCPU::test_prod_gpu_cpu_float16 SKIPPED [0.0148s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0572567Z test_reductions.py::TestReductionsCPU::test_prod_gpu_cpu_float32 SKIPPED [0.0146s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0573270Z test_reductions.py::TestReductionsCPU::test_prod_integer_upcast_cpu W1217 23:57:42.953000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.0573798Z W1217 23:57:42.953000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:954) 2024-12-18T00:40:06.0574308Z W1217 23:57:42.953000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/2: tensor 'L['x']' dtype mismatch. expected Long, actual Char 2024-12-18T00:40:06.0574770Z W1217 23:57:42.953000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.0575450Z W1217 23:57:42.953000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.0575974Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] failed while attempting to run meta for aten.prod.int_out 2024-12-18T00:40:06.0576366Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] Traceback (most recent call last): 2024-12-18T00:40:06.0577102Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:06.0577469Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] r = func(*args, **kwargs) 2024-12-18T00:40:06.0578073Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:06.0578471Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] return self._op(*args, **kwargs) 2024-12-18T00:40:06.0578833Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.0579474Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2381, in prod 2024-12-18T00:40:06.0579813Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] return _reduction( 2024-12-18T00:40:06.0580098Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] a, 2024-12-18T00:40:06.0580423Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ...<5 lines>... 2024-12-18T00:40:06.0580864Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, 2024-12-18T00:40:06.0581152Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ) 2024-12-18T00:40:06.0581812Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2210, in _reduction 2024-12-18T00:40:06.0582163Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] raise RuntimeError( 2024-12-18T00:40:06.0582613Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] "dtype argument and out dtype must match in reduction" 2024-12-18T00:40:06.0582889Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ) 2024-12-18T00:40:06.0583385Z E1217 23:57:43.239000 904 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] RuntimeError: dtype argument and out dtype must match in reduction 2024-12-18T00:40:06.0583547Z PASSED [0.4308s] [ 66%] 2024-12-18T00:40:06.0583855Z test_reductions.py::TestReductionsCPU::test_prod_lowp_cpu_bfloat16 PASSED [0.1123s] [ 66%] 2024-12-18T00:40:06.0584147Z test_reductions.py::TestReductionsCPU::test_prod_lowp_cpu_float16 PASSED [0.1103s] [ 66%] 2024-12-18T00:40:06.0584453Z test_reductions.py::TestReductionsCPU::test_quantile_backward_cpu PASSED [0.2784s] [ 66%] 2024-12-18T00:40:06.0585034Z test_reductions.py::TestReductionsCPU::test_quantile_cpu_float32 SKIPPED [0.0159s] (https://github.com/pytorch/pytorch/pull/138657 discovers a latent bug) [ 66%] 2024-12-18T00:40:06.0585623Z test_reductions.py::TestReductionsCPU::test_quantile_cpu_float64 SKIPPED [0.0151s] (https://github.com/pytorch/pytorch/pull/138657 discovers a latent bug) [ 66%] 2024-12-18T00:40:06.0586279Z test_reductions.py::TestReductionsCPU::test_quantile_error_cpu W1217 23:57:45.146000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.0586858Z W1217 23:57:45.146000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: 'check' (/var/lib/jenkins/workspace/test/test_reductions.py:2680) 2024-12-18T00:40:06.0587350Z W1217 23:57:45.146000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: L['message'] == 'input tensor must be non-empty' 2024-12-18T00:40:06.0587818Z W1217 23:57:45.146000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.0588459Z W1217 23:57:45.146000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.0588574Z PASSED [1.3609s] [ 66%] 2024-12-18T00:40:06.0588860Z test_reductions.py::TestReductionsCPU::test_reduce_dtype_cpu PASSED [1.0493s] [ 66%] 2024-12-18T00:40:06.0589192Z test_reductions.py::TestReductionsCPU::test_reduction_empty_any_all_cpu PASSED [0.0611s] [ 66%] 2024-12-18T00:40:06.0589563Z test_reductions.py::TestReductionsCPU::test_reduction_split_cpu SKIPPED [0.0155s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0590052Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_bfloat16 SKIPPED [0.0152s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0590546Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_float16 SKIPPED [0.0151s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0591063Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_float32 SKIPPED [0.0157s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0591605Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_input_corner_cpu_float64 SKIPPED [0.0150s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0592070Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_bfloat16 SKIPPED [0.0150s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0592548Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_float16 SKIPPED [0.0150s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0593009Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_float32 SKIPPED [0.0187s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0593478Z test_reductions.py::TestReductionsCPU::test_reduction_vectorize_along_output_cpu_float64 SKIPPED [0.0152s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0593927Z test_reductions.py::TestReductionsCPU::test_reductions_large_half_tensors_cpu_bfloat16 SKIPPED [0.0150s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0594393Z test_reductions.py::TestReductionsCPU::test_reductions_large_half_tensors_cpu_complex32 SKIPPED [0.0149s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0594834Z test_reductions.py::TestReductionsCPU::test_reductions_large_half_tensors_cpu_float16 SKIPPED [0.0157s] (Only runs on cuda) [ 66%] 2024-12-18T00:40:06.0595370Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0596052Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0596600Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0597145Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0597680Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0598373Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0598987Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0599529Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0600050Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0600582Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0601105Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_all_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 66%] 2024-12-18T00:40:06.0601643Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_bool SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0602188Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0602738Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0603273Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0603809Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0604333Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0604878Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0605401Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0605930Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amax_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0606467Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0607003Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0607617Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0608154Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0608696Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0609223Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0609760Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0610284Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0610876Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0611398Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0611954Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0612495Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_complex64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0613037Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0613572Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0614123Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0614648Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0615184Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0615706Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0616222Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0616764Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_any_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0617333Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0617940Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0618526Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0619118Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_float16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0619747Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0620335Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0620903Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0621488Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0622054Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0622637Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0623268Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_count_nonzero_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0623842Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0624395Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0624950Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0625487Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0626050Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0626580Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 67%] 2024-12-18T00:40:06.0627149Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0627697Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0628231Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0628784Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0629322Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0629861Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0630394Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int32 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0630933Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0631455Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0632045Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_prod_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0632592Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0633146Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0633561Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_float16 SKIPPED [0.0124s] (Skipped!) [ 68%] 2024-12-18T00:40:06.0634105Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0634688Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_std_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0635221Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0635841Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0636396Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0636808Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_float16 SKIPPED [0.0129s] (Skipped!) [ 68%] 2024-12-18T00:40:06.0637337Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0637888Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0638411Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0638949Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0639469Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0639998Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0640519Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0641084Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_complex128 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0641625Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0642172Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0642703Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0643243Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_var_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0643806Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0644345Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0644870Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0645397Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_float16 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0645909Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0646430Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0646988Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0647493Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0648013Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0648510Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0649023Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0649535Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_bool SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0650064Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0650580Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0651111Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0651622Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 68%] 2024-12-18T00:40:06.0652147Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0652674Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0653192Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0653706Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_uint8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0654227Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0654745Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0655278Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0655859Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0656370Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0656893Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0657403Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0657922Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0658439Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amin_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0659004Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0659534Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0660070Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0660580Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0661103Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0661622Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0662142Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0662644Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0663158Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0663658Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0664174Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0664708Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0665233Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0665769Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0666283Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0666816Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0667398Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0667922Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0668436Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_uint8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0668970Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0669496Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0670031Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0670603Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int16 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0671131Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0671644Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0672174Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0672691Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0673246Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0673819Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0674382Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0674945Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0675499Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0676128Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 69%] 2024-12-18T00:40:06.0676681Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0677237Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0677779Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0678329Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0678870Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_count_nonzero_cpu_uint8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0679732Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_float16 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0680274Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0680828Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0681358Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0681903Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0682434Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0683033Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0683569Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0684120Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_float16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0684659Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0685197Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0685749Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0686280Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int32 SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0686823Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0687348Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0687895Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0688446Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_float16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0689015Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0689566Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0690119Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0690662Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int32 SKIPPED [0.0168s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0691306Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0691911Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0692467Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_cpu_uint8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0693017Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_float16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0693583Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0694135Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0694689Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0695288Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int32 SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0695842Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0696379Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0696924Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmin_cpu_uint8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0697469Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_bool SKIPPED [0.0175s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0698197Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0698762Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_complex64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0699304Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0699856Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_float32 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0700395Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_float64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0700950Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0701482Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0702026Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int64 SKIPPED [0.0169s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 70%] 2024-12-18T00:40:06.0702555Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int8 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0703107Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_uint8 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0703638Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_bool SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0704319Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_complex128 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0704872Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_complex64 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0705431Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_float16 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0705973Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_float32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0706533Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_float64 SKIPPED [0.0169s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0707133Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0707668Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0708214Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int64 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0708743Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_int8 SKIPPED [0.0173s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0709291Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_prod_cpu_uint8 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0709844Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_complex128 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0710412Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_complex64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0710952Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_float16 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0711507Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0712044Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0712591Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0713132Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0713673Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0714198Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0714743Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0715269Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_bool SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0715896Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0716519Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0717071Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0717608Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0718150Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0718693Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0719274Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0719820Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0720346Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0720889Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_sum_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0721442Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0722006Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_complex64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0722548Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0723099Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0723640Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0724187Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int16 SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0724722Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0725269Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0725801Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0726344Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_uint8 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0726879Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0727418Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 71%] 2024-12-18T00:40:06.0727941Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0728515Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0729043Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_mean_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0729575Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nanmean_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0730115Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nanmean_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0730645Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nanmean_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0731226Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0731751Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0732287Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0732812Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0733340Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0733856Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0734393Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0734905Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0735433Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0735939Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0736487Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0736904Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_complex64 SKIPPED [0.0130s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0737303Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_float16 SKIPPED [0.0124s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0737834Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0738353Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0738879Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0739387Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0739962Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0740468Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0740875Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_prod_cpu_uint8 SKIPPED [0.0132s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0741404Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0741941Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0742333Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_float16 SKIPPED [0.0124s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0742864Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0743424Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_std_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0743923Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0744462Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0744983Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0745384Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_float16 SKIPPED [0.0123s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0745902Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0746424Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0746926Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0747444Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0747945Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0748455Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0748968Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_sum_cpu_uint8 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 72%] 2024-12-18T00:40:06.0749384Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_complex128 SKIPPED [0.0122s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0749785Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_complex64 SKIPPED [0.0122s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0750188Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_float16 SKIPPED [0.0122s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0750578Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_float32 SKIPPED [0.0130s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0750969Z test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_var_cpu_float64 SKIPPED [0.0124s] (Skipped!) [ 72%] 2024-12-18T00:40:06.0751523Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_all_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0752100Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_all_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0752647Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_amax_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0753177Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_amin_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0753727Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_any_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0754254Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_any_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0754975Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_count_nonzero_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0755549Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_count_nonzero_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0756187Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_mean_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0756721Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_mean_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0757282Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_prod_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0757827Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_prod_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0758379Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_std_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0758908Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_std_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0759460Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_sum_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0759985Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_sum_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0760540Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_var_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0761074Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values__refs_var_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0761599Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_all_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0762116Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_all_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0762625Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_amax_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0763149Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_amin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0763713Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_any_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0764236Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_any_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0764758Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_argmax_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0765290Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_argmin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0765846Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_count_nonzero_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0766400Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_count_nonzero_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0766986Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_amax_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0767537Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_amin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0768079Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_argmax_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0768634Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_argmin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0769179Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_mean_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0769733Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_mean_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0770284Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_prod_cpu_complex64 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0770817Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_prod_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0771370Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_std_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0771904Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_std_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0772461Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_sum_cpu_complex64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0772998Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_sum_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0773554Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_var_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0774084Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_var_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0774614Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_mean_cpu_complex64 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0775124Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_mean_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0775712Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_nanmean_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0776232Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_nansum_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 73%] 2024-12-18T00:40:06.0776763Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_prod_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0777274Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_prod_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0777803Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_std_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0778316Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_std_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0778895Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_sum_cpu_complex64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0779404Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_sum_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0779928Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_var_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0780433Z test_reductions.py::TestReductionsCPU::test_ref_extremal_values_var_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0780951Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_all_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0781480Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_amax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0782007Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_amin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0782531Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_any_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0783085Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_count_nonzero_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0783617Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_mean_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0784132Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_prod_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0784667Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_std_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0785180Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_sum_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0785707Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D__refs_var_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0786207Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_all_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0786728Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_amax_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0787231Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_amin_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0787793Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_any_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0788304Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_argmax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0788824Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_argmin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0789360Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_count_nonzero_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0789886Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_amax_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0790471Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_amin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0791027Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_argmax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0791631Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_argmin_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0792156Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_mean_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0792696Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_prod_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0793218Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_std_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0793758Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_sum_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0794282Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_masked_var_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0794795Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_mean_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0795310Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_nanmean_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0795895Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_nansum_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0796407Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_prod_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0796917Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_std_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0797412Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_sum_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0798049Z test_reductions.py::TestReductionsCPU::test_ref_large_input_1D_var_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0798568Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_all_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0799103Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_amax_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0799718Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_amin_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0800233Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_any_cpu_float64 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0800804Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_count_nonzero_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 74%] 2024-12-18T00:40:06.0801326Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_mean_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0801859Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_prod_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0802454Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_std_cpu_float64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0802983Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_sum_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0803495Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D__refs_var_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0804005Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_all_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0804507Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_amax_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0805024Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_amin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0805526Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_any_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0806049Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_argmax_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0806559Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_argmin_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0807111Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_count_nonzero_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0807640Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_amax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0808178Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_amin_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0808721Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_argmax_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0809260Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_argmin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0809801Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_mean_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0810329Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_prod_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0810869Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_std_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0811449Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_sum_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0811977Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_masked_var_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0812478Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_mean_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0813006Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_nanmean_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0813516Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_nansum_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0814032Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0814579Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_std_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0815083Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_sum_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0815578Z test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_var_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0816161Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_all_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0816737Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_amax_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0817327Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_amin_cpu_float64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0817898Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_any_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0818515Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_count_nonzero_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0819083Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_mean_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0819653Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_prod_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0820237Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_std_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0820799Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_sum_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0821374Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing__refs_var_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0821921Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_all_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0822480Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_amax_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0823026Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_amin_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0823645Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_any_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0824201Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_argmax_cpu_float64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0824771Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_argmin_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0825354Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_count_nonzero_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0825947Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_amax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 75%] 2024-12-18T00:40:06.0826577Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_amin_cpu_float64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0827177Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_argmax_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0827764Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_argmin_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0828355Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_mean_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0828936Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_prod_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0829528Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_std_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0830099Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_sum_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0830685Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_masked_var_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0831240Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_mean_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0831822Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_nanmean_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0832389Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_nansum_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0832937Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_prod_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0833495Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_std_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0834040Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_sum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0834593Z test_reductions.py::TestReductionsCPU::test_ref_large_input_64bit_indexing_var_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0835095Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0835754Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_complex128 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0836280Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0836806Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0837321Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0837846Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_float64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0838405Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0838920Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0839420Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0839932Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0840437Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_all_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0840958Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0841480Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0842009Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0842525Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0843034Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0843553Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0844066Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0844584Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0845091Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amax_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0845612Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_bool SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0846126Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_float16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0846655Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0847241Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0847762Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0848269Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0848787Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0849293Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0849815Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0850375Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_bool SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0850916Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0851443Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 76%] 2024-12-18T00:40:06.0851956Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0852478Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_float32 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0852993Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0853507Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0854010Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0854524Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0855023Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0855539Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_any_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0856084Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0856662Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_complex128 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0857235Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0857801Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0858352Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0858974Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0859520Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0860077Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0860623Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0861175Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_int8 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0861720Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_count_nonzero_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0862305Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0862852Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0863373Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_float16 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0863909Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0864423Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_mean_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0864949Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0865479Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0866021Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_complex64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0866537Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0867063Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0867577Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0868103Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0868612Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0869129Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0869634Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int8 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0870151Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0870731Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0871251Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0871775Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0872286Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0872808Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_std_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0873306Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0873897Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0874422Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0874946Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0875458Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0876051Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 77%] 2024-12-18T00:40:06.0876566Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0877080Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0877582Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0878096Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_int8 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0878599Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0879137Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0879667Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0880196Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_float16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0880710Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0881222Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0881721Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0882228Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_complex128 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0882803Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0883294Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0883802Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0884292Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0884787Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0885273Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0885821Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0886303Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int8 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0886797Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0887282Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0887791Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0888297Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0888792Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0889294Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0889792Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0890292Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0890777Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0891362Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0891851Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0892363Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0892858Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0893372Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0893861Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0894434Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0894926Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0895423Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0895910Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0896408Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_bool SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0896916Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0897477Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0898176Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0898671Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0899179Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0899667Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 78%] 2024-12-18T00:40:06.0900169Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0900657Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0901152Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0901633Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_any_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0902158Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0902666Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_float32 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0903197Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0903697Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0904202Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0904700Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0905198Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0905709Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmax_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0906320Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0906836Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0907338Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0907845Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0908342Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0908858Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0909416Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0909925Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0910447Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0911011Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_complex128 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0911554Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0912103Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0912632Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0913175Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0913697Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0914222Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0914757Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0915283Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_int8 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0915861Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_count_nonzero_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0916385Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0916918Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0917442Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_float64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0918025Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0918535Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0919059Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0919573Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0920099Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0920621Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0921208Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0921733Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_float64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0922259Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0922773Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0923285Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0923813Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_int8 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 79%] 2024-12-18T00:40:06.0924334Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amin_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0924879Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0925414Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0925962Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0926483Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0927024Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0927543Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0928081Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_int8 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0928607Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmax_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0929156Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0929690Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0930293Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0930817Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0931355Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0931883Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0932418Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_int8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0933008Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_argmin_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0933525Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0934080Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0934614Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0935152Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0935672Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0936211Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0936725Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0937248Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0937762Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0938285Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0938806Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0939332Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0939872Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0940421Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0940943Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0941478Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0942052Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0942578Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0943092Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0943607Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0944130Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0944763Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0945313Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_complex128 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0945843Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0946377Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0946893Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0947425Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0947942Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0948464Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0948972Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:40:06.0949492Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0949999Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_std_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0950524Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0951060Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0951601Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0952120Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0952638Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0953168Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0953726Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int16 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0954248Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0954758Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0955275Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0955859Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_sum_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0956407Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0956992Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0957523Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_float16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0958044Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0958574Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0959080Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0959608Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0960126Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0960647Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0961159Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0961672Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0962195Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0962703Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0963216Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0963715Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_mean_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0964241Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nanmean_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0964760Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nanmean_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0965287Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nanmean_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0965839Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0966362Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0966867Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0967384Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0967878Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0968437Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0968938Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0969445Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0969942Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_nansum_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0970429Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_bool SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0970956Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0971471Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0971981Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0972480Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0972991Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 81%] 2024-12-18T00:40:06.0973476Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0973979Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0974476Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0974969Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0975456Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_prod_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0975972Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0976478Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0976982Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0977532Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0978028Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0978520Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_bool SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0985594Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0986197Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0986835Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0987335Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0987843Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0988328Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0988825Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0989314Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0989816Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0990298Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_sum_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0990817Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0991432Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_complex64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0991943Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0992438Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0992957Z test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0993452Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0993990Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0994510Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0995019Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0995538Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0996181Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0996695Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0997191Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0997704Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0998340Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0998958Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_all_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0999462Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.0999989Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1000502Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1001030Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1001534Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1002060Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1002565Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1003077Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1003584Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:40:06.1004086Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_bool SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1004620Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1005140Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1005664Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1006169Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1006686Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1007191Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1007777Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1008282Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1008794Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1009319Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1009849Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1010357Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_float16 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1010932Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1011437Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1011954Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1012455Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1012958Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1013470Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1013972Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_any_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1014523Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1015087Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1015661Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1016211Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1016781Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1017327Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1017878Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1018417Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1018970Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1019563Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1020112Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_count_nonzero_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1020638Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1021174Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1021692Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1022218Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1022785Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_mean_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1023289Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1023831Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1024239Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_complex64 SKIPPED [0.0122s] (Skipped!) [ 83%] 2024-12-18T00:40:06.1024649Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_float16 SKIPPED [0.0122s] (Skipped!) [ 83%] 2024-12-18T00:40:06.1025168Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1025704Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1026208Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1026728Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1027233Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1027746Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:40:06.1028255Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_prod_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1028795Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1029317Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1029724Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_float16 SKIPPED [0.0121s] (Skipped!) [ 84%] 2024-12-18T00:40:06.1030229Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1030751Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1031317Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1031840Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1032369Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1032761Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_float16 SKIPPED [0.0122s] (Skipped!) [ 84%] 2024-12-18T00:40:06.1033281Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1033787Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1034353Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1034852Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1035362Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int64 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1035967Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1036482Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_sum_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1036888Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_complex128 SKIPPED [0.0122s] (Skipped!) [ 84%] 2024-12-18T00:40:06.1037313Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_complex64 SKIPPED [0.0129s] (Skipped!) [ 84%] 2024-12-18T00:40:06.1037707Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_float16 SKIPPED [0.0121s] (Skipped!) [ 84%] 2024-12-18T00:40:06.1038103Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_float32 SKIPPED [0.0121s] (Skipped!) [ 84%] 2024-12-18T00:40:06.1038506Z test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_float64 SKIPPED [0.0122s] (Skipped!) [ 84%] 2024-12-18T00:40:06.1038989Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1039511Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1040022Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1040529Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1041022Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1041526Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1042010Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1042507Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1043052Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1043547Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1044025Z test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1044522Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1045016Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1045511Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1046070Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1046557Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1047055Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1047538Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1048035Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1048518Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1049020Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_bool SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1049515Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1050022Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1050517Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1051016Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:40:06.1051503Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1052006Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1052486Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1052985Z test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1053461Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1053964Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1054476Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1055017Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1055519Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1056007Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1056501Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1056986Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1057526Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1058000Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1058493Z test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1058997Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_float16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1059513Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1060014Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1060525Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1061023Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1061516Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1062019Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1062512Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1063027Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1063534Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1064049Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1064540Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1065047Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1065537Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1066037Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1066586Z test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1067120Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_bool SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1067667Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1068221Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1068753Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1069346Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1069880Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1070404Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1070938Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1071458Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1071990Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1072522Z test_reductions.py::TestReductionsCPU::test_ref_small_input_count_nonzero_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1073058Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1073578Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1074113Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1074628Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:40:06.1075161Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1075767Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1076292Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1076802Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amax_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1077332Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1077849Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_float32 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1078457Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1078966Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1079475Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1079993Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int64 SKIPPED [0.0168s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1080498Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_int8 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1081019Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_amin_cpu_uint8 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1081606Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_float16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1082148Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_float32 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1082679Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1083210Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1083729Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1084266Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int64 SKIPPED [0.0170s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1084780Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1085316Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmax_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1085846Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1086392Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1086920Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1087456Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1087974Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1088493Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int64 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1089020Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1089538Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_argmin_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1090113Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1090649Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1091281Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1091803Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1092333Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1092851Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1093432Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1093945Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1094468Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1094975Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1095500Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1096017Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1096569Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1097100Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1097632Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1098310Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1098833Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1099368Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:40:06.1099878Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1100407Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1100915Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_int8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1101443Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_prod_cpu_uint8 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1101976Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_complex128 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1102615Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1103128Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1103659Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_float32 SKIPPED [0.0169s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1104174Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1104697Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1105268Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1105793Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int64 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1106296Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1106818Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_uint8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1107324Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_bool SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1107852Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_complex128 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1108399Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_complex64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1108914Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_float16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1109439Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1109957Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_float64 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1110480Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1110993Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1111514Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1112015Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_int8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1112533Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_sum_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1113058Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1113596Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1114164Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_float16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1114688Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1115202Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1115780Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1116293Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1116818Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1117391Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1117918Z test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_var_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1118426Z test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_complex128 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1118929Z test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_complex64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1119317Z test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float16 SKIPPED [0.0130s] (Skipped!) [ 87%] 2024-12-18T00:40:06.1119809Z test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1120318Z test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1120706Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nanmean_cpu_float16 SKIPPED [0.0124s] (Skipped!) [ 87%] 2024-12-18T00:40:06.1121228Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nanmean_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1121734Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nanmean_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1122240Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_bool SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1122621Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_float16 SKIPPED [0.0125s] (Skipped!) [ 87%] 2024-12-18T00:40:06.1123141Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:40:06.1123642Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1124145Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int16 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1124635Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1125136Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1125676Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1126169Z test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_uint8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1126668Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1127176Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_complex128 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1127571Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_complex64 SKIPPED [0.0128s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1127942Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float16 SKIPPED [0.0132s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1128448Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1128997Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1129494Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1129980Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1130475Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1130953Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1131459Z test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1131968Z test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_complex128 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1132481Z test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1132851Z test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_float16 SKIPPED [0.0124s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1133338Z test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1133837Z test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_float64 SKIPPED [0.0171s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1134317Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1134835Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1135334Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1135713Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_float16 SKIPPED [0.0141s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1136200Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1136701Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_float64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1137274Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1137769Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1138248Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1138735Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1139215Z test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_uint8 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1139615Z test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_complex128 SKIPPED [0.0141s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1139998Z test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_complex64 SKIPPED [0.0126s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1140424Z test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_float16 SKIPPED [0.0124s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1140808Z test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_float32 SKIPPED [0.0123s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1141176Z test_reductions.py::TestReductionsCPU::test_ref_small_input_var_cpu_float64 SKIPPED [0.0131s] (Skipped!) [ 88%] 2024-12-18T00:40:06.1141731Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_bfloat16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1142269Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_float16 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1142818Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1143361Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_float64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1143899Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1144423Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1144962Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1145484Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int8 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1146025Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:40:06.1146566Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1147100Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1147640Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_float32 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1148175Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1148711Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1149280Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1149813Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1150329Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1150869Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amin_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1151413Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1151964Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_float16 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1152554Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1153105Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1153633Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1154176Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1154709Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1155256Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1155858Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1156421Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_bfloat16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1156960Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1157502Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1158058Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1158593Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int16 SKIPPED [0.0167s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1159135Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1159669Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1160212Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1160741Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmin_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1161350Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1161867Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1162421Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_complex128 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1162962Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_complex64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1163506Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1164036Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1164642Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1165170Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1165704Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1166227Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1166755Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1167283Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_uint8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1167833Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1168281Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_bool SKIPPED [0.0126s] (Failing on some jobs) [ 89%] 2024-12-18T00:40:06.1168837Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1169382Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_complex64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1169926Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1170465Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1170999Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_float64 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:40:06.1171459Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int16 SKIPPED [0.0146s] (Failing on some jobs) [ 90%] 2024-12-18T00:40:06.1171909Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int32 SKIPPED [0.0132s] (Failing on some jobs) [ 90%] 2024-12-18T00:40:06.1172451Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1172899Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int8 SKIPPED [0.0130s] (Failing on some jobs) [ 90%] 2024-12-18T00:40:06.1173493Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_uint8 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1174035Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_bfloat16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1174591Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_complex128 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1175131Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_complex64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1175677Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_float16 SKIPPED [0.0166s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1176203Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1176799Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1177326Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1177859Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int32 SKIPPED [0.0171s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1178383Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1178895Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_int8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1179431Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_std_cpu_uint8 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1179965Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_bfloat16 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1180417Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_bool SKIPPED [0.0135s] (Failing on some jobs) [ 90%] 2024-12-18T00:40:06.1180956Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_complex128 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1181506Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_complex64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1182030Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1182579Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1183103Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1183559Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int16 SKIPPED [0.0135s] (Failing on some jobs) [ 90%] 2024-12-18T00:40:06.1183996Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int32 SKIPPED [0.0150s] (Failing on some jobs) [ 90%] 2024-12-18T00:40:06.1184523Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int64 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1184957Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int8 SKIPPED [0.0129s] (Failing on some jobs) [ 90%] 2024-12-18T00:40:06.1185541Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1186074Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_bfloat16 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1186621Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_complex128 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1187154Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_complex64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1187679Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1188263Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float32 SKIPPED [0.0164s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1188788Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1189316Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1189830Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int32 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1190357Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int64 SKIPPED [0.0170s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1190872Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int8 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1191492Z test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_uint8 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1191776Z test_reductions.py::TestReductionsCPU::test_repeated_dim_cpu PASSED [0.0256s] [ 90%] 2024-12-18T00:40:06.1192292Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_bfloat16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1192779Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_bool SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1193306Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_complex128 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1193818Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_complex64 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1194338Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1194842Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:40:06.1195341Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_float64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1195911Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int16 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1196405Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1196908Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1197458Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1198109Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_uint8 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1198622Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1199130Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1199636Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1200155Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_float32 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1200744Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_float64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1201251Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1201744Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1202252Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1202743Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1203266Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1203775Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1204267Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_bool SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1204785Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_float16 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1205297Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_float32 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1205813Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1206317Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int16 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1206825Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1207322Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1207825Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1208323Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_uint8 SKIPPED [0.0163s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1208835Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1209407Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_bool SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1209934Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1210443Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_complex64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1210954Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_float16 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1211454Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1211974Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1212514Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1213003Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1213509Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1213997Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1214498Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_uint8 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1215051Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1215586Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_bool SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1216142Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1216703Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1217245Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1217793Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1218337Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1218879Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:40:06.1219413Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1219951Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1220472Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1221066Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_count_nonzero_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1221627Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1222199Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1222772Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1223330Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1223971Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1224526Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_linalg_vector_norm_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1225049Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1225569Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1226098Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1226603Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1227125Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1227630Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_float64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1228157Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1228650Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1229183Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1229697Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1230217Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1230719Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1231231Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1231726Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1232221Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1232778Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1233269Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1233779Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_uint8 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1234281Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1234803Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1235310Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1235951Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_float16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1236460Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1236967Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_std_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1237472Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1237967Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_bool SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1238480Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1239006Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1239508Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1240008Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1240522Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1241011Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1241519Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1242012Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1242512Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1243011Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1243532Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:40:06.1244050Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_complex128 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1244629Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1245132Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1245646Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1246146Z test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1246644Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1247119Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1247678Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1248176Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1248662Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1249158Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1249637Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1250115Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1250591Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1251074Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1251540Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1252021Z test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_uint8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1252508Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1252997Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1253488Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1253981Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1254469Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1254958Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1255436Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1255911Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int64 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1256467Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1256942Z test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1257445Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1257919Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_bool SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1258428Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1258916Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1259464Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1259943Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1260432Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1260911Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1261396Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1261878Z test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1262381Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1262848Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_bool SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1263345Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1263849Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1264329Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1264826Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1265308Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1265792Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1266264Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 93%] 2024-12-18T00:40:06.1266743Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1267215Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1267751Z test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_uint8 SKIPPED [0.0162s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1268259Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1268769Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1269264Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_float32 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1269768Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_float64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1270251Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1270782Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1271278Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1271760Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1272255Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1272750Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1273255Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1273751Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1274255Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1274739Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1275236Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1275788Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int64 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1276282Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1276777Z test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_uint8 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1277325Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1277838Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_bool SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1278375Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1278921Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1279445Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1280037Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1280560Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1281088Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1281595Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1282115Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int64 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1282672Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_int8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1283199Z test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1283742Z test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1284307Z test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1284850Z test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1285396Z test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1285939Z test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1286487Z test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1287001Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_bfloat16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1287520Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1288028Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1288546Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float64 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1289060Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1289557Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1290070Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:40:06.1290565Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int8 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1291139Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1291731Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1292251Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1292759Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1293278Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1293779Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1294292Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1294856Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int64 SKIPPED [0.0159s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1295369Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1295870Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_uint8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1296396Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_bfloat16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1296927Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float16 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1297452Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1298126Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1298638Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1299164Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1299672Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1300195Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1300714Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_uint8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1301258Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1301775Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1302308Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1302828Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1303351Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1303953Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1304475Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1304983Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1305507Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_uint8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1306052Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1306667Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1307230Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_complex64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1307761Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float16 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1308305Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1308839Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1309379Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1309913Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1310454Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1310973Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1311515Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_uint8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1312030Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_bfloat16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1312548Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_bool SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1313071Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_complex128 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1313601Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1314109Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1314631Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:40:06.1315138Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1315769Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1316283Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1316782Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1317292Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int8 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1317793Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_uint8 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1318320Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_bfloat16 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1318885Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float16 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1319407Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float32 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1319913Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1320442Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bfloat16 SKIPPED [0.0155s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1320940Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bool SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1321491Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1322008Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1322529Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1323039Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1323559Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1324057Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1324564Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1325073Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1325568Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1326082Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1326593Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1327127Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex128 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1327734Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex64 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1328252Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float16 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1328754Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1329270Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1329769Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1330282Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1330823Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1331331Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int8 SKIPPED [0.0150s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1331825Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1332346Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1332839Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bool SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1333366Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex128 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1333890Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1334392Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1334909Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1335415Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1335924Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1336424Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1336931Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1337422Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int8 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1337932Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1338442Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1338975Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 96%] 2024-12-18T00:40:06.1339538Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1340056Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1340562Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1341064Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1341584Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1342140Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1342653Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1343149Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int8 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1343660Z test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1344149Z test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1344661Z test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1345168Z test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1345671Z test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1346161Z test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1346662Z test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1347171Z test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1347688Z test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1348197Z test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1348715Z test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1349220Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bfloat16 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1349712Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1350226Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1350727Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1351289Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1351783Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1352282Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1352766Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1353260Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1353745Z test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1354302Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1354780Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bool SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1355287Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex128 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1355847Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1356351Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1356842Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1357331Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float64 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1357819Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1358298Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1358790Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1359263Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1359763Z test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1360249Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_bfloat16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1360755Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex128 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1361244Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1361738Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float16 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1362218Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:40:06.1362766Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1363287Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_bfloat16 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1363832Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex128 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1364359Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex64 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1364875Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1365402Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float32 SKIPPED [0.0152s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1365970Z test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float64 SKIPPED [0.0144s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1366467Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bfloat16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1366935Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bool SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1367443Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1367933Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex64 SKIPPED [0.0143s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1368428Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1368913Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1369402Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float64 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1369873Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int16 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1370354Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int32 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1370825Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int64 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1371305Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int8 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1371785Z test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_uint8 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1372268Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1372776Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex128 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1373266Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1373760Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float16 SKIPPED [0.0146s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1374238Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32 SKIPPED [0.0145s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1374782Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float64 SKIPPED [0.0148s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1375307Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16 SKIPPED [0.0254s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1375853Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex128 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1376383Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1376910Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1377479Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1378010Z test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:40:06.1378380Z test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128 PASSED [0.0800s] [ 98%] 2024-12-18T00:40:06.1378748Z test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64 PASSED [0.0788s] [ 98%] 2024-12-18T00:40:06.1379098Z test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32 PASSED [0.0724s] [ 98%] 2024-12-18T00:40:06.1379449Z test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64 PASSED [0.0713s] [ 98%] 2024-12-18T00:40:06.1380095Z test_reductions.py::TestReductionsCPU::test_std_dim_cpu W1217 23:58:12.600000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.1380637Z W1217 23:58:12.600000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:859) 2024-12-18T00:40:06.1381168Z W1217 23:58:12.600000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['n'])' rank mismatch. expected 2, actual 3 2024-12-18T00:40:06.1381629Z W1217 23:58:12.600000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.1382301Z W1217 23:58:12.600000 904 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.1382706Z W1217 23:58:12.605000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.1383247Z W1217 23:58:12.605000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:858) 2024-12-18T00:40:06.1383724Z W1217 23:58:12.605000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['t']' rank mismatch. expected 2, actual 3 2024-12-18T00:40:06.1384190Z W1217 23:58:12.605000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.1384834Z W1217 23:58:12.605000 904 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.1385246Z W1217 23:58:13.061000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.1385795Z W1217 23:58:13.061000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: 'make_contiguous' (/var/lib/jenkins/workspace/test/test_reductions.py:749) 2024-12-18T00:40:06.1386338Z W1217 23:58:13.061000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: len(L['shape']) == 2 2024-12-18T00:40:06.1386791Z W1217 23:58:13.061000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.1387448Z W1217 23:58:13.061000 904 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.1387551Z PASSED [3.0836s] [ 98%] 2024-12-18T00:40:06.1387851Z test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu PASSED [0.0486s] [ 98%] 2024-12-18T00:40:06.1388211Z test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128 PASSED [0.1023s] [ 98%] 2024-12-18T00:40:06.1388557Z test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64 PASSED [0.1049s] [ 98%] 2024-12-18T00:40:06.1388944Z test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32 PASSED [0.0935s] [ 98%] 2024-12-18T00:40:06.1389271Z test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64 PASSED [0.0924s] [ 98%] 2024-12-18T00:40:06.1389545Z test_reductions.py::TestReductionsCPU::test_std_mean_cpu PASSED [0.0606s] [ 98%] 2024-12-18T00:40:06.1389838Z test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu PASSED [0.0515s] [ 98%] 2024-12-18T00:40:06.1390446Z test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128 SKIPPED [0.2823s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1390456Z 2024-12-18T00:40:06.1390648Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1391058Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_std_vs_numpy_cpu_complex128 2024-12-18T00:40:06.1391083Z 2024-12-18T00:40:06.1391385Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 98%] 2024-12-18T00:40:06.1391973Z test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64 SKIPPED [0.2786s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1391991Z 2024-12-18T00:40:06.1392186Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1392559Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_std_vs_numpy_cpu_complex64 2024-12-18T00:40:06.1392564Z 2024-12-18T00:40:06.1392834Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 98%] 2024-12-18T00:40:06.1393410Z test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32 SKIPPED [0.2756s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1393419Z 2024-12-18T00:40:06.1393619Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1393986Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_std_vs_numpy_cpu_float32 2024-12-18T00:40:06.1393991Z 2024-12-18T00:40:06.1394259Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 98%] 2024-12-18T00:40:06.1394837Z test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64 SKIPPED [0.2768s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1394841Z 2024-12-18T00:40:06.1395042Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1395408Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_std_vs_numpy_cpu_float64 2024-12-18T00:40:06.1395412Z 2024-12-18T00:40:06.1395741Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 99%] 2024-12-18T00:40:06.1396025Z test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool PASSED [0.0981s] [ 99%] 2024-12-18T00:40:06.1396301Z test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64 2024-12-18T00:40:06.1396320Z 2024-12-18T00:40:06.1396856Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_reductions/test_reductions-cb40866da58c9eaf.xml - 2024-12-18T00:40:06.1397015Z !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! KeyboardInterrupt !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 2024-12-18T00:40:06.1397358Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/sympy/core/basic.py:256: KeyboardInterrupt 2024-12-18T00:40:06.1397557Z (to show a full traceback on KeyboardInterrupt use --full-trace) 2024-12-18T00:40:06.1397755Z ================ 298 passed, 4282 skipped in 1799.24s (0:29:59) ================ 2024-12-18T00:40:06.1397998Z Command took >30min, returning 124 2024-12-18T00:40:06.1398113Z Got exit code 124 2024-12-18T00:40:06.1398214Z Retrying single test... 2024-12-18T00:40:06.1398607Z Test results will be stored in test-reports/python-pytest/test_reductions/test_reductions-af91440e1dacc574.xml 2024-12-18T00:40:06.1398861Z ============================= test session starts ============================== 2024-12-18T00:40:06.1399163Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:40:06.1399281Z cachedir: .pytest_cache 2024-12-18T00:40:06.1399721Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:40:06.1399852Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:40:06.1399956Z configfile: pytest.ini 2024-12-18T00:40:06.1400419Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:40:06.1400631Z collecting ... collected 4625 items / 4624 deselected / 1 selected 2024-12-18T00:40:06.1401080Z stepcurrent: skipping 4580 already run items. Running only test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64 2024-12-18T00:40:06.1401204Z Running 1 items in this shard 2024-12-18T00:40:06.1401208Z 2024-12-18T00:40:06.1401505Z test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64 PASSED [1065.3075s] [100%] 2024-12-18T00:40:06.1401511Z 2024-12-18T00:40:06.1402057Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_reductions/test_reductions-af91440e1dacc574.xml - 2024-12-18T00:40:06.1402243Z =============== 1 passed, 4624 deselected in 1065.50s (0:17:45) ================ 2024-12-18T00:40:06.1402354Z Got exit code 0 2024-12-18T00:40:06.1402572Z Test succeeeded in new process, continuing with the rest of the tests 2024-12-18T00:40:06.1402957Z Test results will be stored in test-reports/python-pytest/test_reductions/test_reductions-fe3893e38ff375cb.xml 2024-12-18T00:40:06.1403119Z ============================= test session starts ============================== 2024-12-18T00:40:06.1403414Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:40:06.1403534Z cachedir: .pytest_cache 2024-12-18T00:40:06.1403977Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:40:06.1404104Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:40:06.1404206Z configfile: pytest.ini 2024-12-18T00:40:06.1404666Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:40:06.1404885Z collecting ... collected 4625 items / 4581 deselected / 44 selected 2024-12-18T00:40:06.1405025Z stepcurrent: skipping 4581 already run items. 2024-12-18T00:40:06.1405146Z Running 44 items in this shard 2024-12-18T00:40:06.1405151Z 2024-12-18T00:40:06.1405550Z test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu SKIPPED [0.0636s] (Only runs on cuda) [ 2%] 2024-12-18T00:40:06.1406032Z test_reductions.py::TestReductionsCPU::test_sum_dim_cpu SKIPPED [0.0166s] (test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test) [ 4%] 2024-12-18T00:40:06.1406860Z test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] Graph break from `Tensor.item()`, consider setting: 2024-12-18T00:40:06.1407319Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] torch._dynamo.config.capture_scalar_outputs = True 2024-12-18T00:40:06.1407584Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] or: 2024-12-18T00:40:06.1408009Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1 2024-12-18T00:40:06.1408442Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] to include these operations in the captured graph. 2024-12-18T00:40:06.1408690Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] 2024-12-18T00:40:06.1409134Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] Graph break: from user code at: 2024-12-18T00:40:06.1409826Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] File "/var/lib/jenkins/workspace/test/test_reductions.py", line 460, in test_sum_dim_reduction_uint8_overflow 2024-12-18T00:40:06.1410290Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] self.assertEqual(x.sum(dtype=torch.uint8).item(), 16) 2024-12-18T00:40:06.1410536Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] 2024-12-18T00:40:06.1410794Z W1218 00:39:49.552000 970 site-packages/torch/_dynamo/variables/tensor.py:869] [1/0] 2024-12-18T00:40:06.1410894Z PASSED [0.6625s] [ 6%] 2024-12-18T00:40:06.1411574Z test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu W1218 00:39:50.027000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.1412101Z W1218 00:39:50.027000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:949) 2024-12-18T00:40:06.1412605Z W1218 00:39:50.027000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/2: tensor 'L['x']' dtype mismatch. expected Long, actual Char 2024-12-18T00:40:06.1413059Z W1218 00:39:50.027000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.1413717Z W1218 00:39:50.027000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.1414190Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] failed while attempting to run meta for aten.sum.IntList_out 2024-12-18T00:40:06.1414573Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] Traceback (most recent call last): 2024-12-18T00:40:06.1415317Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:06.1415672Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] r = func(*args, **kwargs) 2024-12-18T00:40:06.1416287Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:06.1416668Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] return self._op(*args, **kwargs) 2024-12-18T00:40:06.1417045Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1417729Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2329, in sum 2024-12-18T00:40:06.1418078Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] return _reduction( 2024-12-18T00:40:06.1418363Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] a, 2024-12-18T00:40:06.1418695Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ...<5 lines>... 2024-12-18T00:40:06.1419137Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, 2024-12-18T00:40:06.1419417Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ) 2024-12-18T00:40:06.1420074Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2210, in _reduction 2024-12-18T00:40:06.1420477Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] raise RuntimeError( 2024-12-18T00:40:06.1420929Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] "dtype argument and out dtype must match in reduction" 2024-12-18T00:40:06.1421197Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] ) 2024-12-18T00:40:06.1421713Z E1218 00:39:50.304000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [9/0] RuntimeError: dtype argument and out dtype must match in reduction 2024-12-18T00:40:06.1421813Z PASSED [0.4354s] [ 9%] 2024-12-18T00:40:06.1422219Z test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64 SKIPPED [0.0158s] (Only runs on cuda) [ 11%] 2024-12-18T00:40:06.1422555Z test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16 PASSED [1.7645s] [ 13%] 2024-12-18T00:40:06.1422909Z test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16 PASSED [1.8231s] [ 15%] 2024-12-18T00:40:06.1423195Z test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64 PASSED [0.1902s] [ 18%] 2024-12-18T00:40:06.1423486Z test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu PASSED [0.2013s] [ 20%] 2024-12-18T00:40:06.1423787Z test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16 PASSED [0.4877s] [ 22%] 2024-12-18T00:40:06.1424087Z test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32 PASSED [0.4517s] [ 25%] 2024-12-18T00:40:06.1424399Z test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64 PASSED [0.4545s] [ 27%] 2024-12-18T00:40:06.1424695Z test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16 PASSED [0.2849s] [ 29%] 2024-12-18T00:40:06.1425003Z test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32 PASSED [0.2812s] [ 31%] 2024-12-18T00:40:06.1425299Z test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64 PASSED [0.2767s] [ 34%] 2024-12-18T00:40:06.1425608Z test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8 PASSED [0.2805s] [ 36%] 2024-12-18T00:40:06.1426470Z test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] failed while attempting to run meta for aten.argmax.default 2024-12-18T00:40:06.1426866Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] Traceback (most recent call last): 2024-12-18T00:40:06.1427594Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:06.1427959Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] r = func(*args, **kwargs) 2024-12-18T00:40:06.1428627Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:06.1429006Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] return self._op(*args, **kwargs) 2024-12-18T00:40:06.1429386Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1430106Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_meta_registrations.py", line 6062, in argmax_argmin_meta 2024-12-18T00:40:06.1430527Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] check_argmax_argmin("argmax", self, dim) 2024-12-18T00:40:06.1430904Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1431686Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_meta_registrations.py", line 6052, in check_argmax_argmin 2024-12-18T00:40:06.1432083Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] zero_numel_check_dims(self, dim, name) 2024-12-18T00:40:06.1432471Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1433200Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_meta_registrations.py", line 6042, in zero_numel_check_dims 2024-12-18T00:40:06.1433549Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] torch._check_index( 2024-12-18T00:40:06.1433879Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:40:06.1434241Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] self.size(dim) != 0, 2024-12-18T00:40:06.1434579Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1435101Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] lambda: f"{fn_name}: Expected reduction dim {dim} to have non-zero size.", 2024-12-18T00:40:06.1435511Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1435858Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ) 2024-12-18T00:40:06.1436137Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ^ 2024-12-18T00:40:06.1436794Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py", line 1665, in _check_index 2024-12-18T00:40:06.1437204Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] _check_with(IndexError, cond, message) 2024-12-18T00:40:06.1437577Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1438225Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py", line 1612, in _check_with 2024-12-18T00:40:06.1438612Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] raise error_type(message_evaluated) 2024-12-18T00:40:06.1439121Z E1218 00:39:56.913000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] IndexError: argmax: Expected reduction dim 1 to have non-zero size. 2024-12-18T00:40:06.1439703Z SKIPPED [0.0808s] (Failed running call_function (*(FakeTensor(..., size=(2, 0, 4)),), **{'dim': 1}): 2024-12-18T00:40:06.1439886Z argmax: Expected reduction dim 1 to have non-zero size. 2024-12-18T00:40:06.1439891Z 2024-12-18T00:40:06.1439991Z from user code: 2024-12-18T00:40:06.1440289Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 3560, in 2024-12-18T00:40:06.1440616Z self.assertRaisesRegex(IndexError, "Expected reduction dim", lambda: fn(master_input, dim=1)) 2024-12-18T00:40:06.1440621Z 2024-12-18T00:40:06.1440836Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:06.1440841Z 2024-12-18T00:40:06.1440860Z 2024-12-18T00:40:06.1441067Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:06.1441171Z import torch._dynamo 2024-12-18T00:40:06.1441335Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:06.1441340Z 2024-12-18T00:40:06.1441344Z 2024-12-18T00:40:06.1441585Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1442093Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu 2024-12-18T00:40:06.1442098Z 2024-12-18T00:40:06.1442358Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 38%] 2024-12-18T00:40:06.1443125Z test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] failed while attempting to run meta for aten.amax.default 2024-12-18T00:40:06.1443506Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] Traceback (most recent call last): 2024-12-18T00:40:06.1444242Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2384, in _dispatch_impl 2024-12-18T00:40:06.1444609Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] r = func(*args, **kwargs) 2024-12-18T00:40:06.1445223Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_ops.py", line 723, in __call__ 2024-12-18T00:40:06.1445606Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] return self._op(*args, **kwargs) 2024-12-18T00:40:06.1445986Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:40:06.1446623Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2428, in amax 2024-12-18T00:40:06.1446967Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] return _reduction( 2024-12-18T00:40:06.1447270Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] a, 2024-12-18T00:40:06.1447587Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ...<6 lines>... 2024-12-18T00:40:06.1448040Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] output_dtype_kind=REDUCTION_OUTPUT_TYPE_KIND.SAME, 2024-12-18T00:40:06.1448311Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ) 2024-12-18T00:40:06.1448982Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_refs/__init__.py", line 2221, in _reduction 2024-12-18T00:40:06.1449324Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] raise RuntimeError( 2024-12-18T00:40:06.1449931Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] "reducing over zero-size dimension for reduction operation without identity" 2024-12-18T00:40:06.1450199Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] ) 2024-12-18T00:40:06.1450795Z E1218 00:39:56.990000 970 site-packages/torch/_subclasses/fake_tensor.py:2388] [2/0] RuntimeError: reducing over zero-size dimension for reduction operation without identity 2024-12-18T00:40:06.1451302Z SKIPPED [0.0547s] (Failed running call_function (*(FakeTensor(..., size=(2, 0, 4)),), **{'dim': 1}): 2024-12-18T00:40:06.1451554Z reducing over zero-size dimension for reduction operation without identity 2024-12-18T00:40:06.1451559Z 2024-12-18T00:40:06.1451655Z from user code: 2024-12-18T00:40:06.1451939Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 3523, in 2024-12-18T00:40:06.1452283Z self.assertRaisesRegex(IndexError, "Expected reduction dim", lambda: fn(master_input, dim=1)) 2024-12-18T00:40:06.1452288Z 2024-12-18T00:40:06.1452552Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:06.1452557Z 2024-12-18T00:40:06.1452561Z 2024-12-18T00:40:06.1452781Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:06.1452883Z import torch._dynamo 2024-12-18T00:40:06.1453038Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:06.1453043Z 2024-12-18T00:40:06.1453047Z 2024-12-18T00:40:06.1453240Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1453635Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_tensor_compare_ops_empty_cpu 2024-12-18T00:40:06.1453640Z 2024-12-18T00:40:06.1453904Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 40%] 2024-12-18T00:40:06.1454668Z test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu SKIPPED [0.3571s] (This test passed, maybe we can remove `test/dynamo_skips/TestReductionsCPU.test_tensor_reduce_ops_empty_cpu`) [ 43%] 2024-12-18T00:40:06.1455030Z test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128 PASSED [0.0796s] [ 45%] 2024-12-18T00:40:06.1455385Z test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64 PASSED [0.0773s] [ 47%] 2024-12-18T00:40:06.1455744Z test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32 PASSED [0.0741s] [ 50%] 2024-12-18T00:40:06.1456092Z test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64 PASSED [0.0727s] [ 52%] 2024-12-18T00:40:06.1456416Z test_reductions.py::TestReductionsCPU::test_var_cpu SKIPPED [0.0160s] (Only runs on cuda) [ 54%] 2024-12-18T00:40:06.1457039Z test_reductions.py::TestReductionsCPU::test_var_dim_cpu W1218 00:39:59.601000 970 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.1457582Z W1218 00:39:59.601000 970 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:867) 2024-12-18T00:40:06.1458101Z W1218 00:39:59.601000 970 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] last reason: 6/0: tensor '___from_numpy(L['n'])' rank mismatch. expected 2, actual 3 2024-12-18T00:40:06.1458571Z W1218 00:39:59.601000 970 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.1459219Z W1218 00:39:59.601000 970 site-packages/torch/_dynamo/convert_frame.py:906] [6/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.1459630Z W1218 00:39:59.605000 970 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.1460143Z W1218 00:39:59.605000 970 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] function: '' (/var/lib/jenkins/workspace/test/test_reductions.py:866) 2024-12-18T00:40:06.1460679Z W1218 00:39:59.605000 970 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] last reason: 7/0: tensor 'L['t']' rank mismatch. expected 2, actual 3 2024-12-18T00:40:06.1461133Z W1218 00:39:59.605000 970 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.1461785Z W1218 00:39:59.605000 970 site-packages/torch/_dynamo/convert_frame.py:906] [7/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.1462185Z W1218 00:40:00.048000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:40:06.1462733Z W1218 00:40:00.048000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] function: 'make_contiguous' (/var/lib/jenkins/workspace/test/test_reductions.py:749) 2024-12-18T00:40:06.1463273Z W1218 00:40:00.048000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] last reason: 2/0: len(L['shape']) == 2 2024-12-18T00:40:06.1463728Z W1218 00:40:00.048000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:40:06.1464381Z W1218 00:40:00.048000 970 site-packages/torch/_dynamo/convert_frame.py:906] [2/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:40:06.1464481Z PASSED [3.2140s] [ 56%] 2024-12-18T00:40:06.1464856Z test_reductions.py::TestReductionsCPU::test_var_large_input_cpu SKIPPED [0.0163s] (Only runs on cuda) [ 59%] 2024-12-18T00:40:06.1465148Z test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu PASSED [0.0468s] [ 61%] 2024-12-18T00:40:06.1465502Z test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128 PASSED [0.1001s] [ 63%] 2024-12-18T00:40:06.1465848Z test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64 PASSED [0.1022s] [ 65%] 2024-12-18T00:40:06.1466190Z test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32 PASSED [0.0918s] [ 68%] 2024-12-18T00:40:06.1466514Z test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64 PASSED [0.0908s] [ 70%] 2024-12-18T00:40:06.1466774Z test_reductions.py::TestReductionsCPU::test_var_mean_cpu PASSED [0.1626s] [ 72%] 2024-12-18T00:40:06.1467077Z test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu PASSED [0.0530s] [ 75%] 2024-12-18T00:40:06.1467357Z test_reductions.py::TestReductionsCPU::test_var_stability2_cpu PASSED [0.1459s] [ 77%] 2024-12-18T00:40:06.1467649Z test_reductions.py::TestReductionsCPU::test_var_stability_cpu PASSED [0.0929s] [ 79%] 2024-12-18T00:40:06.1467921Z test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu PASSED [0.5994s] [ 81%] 2024-12-18T00:40:06.1468538Z test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128 SKIPPED [0.2923s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1468543Z 2024-12-18T00:40:06.1468733Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1469121Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_var_vs_numpy_cpu_complex128 2024-12-18T00:40:06.1469126Z 2024-12-18T00:40:06.1469385Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 84%] 2024-12-18T00:40:06.1469989Z test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64 SKIPPED [0.5701s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1469994Z 2024-12-18T00:40:06.1470182Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1470554Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_var_vs_numpy_cpu_complex64 2024-12-18T00:40:06.1470619Z 2024-12-18T00:40:06.1470882Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 86%] 2024-12-18T00:40:06.1471458Z test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32 SKIPPED [0.2846s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1471472Z 2024-12-18T00:40:06.1471664Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1472027Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_var_vs_numpy_cpu_float32 2024-12-18T00:40:06.1472031Z 2024-12-18T00:40:06.1472298Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 88%] 2024-12-18T00:40:06.1472876Z test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64 SKIPPED [0.2921s] (The values for attribute 'shape' do not match: torch.Size([1, 1]) != torch.Size([]). 2024-12-18T00:40:06.1472885Z 2024-12-18T00:40:06.1473091Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1473503Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_var_vs_numpy_cpu_float64 2024-12-18T00:40:06.1473508Z 2024-12-18T00:40:06.1473775Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 90%] 2024-12-18T00:40:06.1474282Z test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128 SKIPPED [0.0581s] (IndexError: list index out of range 2024-12-18T00:40:06.1474286Z 2024-12-18T00:40:06.1474392Z from user code: 2024-12-18T00:40:06.1474904Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2964, in torch_dynamo_resume_in_test_warn_invalid_degrees_of_freedom_at_2962 2024-12-18T00:40:06.1475048Z _assert_warning(f, tensor, correction) 2024-12-18T00:40:06.1475354Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2958, in _assert_warning 2024-12-18T00:40:06.1475558Z self.assertIn('degrees of freedom is <= 0', str(w[0].message)) 2024-12-18T00:40:06.1475563Z 2024-12-18T00:40:06.1475876Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:06.1475882Z 2024-12-18T00:40:06.1475886Z 2024-12-18T00:40:06.1476097Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:06.1476224Z import torch._dynamo 2024-12-18T00:40:06.1476369Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:06.1476373Z 2024-12-18T00:40:06.1476377Z 2024-12-18T00:40:06.1476583Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1477044Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_warn_invalid_degrees_of_freedom_cpu_complex128 2024-12-18T00:40:06.1477049Z 2024-12-18T00:40:06.1477322Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 93%] 2024-12-18T00:40:06.1477828Z test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64 SKIPPED [0.0581s] (IndexError: list index out of range 2024-12-18T00:40:06.1477836Z 2024-12-18T00:40:06.1477949Z from user code: 2024-12-18T00:40:06.1478457Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2964, in torch_dynamo_resume_in_test_warn_invalid_degrees_of_freedom_at_2962 2024-12-18T00:40:06.1478583Z _assert_warning(f, tensor, correction) 2024-12-18T00:40:06.1478903Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2958, in _assert_warning 2024-12-18T00:40:06.1479098Z self.assertIn('degrees of freedom is <= 0', str(w[0].message)) 2024-12-18T00:40:06.1479102Z 2024-12-18T00:40:06.1479338Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:06.1479343Z 2024-12-18T00:40:06.1479347Z 2024-12-18T00:40:06.1479552Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:06.1479669Z import torch._dynamo 2024-12-18T00:40:06.1479813Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:06.1479870Z 2024-12-18T00:40:06.1479874Z 2024-12-18T00:40:06.1480085Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1480546Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_warn_invalid_degrees_of_freedom_cpu_complex64 2024-12-18T00:40:06.1480550Z 2024-12-18T00:40:06.1480826Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 95%] 2024-12-18T00:40:06.1481320Z test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32 SKIPPED [0.0587s] (IndexError: list index out of range 2024-12-18T00:40:06.1481325Z 2024-12-18T00:40:06.1481442Z from user code: 2024-12-18T00:40:06.1481944Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2964, in torch_dynamo_resume_in_test_warn_invalid_degrees_of_freedom_at_2962 2024-12-18T00:40:06.1482068Z _assert_warning(f, tensor, correction) 2024-12-18T00:40:06.1482389Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2958, in _assert_warning 2024-12-18T00:40:06.1482635Z self.assertIn('degrees of freedom is <= 0', str(w[0].message)) 2024-12-18T00:40:06.1482640Z 2024-12-18T00:40:06.1482869Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:06.1482874Z 2024-12-18T00:40:06.1482877Z 2024-12-18T00:40:06.1483078Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:06.1483194Z import torch._dynamo 2024-12-18T00:40:06.1483338Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:06.1483342Z 2024-12-18T00:40:06.1483346Z 2024-12-18T00:40:06.1483551Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1483998Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_warn_invalid_degrees_of_freedom_cpu_float32 2024-12-18T00:40:06.1484003Z 2024-12-18T00:40:06.1484274Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 97%] 2024-12-18T00:40:06.1484775Z test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 SKIPPED [0.0583s] (IndexError: list index out of range 2024-12-18T00:40:06.1484779Z 2024-12-18T00:40:06.1484876Z from user code: 2024-12-18T00:40:06.1485393Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2964, in torch_dynamo_resume_in_test_warn_invalid_degrees_of_freedom_at_2962 2024-12-18T00:40:06.1485520Z _assert_warning(f, tensor, correction) 2024-12-18T00:40:06.1485839Z File "/var/lib/jenkins/workspace/test/test_reductions.py", line 2958, in _assert_warning 2024-12-18T00:40:06.1486031Z self.assertIn('degrees of freedom is <= 0', str(w[0].message)) 2024-12-18T00:40:06.1486036Z 2024-12-18T00:40:06.1486265Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:40:06.1486270Z 2024-12-18T00:40:06.1486273Z 2024-12-18T00:40:06.1486479Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:40:06.1486600Z import torch._dynamo 2024-12-18T00:40:06.1486747Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:40:06.1486751Z 2024-12-18T00:40:06.1486758Z 2024-12-18T00:40:06.1486963Z To execute this test, run the following from the base repo dir: 2024-12-18T00:40:06.1487413Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py TestReductionsCPU.test_warn_invalid_degrees_of_freedom_cpu_float64 2024-12-18T00:40:06.1487418Z 2024-12-18T00:40:06.1487678Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [100%] 2024-12-18T00:40:06.1487697Z 2024-12-18T00:40:06.1488228Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_reductions/test_reductions-fe3893e38ff375cb.xml - 2024-12-18T00:40:06.1488419Z =============== 28 passed, 16 skipped, 4581 deselected in 15.16s =============== 2024-12-18T00:40:06.1488930Z The following tests failed and then succeeded when run in a new process['test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64'] 2024-12-18T00:40:06.1488985Z 2024-12-18T00:40:06.1489355Z FINISHED PRINTING LOG FILE of test_reductions 1/1 (test/test-reports/test_reductions_1.1_7af9e76d08b9736e_.log) 2024-12-18T00:40:06.1489360Z 2024-12-18T00:40:06.1489576Z Running test_cuda_nvml_based_avail 1/1 ... [2024-12-18 00:40:05.416437] 2024-12-18T00:40:06.1489692Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:40:06.1490596Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:40:05.416779] 2024-12-18T00:40:08.5411352Z 2024-12-18T00:40:08.5412432Z test_cuda_nvml_based_avail 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_nvml_based_avail_1.1_ccfb4ec03e3cabba_.log 2024-12-18T00:40:08.5413262Z Running 0 items in this shard: 2024-12-18T00:40:08.5413495Z 2024-12-18T00:40:08.5415355Z Running test_cuda_primary_ctx 1/1 ... [2024-12-18 00:40:08.541388] 2024-12-18T00:40:08.5416041Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:40:08.5419244Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_primary_ctx.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:40:08.541717] 2024-12-18T00:40:11.6788602Z 2024-12-18T00:40:11.6789498Z test_cuda_primary_ctx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_primary_ctx_1.1_d44231bce6f346af_.log 2024-12-18T00:40:11.6790278Z Running 0 items in this shard: 2024-12-18T00:40:11.6790544Z 2024-12-18T00:40:11.6791697Z Running test_cpp_extensions_aot_ninja 1/1 ... [2024-12-18 00:40:11.678999] 2024-12-18T00:40:14.2170308Z running install 2024-12-18T00:40:14.2171573Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:40:14.2172393Z !! 2024-12-18T00:40:14.2172529Z 2024-12-18T00:40:14.2172668Z ******************************************************************************** 2024-12-18T00:40:14.2173058Z Please avoid running ``setup.py`` directly. 2024-12-18T00:40:14.2173461Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:40:14.2173835Z standards-based tools. 2024-12-18T00:40:14.2174022Z 2024-12-18T00:40:14.2174335Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:40:14.2174862Z ******************************************************************************** 2024-12-18T00:40:14.2175110Z 2024-12-18T00:40:14.2175196Z !! 2024-12-18T00:40:14.2175420Z self.initialize_options() 2024-12-18T00:40:14.2306957Z running build 2024-12-18T00:40:14.2307212Z running build_py 2024-12-18T00:40:14.2383464Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:40:14.2392074Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:40:14.2401485Z running build_ext 2024-12-18T00:40:14.3595475Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:40:14.3596745Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313 2024-12-18T00:40:14.3918364Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:40:14.3919103Z Compiling objects... 2024-12-18T00:40:14.3919438Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:40:15.4470457Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T00:40:15.4570614Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2024-12-18T00:40:15.7293059Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:40:15.7607800Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:40:15.7618475Z Compiling objects... 2024-12-18T00:40:15.7618805Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:40:16.4727241Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T00:40:16.4783019Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2024-12-18T00:40:16.7421068Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:40:16.7738337Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:40:16.7739384Z Compiling objects... 2024-12-18T00:40:16.7739783Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:40:17.7182384Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T00:40:17.7236740Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2024-12-18T00:40:17.9952506Z running install_lib 2024-12-18T00:40:18.0032888Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2024-12-18T00:40:18.0036737Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:40:18.0038826Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:40:18.0040784Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:40:18.0073726Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:40:18.0108210Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:40:18.0146188Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-313.pyc 2024-12-18T00:40:18.0148911Z running install_egg_info 2024-12-18T00:40:18.0316927Z running egg_info 2024-12-18T00:40:18.0317819Z creating torch_test_cpp_extension.egg-info 2024-12-18T00:40:18.0384782Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:40:18.0387894Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:40:18.0389648Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:40:18.0391512Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:40:18.0392791Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:40:18.0464269Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:40:18.0471390Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:40:18.0473454Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2024-12-18T00:40:18.0478842Z running install_scripts 2024-12-18T00:40:20.1857151Z running install 2024-12-18T00:40:20.1858502Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:40:20.1859933Z !! 2024-12-18T00:40:20.1860135Z 2024-12-18T00:40:20.1860336Z ******************************************************************************** 2024-12-18T00:40:20.1861063Z Please avoid running ``setup.py`` directly. 2024-12-18T00:40:20.1862160Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:40:20.1862852Z standards-based tools. 2024-12-18T00:40:20.1863220Z 2024-12-18T00:40:20.1863783Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:40:20.1864776Z ******************************************************************************** 2024-12-18T00:40:20.1865239Z 2024-12-18T00:40:20.1865385Z !! 2024-12-18T00:40:20.1865785Z self.initialize_options() 2024-12-18T00:40:20.1993311Z running build 2024-12-18T00:40:20.1993717Z running build_ext 2024-12-18T00:40:20.3100501Z building 'no_python_abi_suffix_test' extension 2024-12-18T00:40:20.3102321Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313 2024-12-18T00:40:20.3423338Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:40:20.3424890Z Compiling objects... 2024-12-18T00:40:20.3425376Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:40:20.4241481Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T00:40:20.4288554Z creating build/lib.linux-x86_64-cpython-313 2024-12-18T00:40:20.4293342Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so 2024-12-18T00:40:20.4906405Z running install_lib 2024-12-18T00:40:20.4983030Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2024-12-18T00:40:20.4986823Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2024-12-18T00:40:20.4992344Z running install_egg_info 2024-12-18T00:40:20.5169055Z running egg_info 2024-12-18T00:40:20.5169855Z creating no_python_abi_suffix_test.egg-info 2024-12-18T00:40:20.5238843Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-12-18T00:40:20.5242207Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-12-18T00:40:20.5244005Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-12-18T00:40:20.5245640Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:40:20.5317143Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:40:20.5323808Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:40:20.5325305Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info 2024-12-18T00:40:20.5330280Z running install_scripts 2024-12-18T00:40:20.9632747Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:40:20.9635548Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:40:20.963272] 2024-12-18T00:40:26.9985019Z 2024-12-18T00:40:26.9986123Z test_cpp_extensions_aot_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_ninja_1.1_26639d44ab7aa4ea_.log 2024-12-18T00:40:26.9992900Z Running 18 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2024-12-18T00:40:26.9999129Z 2024-12-18T00:40:26.9999325Z Running test_spectral_ops 1/1 ... [2024-12-18 00:40:26.998736] 2024-12-18T00:40:26.9999742Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:40:27.0000766Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_spectral_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:40:26.999032] 2024-12-18T00:41:01.6096590Z 2024-12-18T00:41:01.6098140Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_7c0c9529e06ef087_.log 2024-12-18T00:41:01.6215442Z Running 280 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_ifft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft2_invalid_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_ifft_rfft_irfft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_input_modification_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_invalid_dtypes_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_plan_repeatable_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_int8, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_against_librosa_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_linearity_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_of_sine_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_simple_cases_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_various_params_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_with_padding_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_throws_cpu, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_complex_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_window_device_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_hfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ihfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftn_cpu 2024-12-18T00:41:01.6301201Z 2024-12-18T00:41:01.6301479Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2024-12-18 00:41:01.610455] 2024-12-18T00:41:04.1120778Z running install 2024-12-18T00:41:04.1122457Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:41:04.1123251Z !! 2024-12-18T00:41:04.1123370Z 2024-12-18T00:41:04.1123514Z ******************************************************************************** 2024-12-18T00:41:04.1123891Z Please avoid running ``setup.py`` directly. 2024-12-18T00:41:04.1124300Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:41:04.1124678Z standards-based tools. 2024-12-18T00:41:04.1125175Z 2024-12-18T00:41:04.1125499Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:41:04.1126051Z ******************************************************************************** 2024-12-18T00:41:04.1126298Z 2024-12-18T00:41:04.1126401Z !! 2024-12-18T00:41:04.1126619Z self.initialize_options() 2024-12-18T00:41:04.1261659Z running build 2024-12-18T00:41:04.1261936Z running build_py 2024-12-18T00:41:04.1341154Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:41:04.1343513Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T00:41:04.1347143Z running build_ext 2024-12-18T00:41:04.2176569Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T00:41:04.2177163Z creating build/temp.linux-x86_64-cpython-313 2024-12-18T00:41:04.2183479Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T00:41:05.5762618Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T00:41:05.5764110Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T00:41:05.5765022Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2024-12-18T00:41:05.5765568Z from extension.cpp:1: 2024-12-18T00:41:05.5768694Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T00:41:05.5770049Z extension.cpp:45:53: required from here 2024-12-18T00:41:05.5771652Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T00:41:05.5772873Z 1539 | class class_ : public detail::generic_type { 2024-12-18T00:41:05.5773227Z | ^~~~~~ 2024-12-18T00:41:05.5774829Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T00:41:05.5776255Z extension.cpp:45:53: required from here 2024-12-18T00:41:05.5779639Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2024-12-18T00:41:05.5782459Z 1599 | with_internals([&](internals &internals) { 2024-12-18T00:41:05.5783084Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:41:05.5783621Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T00:41:05.5784231Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:41:05.5784726Z 1601 | : internals.registered_types_cpp; 2024-12-18T00:41:05.5785161Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:41:05.5785612Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T00:41:05.5786066Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:41:05.5786472Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T00:41:05.5786919Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T00:41:05.5787248Z 1604 | }); 2024-12-18T00:41:05.5787555Z | ~ 2024-12-18T00:41:05.5791016Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2024-12-18T00:41:05.9794381Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T00:41:05.9799314Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T00:41:07.3593059Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2024-12-18T00:41:07.7282018Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T00:41:07.7286414Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T00:41:09.2552922Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:41:09.2554610Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:41:09.2555532Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:41:09.2556775Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:41:09.2557451Z from rng_extension.cpp:6: 2024-12-18T00:41:09.2558531Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:41:09.2559711Z 1123 | # pragma unroll 2024-12-18T00:41:09.2560099Z | 2024-12-18T00:41:09.2561062Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T00:41:09.2562458Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:41:09.2563727Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:41:09.2564597Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:41:09.2565632Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:41:09.2566421Z from rng_extension.cpp:6: 2024-12-18T00:41:09.2567210Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:41:09.2567974Z 59 | #pragma unroll 2024-12-18T00:41:09.2568217Z | 2024-12-18T00:41:09.2568888Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:41:09.2569644Z 72 | #pragma unroll 2024-12-18T00:41:09.2569896Z | 2024-12-18T00:41:09.2570562Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:41:09.2571307Z 87 | #pragma unroll 2024-12-18T00:41:09.2571560Z | 2024-12-18T00:41:09.2572098Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T00:41:09.2572981Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T00:41:09.2573781Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T00:41:09.2574558Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T00:41:09.2575437Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T00:41:09.2576103Z from rng_extension.cpp:6: 2024-12-18T00:41:09.2576894Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T00:41:09.2577869Z 153 | #pragma unroll 2024-12-18T00:41:09.2578136Z | 2024-12-18T00:41:09.2581169Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2024-12-18T00:41:09.6501063Z running install_lib 2024-12-18T00:41:09.6583448Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:41:09.6673619Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:41:09.6760048Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T00:41:09.6854740Z running install_egg_info 2024-12-18T00:41:09.7028180Z running egg_info 2024-12-18T00:41:09.7096955Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T00:41:09.7101545Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T00:41:09.7103606Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T00:41:09.7105484Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T00:41:09.7178827Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:41:09.7186857Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T00:41:09.7188680Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2024-12-18T00:41:09.7190064Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2024-12-18T00:41:09.7196239Z running install_scripts 2024-12-18T00:41:11.8224463Z running install 2024-12-18T00:41:11.8225965Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T00:41:11.8226756Z !! 2024-12-18T00:41:11.8226931Z 2024-12-18T00:41:11.8227060Z ******************************************************************************** 2024-12-18T00:41:11.8227470Z Please avoid running ``setup.py`` directly. 2024-12-18T00:41:11.8227879Z Instead, use pypa/build, pypa/installer or other 2024-12-18T00:41:11.8228238Z standards-based tools. 2024-12-18T00:41:11.8228443Z 2024-12-18T00:41:11.8228750Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T00:41:11.8229281Z ******************************************************************************** 2024-12-18T00:41:11.8229535Z 2024-12-18T00:41:11.8229621Z !! 2024-12-18T00:41:11.8229853Z self.initialize_options() 2024-12-18T00:41:11.8359092Z running build 2024-12-18T00:41:11.8359549Z running build_ext 2024-12-18T00:41:11.9450007Z building 'no_python_abi_suffix_test' extension 2024-12-18T00:41:11.9765216Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja... 2024-12-18T00:41:11.9766309Z Compiling objects... 2024-12-18T00:41:11.9766652Z Using envvar MAX_JOBS (6) as the number of workers... 2024-12-18T00:41:12.0039479Z ninja: no work to do. 2024-12-18T00:41:12.0084984Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so 2024-12-18T00:41:12.0667751Z running install_lib 2024-12-18T00:41:12.0746086Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2024-12-18T00:41:12.0750492Z running install_egg_info 2024-12-18T00:41:12.0921190Z running egg_info 2024-12-18T00:41:12.0988167Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2024-12-18T00:41:12.0991729Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2024-12-18T00:41:12.0993868Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2024-12-18T00:41:12.1065354Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:41:12.1072770Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2024-12-18T00:41:12.1074380Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info' (and everything under it) 2024-12-18T00:41:12.1075891Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info 2024-12-18T00:41:12.1080704Z running install_scripts 2024-12-18T00:41:12.5505597Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:41:12.5507831Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:41:12.550548] 2024-12-18T00:41:18.6560815Z 2024-12-18T00:41:18.6561837Z test_cpp_extensions_aot_no_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_4cbfa79f4be90361_.log 2024-12-18T00:41:18.6569093Z Running 18 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2024-12-18T00:41:18.6575542Z 2024-12-18T00:41:18.6575731Z Running test_show_pickle 1/1 ... [2024-12-18 00:41:18.656262] 2024-12-18T00:41:18.6576141Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:41:18.6577156Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:41:18.656568] 2024-12-18T00:41:22.0254333Z 2024-12-18T00:41:22.0255452Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_8ccadbdb210ccbf7_.log 2024-12-18T00:41:22.0257134Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2024-12-18T00:41:22.0257635Z 2024-12-18T00:41:22.0257940Z Running test_namedtuple_return_api 1/1 ... [2024-12-18 00:41:22.025373] 2024-12-18T00:41:22.0258399Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:41:22.0259515Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtuple_return_api.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:41:22.025684] 2024-12-18T00:41:27.0466941Z 2024-12-18T00:41:27.0467955Z test_namedtuple_return_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtuple_return_api_1.1_41ef5f9a1245ff5c_.log 2024-12-18T00:41:27.0470032Z Running 3 items in this shard: test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_import_return_types, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_namedtuple_return, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_native_functions_yaml 2024-12-18T00:41:27.0471196Z 2024-12-18T00:41:27.0471408Z Running test_jit_disabled 1/1 ... [2024-12-18 00:41:27.046829] 2024-12-18T00:41:27.0471817Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:41:27.0473579Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_disabled.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:41:27.047147] 2024-12-18T00:41:30.5159783Z 2024-12-18T00:41:30.5160797Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_c62a55a63828c53e_.log 2024-12-18T00:41:30.5162442Z Running 3 items in this shard: test/test_jit_disabled.py::TestJitDisabled::test_attribute, test/test_jit_disabled.py::TestJitDisabled::test_recursive_script, test/test_jit_disabled.py::TestJitDisabled::test_script_module_construction 2024-12-18T00:41:30.5163651Z 2024-12-18T00:41:30.5163839Z Running test_autocast 1/1 ... [2024-12-18 00:41:30.516136] 2024-12-18T00:41:30.5164247Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:41:30.5166692Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autocast.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:41:30.516459] 2024-12-18T00:42:31.3095308Z 2024-12-18T00:42:31.3096330Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_e14ad2157391298e_.log 2024-12-18T00:42:31.3104676Z Running 20 items in this shard: test/test_autocast.py::TestAutocastCPU::test_autocast_disabled_with_fp32_dtype, test/test_autocast.py::TestAutocastCPU::test_autocast_methods_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_16, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_rnn, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_16, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_need_autocast_promote, test/test_autocast.py::TestAutocastCPU::test_cpu_autocast_deprecated_warning, test/test_autocast.py::TestAutocastCPU::test_generic_autocast, test/test_autocast.py::TestAutocastGPU::test_autocast_prioritize, test/test_autocast.py::TestAutocastGPU::test_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_bfloat16_supported, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_error_message, test/test_autocast.py::TestTorchAutocast::test_autocast_fast_dtype, test/test_autocast.py::TestTorchAutocast::test_invalid_device, test/test_autocast.py::TestTorchAutocast::test_non_string_device 2024-12-18T00:42:31.3110633Z 2024-12-18T00:42:31.3110814Z Running test_torch 1/1 ... [2024-12-18 00:42:31.309662] 2024-12-18T00:42:31.3111206Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:42:31.3112205Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:42:31.310026] 2024-12-18T00:47:49.8458017Z 2024-12-18T00:47:49.8459155Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_e6fbf3520a3b0c44_.log 2024-12-18T00:47:49.8766202Z Running 1022 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_ressurecting_clear, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_dim_function_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_deterministic_path_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_one_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_deprecated_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_pass_itself_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_accumulation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_separate_unscale_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_penalty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_state_dict_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_sparse_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_update_scale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_large_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_scalars_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_non_accumulate_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_mean_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_prod_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_float8_e4m3fn, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_float8_e4m3fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_float8_e5m2, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_float8_e5m2fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_select_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_invalid_shapes_grid_sampler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_is_set_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_is_signed_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fn, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e5m2, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e5m2fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumprod_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumsum_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_logcumsumexp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_lognormal_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_inplace_noncontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_large_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_clone_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_consistency_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_cpu_and_cuda_ops_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_empty_like_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_factory_like_functions_preserve_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_operators_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_preserved_after_permute_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_propagation_rules_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_shortcuts_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_module_share_memory_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_w_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_wo_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_gpu_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_rng_state_advance_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_copy_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveMaxPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AvgPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_CTCLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_EmbeddingBag_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_FractionalMaxPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_FractionalMaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_NLLLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_bincount_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_histc_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bicubic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_linear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_trilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_kthvalue_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_median_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_accumulate_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint2x4, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nullary_op_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pairwise_distance_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_norm_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pickle_gradscaler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pin_memory_from_constructor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_repeat_interleave_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scalar_check_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_non_unique_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_one_dim_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_zero_size_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_serialization_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_default_tensor_type_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_shift_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_skip_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_False_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_True_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2024-12-18T00:47:49.9061450Z 2024-12-18T00:47:49.9061682Z Running test_multiprocessing 1/1 ... [2024-12-18 00:47:49.848090] 2024-12-18T00:47:49.9062130Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:47:49.9063173Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:47:49.848410] 2024-12-18T00:48:30.1696233Z 2024-12-18T00:48:30.1697217Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_22da63aa03784453_.log 2024-12-18T00:48:30.1712453Z Running 41 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_meta_simple, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork 2024-12-18T00:48:30.1726597Z 2024-12-18T00:48:30.1726787Z Running test_native_mha 1/1 ... [2024-12-18 00:48:30.169849] 2024-12-18T00:48:30.1727256Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:48:30.1728331Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_native_mha.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:48:30.170164] 2024-12-18T00:49:05.8315616Z 2024-12-18T00:49:05.8318170Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_7541010acb5e9bfd_.log 2024-12-18T00:49:05.8340226Z Running 28 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_encoder_decoder_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_nested_cpu_float32 2024-12-18T00:49:05.8360975Z 2024-12-18T00:49:05.8361181Z Running test_sort_and_select 1/1 ... [2024-12-18 00:49:05.831811] 2024-12-18T00:49:05.8361604Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:49:05.8362638Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:49:05.832143] 2024-12-18T00:50:04.1242408Z 2024-12-18T00:50:04.1243880Z test_sort_and_select 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sort_and_select_1.1_8404e660e11ba3fe_.log 2024-12-18T00:50:04.1287002Z Running 112 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_dtypes_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_scalar_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_slow_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_expanded_tensor_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_4d_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_arguments_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_noncontiguous_gpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_quantized_scalar_input_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_dim_cpu 2024-12-18T00:50:04.1323044Z 2024-12-18T00:50:04.1323241Z Running nn/test_pooling 1/1 ... [2024-12-18 00:50:04.125006] 2024-12-18T00:50:04.1323653Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:50:04.1324673Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_pooling.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:50:04.125420] 2024-12-18T00:51:10.3390515Z 2024-12-18T00:51:10.3391928Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_ee9bb53316ce7ead_.log 2024-12-18T00:51:10.3431842Z Running 101 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool3d, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu 2024-12-18T00:51:10.3468204Z 2024-12-18T00:51:10.3468415Z Running test_python_dispatch 1/1 ... [2024-12-18 00:51:10.339430] 2024-12-18T00:51:10.3468841Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:51:10.3469951Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_python_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:51:10.339778] 2024-12-18T00:51:33.9636396Z 2024-12-18T00:51:33.9639885Z PRINTING LOG FILE of test_python_dispatch 1/1 (test/test-reports/test_python_dispatch_1.1_94b4652ac9ba6f1f_.log) 2024-12-18T00:51:33.9641467Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-68a026d3ffd7dc52.xml 2024-12-18T00:51:33.9642714Z ============================= test session starts ============================== 2024-12-18T00:51:33.9643494Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:51:33.9644031Z cachedir: .pytest_cache 2024-12-18T00:51:33.9644629Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:51:33.9645303Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:51:33.9645663Z configfile: pytest.ini 2024-12-18T00:51:33.9646309Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:51:33.9647352Z collecting ... collected 119 items 2024-12-18T00:51:33.9647929Z stepcurrent: Cannot find last run test, not skipping 2024-12-18T00:51:33.9693238Z Running 119 items in this shard: test/test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed, test/test_python_dispatch.py::TestPythonRegistration::test_alias_analysis, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_fallback, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset, test/test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls, test/test_python_dispatch.py::TestPythonRegistration::test_finalizer, test/test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries, test/test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum, test/test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator, test/test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_error_cases, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_multiple_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_no_returns, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_one_return, test/test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_with_optional, test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint, test/test_python_dispatch.py::TestPythonDispatch::test_all_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr, test/test_python_dispatch.py::TestPythonDispatch::test_basic, test/test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor, test/test_python_dispatch.py::TestPythonDispatch::test_custom_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode, test/test_python_dispatch.py::TestPythonDispatch::test_exception_handling, test/test_python_dispatch.py::TestPythonDispatch::test_fancy_strides, test/test_python_dispatch.py::TestPythonDispatch::test_format, test/test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode, test/test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack, test/test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_invalid_ret, test/test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default, test/test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_like, test/test_python_dispatch.py::TestPythonDispatch::test_list_ret, test/test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata, test/test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug, test/test_python_dispatch.py::TestPythonDispatch::test_mode_detection, test/test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode, test/test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_new_ones, test/test_python_dispatch.py::TestPythonDispatch::test_none_wrapping, test/test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode, test/test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list, test/test_python_dispatch.py::TestPythonDispatch::test_out, test/test_python_dispatch.py::TestPythonDispatch::test_produce_real_type, test/test_python_dispatch.py::TestPythonDispatch::test_record_stream, test/test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride, test/test_python_dispatch.py::TestPythonDispatch::test_return_stream, test/test_python_dispatch.py::TestPythonDispatch::test_set_data, test/test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach, test/test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_storage, test/test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object, test/test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_autograd_device_check, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_creation, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors, test/test_python_dispatch.py::TestPythonDispatch::test_version, test/test_python_dispatch.py::TestPythonDispatch::test_view_returns_alias_under_torch_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately, test/test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_reentrant_dispatch_with_mode, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes, test/test_python_dispatch.py::TestPythonDispatcher::test_basic, test/test_python_dispatch.py::TestPythonDispatcher::test_lstsq, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_fft_fft2_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 2024-12-18T00:51:33.9736312Z 2024-12-18T00:51:33.9736667Z test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed PASSED [0.2287s] [ 0%] 2024-12-18T00:51:33.9737437Z test_python_dispatch.py::TestPythonRegistration::test_alias_analysis PASSED [0.1750s] [ 1%] 2024-12-18T00:51:33.9738213Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library PASSED [0.0861s] [ 2%] 2024-12-18T00:51:33.9739177Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing PASSED [0.0453s] [ 3%] 2024-12-18T00:51:33.9740122Z test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing PASSED [0.0588s] [ 4%] 2024-12-18T00:51:33.9741030Z test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind PASSED [0.0180s] [ 5%] 2024-12-18T00:51:33.9741877Z test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable PASSED [0.0416s] [ 5%] 2024-12-18T00:51:33.9742732Z test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg PASSED [0.0540s] [ 6%] 2024-12-18T00:51:33.9744026Z test_python_dispatch.py::TestPythonRegistration::test_fallback SKIPPED [0.1095s] ( is not 2024-12-18T00:51:33.9744927Z 2024-12-18T00:51:33.9744932Z 2024-12-18T00:51:33.9745147Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:51:33.9745574Z import torch._dynamo 2024-12-18T00:51:33.9745897Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:51:33.9746146Z 2024-12-18T00:51:33.9746150Z 2024-12-18T00:51:33.9746338Z To execute this test, run the following from the base repo dir: 2024-12-18T00:51:33.9746990Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_fallback 2024-12-18T00:51:33.9747462Z 2024-12-18T00:51:33.9747722Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 7%] 2024-12-18T00:51:33.9748443Z test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough PASSED [0.0942s] [ 8%] 2024-12-18T00:51:33.9749432Z test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset SKIPPED [0.0890s] (ValueError: not enough values to unpack (expected 2, got 1) 2024-12-18T00:51:33.9750077Z 2024-12-18T00:51:33.9750080Z 2024-12-18T00:51:33.9750302Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:51:33.9750729Z import torch._dynamo 2024-12-18T00:51:33.9751035Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:51:33.9751291Z 2024-12-18T00:51:33.9751295Z 2024-12-18T00:51:33.9751483Z To execute this test, run the following from the base repo dir: 2024-12-18T00:51:33.9752155Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_fallback_keyset 2024-12-18T00:51:33.9752650Z 2024-12-18T00:51:33.9752907Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 9%] 2024-12-18T00:51:33.9753708Z test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls PASSED [0.0530s] [ 10%] 2024-12-18T00:51:33.9754609Z test_python_dispatch.py::TestPythonRegistration::test_finalizer SKIPPED [0.1810s] (Scalars are not equal! 2024-12-18T00:51:33.9755175Z 2024-12-18T00:51:33.9755291Z Expected 2 but got 8. 2024-12-18T00:51:33.9755548Z Absolute difference: 6 2024-12-18T00:51:33.9755936Z Relative difference: 3.0 2024-12-18T00:51:33.9756121Z 2024-12-18T00:51:33.9756309Z To execute this test, run the following from the base repo dir: 2024-12-18T00:51:33.9756966Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_finalizer 2024-12-18T00:51:33.9757423Z 2024-12-18T00:51:33.9757698Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0) [ 10%] 2024-12-18T00:51:33.9759751Z test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries SKIPPED [0.0141s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142460 for platform(s) asan, linux, rocm, slow, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 11%] 2024-12-18T00:51:33.9763303Z test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum SKIPPED [0.0135s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142475 for platform(s) asan, linux, mac, macos, rocm, win, windows, slow. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 12%] 2024-12-18T00:51:33.9766421Z test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator SKIPPED [0.0146s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142495 for platform(s) linux, slow. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 13%] 2024-12-18T00:51:33.9769544Z test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough SKIPPED [0.0135s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142494 for platform(s) asan, linux, rocm, mac, macos, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 14%] 2024-12-18T00:51:33.9771602Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_error_cases PASSED [0.0784s] [ 15%] 2024-12-18T00:51:33.9773745Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_multiple_returns SKIPPED [0.0139s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142807 for platform(s) asan, linux, rocm, slow, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 15%] 2024-12-18T00:51:33.9777111Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_no_returns SKIPPED [0.0136s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/117834 for platform(s) asan, dynamo, linux, rocm, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 16%] 2024-12-18T00:51:33.9780457Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_one_return SKIPPED [0.0136s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/117816 for platform(s) asan, dynamo, linux, rocm, slow, win, windows, mac, macos. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 17%] 2024-12-18T00:51:33.9783965Z test_python_dispatch.py::TestPythonRegistration::test_register_functional_op_with_optional SKIPPED [0.0142s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/117871 for platform(s) asan, dynamo, linux, rocm, win, windows. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 18%] 2024-12-18T00:51:33.9786150Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint ('RERUN', {'yellow': True}) [0.0586s] [ 19%] 2024-12-18T00:51:33.9787054Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint ('RERUN', {'yellow': True}) [0.0560s] [ 19%] 2024-12-18T00:51:33.9787884Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint FAILED [0.0558s] [ 19%] 2024-12-18T00:51:33.9788336Z 2024-12-18T00:51:33.9788469Z ==================================== RERUNS ==================================== 2024-12-18T00:51:33.9788973Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:51:33.9789454Z Traceback (most recent call last): 2024-12-18T00:51:33.9790072Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 573, in test_returning_symint 2024-12-18T00:51:33.9790677Z def test_returning_symint(self) -> None: 2024-12-18T00:51:33.9791370Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 575, in torch_dynamo_resume_in_test_returning_symint_at_574 2024-12-18T00:51:33.9792116Z fake_tensor_mode = FakeTensorMode(shape_env=shape_env) 2024-12-18T00:51:33.9792800Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 1380, in __call__ 2024-12-18T00:51:33.9793447Z return self._torchdynamo_orig_callable( 2024-12-18T00:51:33.9793769Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:51:33.9794152Z frame, cache_entry, self.hooks, frame_state, skip=1 2024-12-18T00:51:33.9794627Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9794944Z ) 2024-12-18T00:51:33.9795154Z ^ 2024-12-18T00:51:33.9795718Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 1164, in __call__ 2024-12-18T00:51:33.9796352Z result = self._inner_convert( 2024-12-18T00:51:33.9796723Z frame, cache_entry, hooks, frame_state, skip=skip + 1 2024-12-18T00:51:33.9797097Z ) 2024-12-18T00:51:33.9797618Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 547, in __call__ 2024-12-18T00:51:33.9798403Z return _compile( 2024-12-18T00:51:33.9798651Z frame.f_code, 2024-12-18T00:51:33.9798904Z ...<14 lines>... 2024-12-18T00:51:33.9799161Z skip=skip + 1, 2024-12-18T00:51:33.9799413Z ) 2024-12-18T00:51:33.9799921Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 986, in _compile 2024-12-18T00:51:33.9800638Z guarded_code = compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:51:33.9801388Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 715, in compile_inner 2024-12-18T00:51:33.9802097Z return _compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:51:33.9802786Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_utils_internal.py", line 95, in wrapper_function 2024-12-18T00:51:33.9803414Z return function(*args, **kwargs) 2024-12-18T00:51:33.9804037Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 750, in _compile_inner 2024-12-18T00:51:33.9804723Z out_code = transform_code_object(code, transform) 2024-12-18T00:51:33.9805495Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object 2024-12-18T00:51:33.9806260Z transformations(instructions, code_options) 2024-12-18T00:51:33.9806625Z ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9807362Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 231, in _fn 2024-12-18T00:51:33.9807948Z return fn(*args, **kwargs) 2024-12-18T00:51:33.9808539Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 662, in transform 2024-12-18T00:51:33.9809149Z tracer.run() 2024-12-18T00:51:33.9809388Z ~~~~~~~~~~^^ 2024-12-18T00:51:33.9809915Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2868, in run 2024-12-18T00:51:33.9810516Z super().run() 2024-12-18T00:51:33.9810743Z ~~~~~~~~~~~^^ 2024-12-18T00:51:33.9811271Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 1052, in run 2024-12-18T00:51:33.9811872Z while self.step(): 2024-12-18T00:51:33.9812128Z ~~~~~~~~~^^ 2024-12-18T00:51:33.9812676Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 962, in step 2024-12-18T00:51:33.9813319Z self.dispatch_table[inst.opcode](self, inst) 2024-12-18T00:51:33.9813765Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^ 2024-12-18T00:51:33.9814393Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 659, in wrapper 2024-12-18T00:51:33.9815019Z return inner_fn(self, inst) 2024-12-18T00:51:33.9815617Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2341, in CALL 2024-12-18T00:51:33.9816220Z self._call(inst) 2024-12-18T00:51:33.9816455Z ~~~~~~~~~~^^^^^^ 2024-12-18T00:51:33.9816999Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2335, in _call 2024-12-18T00:51:33.9817627Z self.call_function(fn, args, kwargs) 2024-12-18T00:51:33.9817950Z ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9818587Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 897, in call_function 2024-12-18T00:51:33.9819354Z self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] 2024-12-18T00:51:33.9819809Z ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9820460Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/torch.py", line 953, in call_function 2024-12-18T00:51:33.9821113Z tensor_variable = wrap_fx_proxy( 2024-12-18T00:51:33.9821419Z tx=tx, 2024-12-18T00:51:33.9821636Z ...<4 lines>... 2024-12-18T00:51:33.9821875Z ), 2024-12-18T00:51:33.9822090Z ) 2024-12-18T00:51:33.9822655Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2153, in wrap_fx_proxy 2024-12-18T00:51:33.9823405Z return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs) 2024-12-18T00:51:33.9824170Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2219, in wrap_fx_proxy_cls 2024-12-18T00:51:33.9824839Z return _wrap_fx_proxy( 2024-12-18T00:51:33.9825216Z target_cls, tx, proxy, example_value, subclass_type, **options 2024-12-18T00:51:33.9825611Z ) 2024-12-18T00:51:33.9826173Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2315, in _wrap_fx_proxy 2024-12-18T00:51:33.9826960Z example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True) 2024-12-18T00:51:33.9827693Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2536, in get_fake_value 2024-12-18T00:51:33.9828433Z raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None 2024-12-18T00:51:33.9829180Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2471, in get_fake_value 2024-12-18T00:51:33.9829791Z ret_val = wrap_fake_exception( 2024-12-18T00:51:33.9830177Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:51:33.9830631Z ) 2024-12-18T00:51:33.9831154Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2017, in wrap_fake_exception 2024-12-18T00:51:33.9831776Z return fn() 2024-12-18T00:51:33.9832286Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2472, in 2024-12-18T00:51:33.9832939Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:51:33.9833351Z ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9833946Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2604, in run_node 2024-12-18T00:51:33.9834595Z raise RuntimeError(make_error_message(e)).with_traceback( 2024-12-18T00:51:33.9834990Z e.__traceback__ 2024-12-18T00:51:33.9835242Z ) from e 2024-12-18T00:51:33.9835843Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2586, in run_node 2024-12-18T00:51:33.9836447Z return node.target(*args, **kwargs) 2024-12-18T00:51:33.9836753Z ~~~~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9837383Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_stats.py", line 21, in wrapper 2024-12-18T00:51:33.9837953Z return fn(*args, **kwargs) 2024-12-18T00:51:33.9838595Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1276, in __torch_dispatch__ 2024-12-18T00:51:33.9839304Z return self.dispatch(func, types, args, kwargs) 2024-12-18T00:51:33.9839674Z ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9840288Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1816, in dispatch 2024-12-18T00:51:33.9841001Z return self._cached_dispatch_impl(func, types, args, kwargs) 2024-12-18T00:51:33.9841430Z ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9842149Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1377, in _cached_dispatch_impl 2024-12-18T00:51:33.9842915Z output = self._dispatch_impl(func, types, args, kwargs) 2024-12-18T00:51:33.9843643Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2354, in _dispatch_impl 2024-12-18T00:51:33.9844329Z op_impl_out = op_impl(self, func, *args, **kwargs) 2024-12-18T00:51:33.9845008Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_impls.py", line 188, in constructors 2024-12-18T00:51:33.9845682Z with in_kernel_invocation_manager(fake_mode): 2024-12-18T00:51:33.9846043Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^ 2024-12-18T00:51:33.9846532Z File "/opt/conda/envs/py_3.13/lib/python3.13/contextlib.py", line 141, in __enter__ 2024-12-18T00:51:33.9847022Z return next(self.gen) 2024-12-18T00:51:33.9847675Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 509, in in_kernel_invocation_manager 2024-12-18T00:51:33.9848503Z assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" 2024-12-18T00:51:33.9848933Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9849625Z torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:51:33.9850326Z True, False 2024-12-18T00:51:33.9850474Z 2024-12-18T00:51:33.9850569Z from user code: 2024-12-18T00:51:33.9851185Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:51:33.9851918Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:51:33.9852175Z 2024-12-18T00:51:33.9852404Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:51:33.9852735Z 2024-12-18T00:51:33.9852739Z 2024-12-18T00:51:33.9853062Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:51:33.9853470Z import torch._dynamo 2024-12-18T00:51:33.9853799Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:51:33.9854060Z 2024-12-18T00:51:33.9854064Z 2024-12-18T00:51:33.9854258Z To execute this test, run the following from the base repo dir: 2024-12-18T00:51:33.9854950Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:51:33.9855436Z 2024-12-18T00:51:33.9855691Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:51:33.9856290Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:51:33.9856756Z Traceback (most recent call last): 2024-12-18T00:51:33.9857326Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 573, in test_returning_symint 2024-12-18T00:51:33.9857935Z def test_returning_symint(self) -> None: 2024-12-18T00:51:33.9858629Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 575, in torch_dynamo_resume_in_test_returning_symint_at_574 2024-12-18T00:51:33.9859427Z fake_tensor_mode = FakeTensorMode(shape_env=shape_env) 2024-12-18T00:51:33.9860117Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 1380, in __call__ 2024-12-18T00:51:33.9860743Z return self._torchdynamo_orig_callable( 2024-12-18T00:51:33.9861077Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:51:33.9861442Z frame, cache_entry, self.hooks, frame_state, skip=1 2024-12-18T00:51:33.9861825Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9862140Z ) 2024-12-18T00:51:33.9862334Z ^ 2024-12-18T00:51:33.9862850Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 1164, in __call__ 2024-12-18T00:51:33.9863473Z result = self._inner_convert( 2024-12-18T00:51:33.9863840Z frame, cache_entry, hooks, frame_state, skip=skip + 1 2024-12-18T00:51:33.9864211Z ) 2024-12-18T00:51:33.9864722Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 547, in __call__ 2024-12-18T00:51:33.9865328Z return _compile( 2024-12-18T00:51:33.9865585Z frame.f_code, 2024-12-18T00:51:33.9865839Z ...<14 lines>... 2024-12-18T00:51:33.9866091Z skip=skip + 1, 2024-12-18T00:51:33.9866327Z ) 2024-12-18T00:51:33.9866847Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 986, in _compile 2024-12-18T00:51:33.9867561Z guarded_code = compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:51:33.9868291Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 715, in compile_inner 2024-12-18T00:51:33.9869002Z return _compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:51:33.9869686Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_utils_internal.py", line 95, in wrapper_function 2024-12-18T00:51:33.9870299Z return function(*args, **kwargs) 2024-12-18T00:51:33.9870932Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 750, in _compile_inner 2024-12-18T00:51:33.9871614Z out_code = transform_code_object(code, transform) 2024-12-18T00:51:33.9872379Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object 2024-12-18T00:51:33.9873140Z transformations(instructions, code_options) 2024-12-18T00:51:33.9873499Z ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9874081Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 231, in _fn 2024-12-18T00:51:33.9874680Z return fn(*args, **kwargs) 2024-12-18T00:51:33.9875269Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 662, in transform 2024-12-18T00:51:33.9876055Z tracer.run() 2024-12-18T00:51:33.9876298Z ~~~~~~~~~~^^ 2024-12-18T00:51:33.9876829Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2868, in run 2024-12-18T00:51:33.9877440Z super().run() 2024-12-18T00:51:33.9877682Z ~~~~~~~~~~~^^ 2024-12-18T00:51:33.9878216Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 1052, in run 2024-12-18T00:51:33.9878816Z while self.step(): 2024-12-18T00:51:33.9879071Z ~~~~~~~~~^^ 2024-12-18T00:51:33.9879601Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 962, in step 2024-12-18T00:51:33.9880244Z self.dispatch_table[inst.opcode](self, inst) 2024-12-18T00:51:33.9880604Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^ 2024-12-18T00:51:33.9881228Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 659, in wrapper 2024-12-18T00:51:33.9881855Z return inner_fn(self, inst) 2024-12-18T00:51:33.9882495Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2341, in CALL 2024-12-18T00:51:33.9883100Z self._call(inst) 2024-12-18T00:51:33.9883348Z ~~~~~~~~~~^^^^^^ 2024-12-18T00:51:33.9883895Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2335, in _call 2024-12-18T00:51:33.9884528Z self.call_function(fn, args, kwargs) 2024-12-18T00:51:33.9884850Z ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9885474Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 897, in call_function 2024-12-18T00:51:33.9886249Z self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] 2024-12-18T00:51:33.9886700Z ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9887345Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/torch.py", line 953, in call_function 2024-12-18T00:51:33.9888005Z tensor_variable = wrap_fx_proxy( 2024-12-18T00:51:33.9888315Z tx=tx, 2024-12-18T00:51:33.9888537Z ...<4 lines>... 2024-12-18T00:51:33.9888772Z ), 2024-12-18T00:51:33.9888990Z ) 2024-12-18T00:51:33.9889551Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2153, in wrap_fx_proxy 2024-12-18T00:51:33.9890306Z return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs) 2024-12-18T00:51:33.9891059Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2219, in wrap_fx_proxy_cls 2024-12-18T00:51:33.9891745Z return _wrap_fx_proxy( 2024-12-18T00:51:33.9892122Z target_cls, tx, proxy, example_value, subclass_type, **options 2024-12-18T00:51:33.9892521Z ) 2024-12-18T00:51:33.9893092Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2315, in _wrap_fx_proxy 2024-12-18T00:51:33.9893875Z example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True) 2024-12-18T00:51:33.9894724Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2536, in get_fake_value 2024-12-18T00:51:33.9895471Z raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None 2024-12-18T00:51:33.9896213Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2471, in get_fake_value 2024-12-18T00:51:33.9896828Z ret_val = wrap_fake_exception( 2024-12-18T00:51:33.9897216Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:51:33.9897583Z ) 2024-12-18T00:51:33.9898320Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2017, in wrap_fake_exception 2024-12-18T00:51:33.9898950Z return fn() 2024-12-18T00:51:33.9899462Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2472, in 2024-12-18T00:51:33.9900285Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:51:33.9900707Z ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9901298Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2604, in run_node 2024-12-18T00:51:33.9901962Z raise RuntimeError(make_error_message(e)).with_traceback( 2024-12-18T00:51:33.9902356Z e.__traceback__ 2024-12-18T00:51:33.9902619Z ) from e 2024-12-18T00:51:33.9903123Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2586, in run_node 2024-12-18T00:51:33.9903707Z return node.target(*args, **kwargs) 2024-12-18T00:51:33.9904034Z ~~~~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9904584Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_stats.py", line 21, in wrapper 2024-12-18T00:51:33.9905154Z return fn(*args, **kwargs) 2024-12-18T00:51:33.9905811Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1276, in __torch_dispatch__ 2024-12-18T00:51:33.9906597Z return self.dispatch(func, types, args, kwargs) 2024-12-18T00:51:33.9906953Z ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9907587Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1816, in dispatch 2024-12-18T00:51:33.9908299Z return self._cached_dispatch_impl(func, types, args, kwargs) 2024-12-18T00:51:33.9908730Z ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9909438Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1377, in _cached_dispatch_impl 2024-12-18T00:51:33.9910195Z output = self._dispatch_impl(func, types, args, kwargs) 2024-12-18T00:51:33.9910902Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2354, in _dispatch_impl 2024-12-18T00:51:33.9911615Z op_impl_out = op_impl(self, func, *args, **kwargs) 2024-12-18T00:51:33.9912289Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_impls.py", line 188, in constructors 2024-12-18T00:51:33.9912963Z with in_kernel_invocation_manager(fake_mode): 2024-12-18T00:51:33.9913320Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^ 2024-12-18T00:51:33.9913802Z File "/opt/conda/envs/py_3.13/lib/python3.13/contextlib.py", line 141, in __enter__ 2024-12-18T00:51:33.9914277Z return next(self.gen) 2024-12-18T00:51:33.9914936Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 509, in in_kernel_invocation_manager 2024-12-18T00:51:33.9915863Z assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" 2024-12-18T00:51:33.9916299Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9916985Z torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:51:33.9917666Z True, False 2024-12-18T00:51:33.9917812Z 2024-12-18T00:51:33.9917910Z from user code: 2024-12-18T00:51:33.9918527Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:51:33.9919256Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:51:33.9919514Z 2024-12-18T00:51:33.9919743Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:51:33.9920070Z 2024-12-18T00:51:33.9920073Z 2024-12-18T00:51:33.9920290Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:51:33.9920711Z import torch._dynamo 2024-12-18T00:51:33.9921014Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:51:33.9921269Z 2024-12-18T00:51:33.9921273Z 2024-12-18T00:51:33.9921462Z To execute this test, run the following from the base repo dir: 2024-12-18T00:51:33.9922209Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:51:33.9922710Z 2024-12-18T00:51:33.9922946Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:51:33.9923435Z =================================== FAILURES =================================== 2024-12-18T00:51:33.9923941Z _________________ TestPythonRegistration.test_returning_symint _________________ 2024-12-18T00:51:33.9924402Z Traceback (most recent call last): 2024-12-18T00:51:33.9924967Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 573, in test_returning_symint 2024-12-18T00:51:33.9925568Z def test_returning_symint(self) -> None: 2024-12-18T00:51:33.9926263Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 575, in torch_dynamo_resume_in_test_returning_symint_at_574 2024-12-18T00:51:33.9927007Z fake_tensor_mode = FakeTensorMode(shape_env=shape_env) 2024-12-18T00:51:33.9927698Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 1380, in __call__ 2024-12-18T00:51:33.9928377Z return self._torchdynamo_orig_callable( 2024-12-18T00:51:33.9928713Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ 2024-12-18T00:51:33.9929086Z frame, cache_entry, self.hooks, frame_state, skip=1 2024-12-18T00:51:33.9929468Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9929781Z ) 2024-12-18T00:51:33.9929977Z ^ 2024-12-18T00:51:33.9930496Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 1164, in __call__ 2024-12-18T00:51:33.9931119Z result = self._inner_convert( 2024-12-18T00:51:33.9931485Z frame, cache_entry, hooks, frame_state, skip=skip + 1 2024-12-18T00:51:33.9931854Z ) 2024-12-18T00:51:33.9932360Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 547, in __call__ 2024-12-18T00:51:33.9932973Z return _compile( 2024-12-18T00:51:33.9933231Z frame.f_code, 2024-12-18T00:51:33.9933485Z ...<14 lines>... 2024-12-18T00:51:33.9933741Z skip=skip + 1, 2024-12-18T00:51:33.9933979Z ) 2024-12-18T00:51:33.9934495Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 986, in _compile 2024-12-18T00:51:33.9935209Z guarded_code = compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:51:33.9935937Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 715, in compile_inner 2024-12-18T00:51:33.9936652Z return _compile_inner(code, one_graph, hooks, transform) 2024-12-18T00:51:33.9937337Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_utils_internal.py", line 95, in wrapper_function 2024-12-18T00:51:33.9937956Z return function(*args, **kwargs) 2024-12-18T00:51:33.9938593Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 750, in _compile_inner 2024-12-18T00:51:33.9939282Z out_code = transform_code_object(code, transform) 2024-12-18T00:51:33.9940051Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/bytecode_transformation.py", line 1361, in transform_code_object 2024-12-18T00:51:33.9940818Z transformations(instructions, code_options) 2024-12-18T00:51:33.9941180Z ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9941765Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 231, in _fn 2024-12-18T00:51:33.9942365Z return fn(*args, **kwargs) 2024-12-18T00:51:33.9942953Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/convert_frame.py", line 662, in transform 2024-12-18T00:51:33.9943563Z tracer.run() 2024-12-18T00:51:33.9943802Z ~~~~~~~~~~^^ 2024-12-18T00:51:33.9944330Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2868, in run 2024-12-18T00:51:33.9945013Z super().run() 2024-12-18T00:51:33.9945251Z ~~~~~~~~~~~^^ 2024-12-18T00:51:33.9945787Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 1052, in run 2024-12-18T00:51:33.9946395Z while self.step(): 2024-12-18T00:51:33.9946651Z ~~~~~~~~~^^ 2024-12-18T00:51:33.9947180Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 962, in step 2024-12-18T00:51:33.9947840Z self.dispatch_table[inst.opcode](self, inst) 2024-12-18T00:51:33.9948202Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^ 2024-12-18T00:51:33.9948833Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 659, in wrapper 2024-12-18T00:51:33.9949457Z return inner_fn(self, inst) 2024-12-18T00:51:33.9950036Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2341, in CALL 2024-12-18T00:51:33.9950645Z self._call(inst) 2024-12-18T00:51:33.9950902Z ~~~~~~~~~~^^^^^^ 2024-12-18T00:51:33.9951513Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 2335, in _call 2024-12-18T00:51:33.9952150Z self.call_function(fn, args, kwargs) 2024-12-18T00:51:33.9952479Z ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9953103Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/symbolic_convert.py", line 897, in call_function 2024-12-18T00:51:33.9953884Z self.push(fn.call_function(self, args, kwargs)) # type: ignore[arg-type] 2024-12-18T00:51:33.9954337Z ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9954987Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/torch.py", line 953, in call_function 2024-12-18T00:51:33.9955762Z tensor_variable = wrap_fx_proxy( 2024-12-18T00:51:33.9956074Z tx=tx, 2024-12-18T00:51:33.9956293Z ...<4 lines>... 2024-12-18T00:51:33.9956536Z ), 2024-12-18T00:51:33.9956754Z ) 2024-12-18T00:51:33.9957327Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2153, in wrap_fx_proxy 2024-12-18T00:51:33.9958078Z return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs) 2024-12-18T00:51:33.9958831Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2219, in wrap_fx_proxy_cls 2024-12-18T00:51:33.9959512Z return _wrap_fx_proxy( 2024-12-18T00:51:33.9959887Z target_cls, tx, proxy, example_value, subclass_type, **options 2024-12-18T00:51:33.9960280Z ) 2024-12-18T00:51:33.9960848Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/builder.py", line 2315, in _wrap_fx_proxy 2024-12-18T00:51:33.9961624Z example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True) 2024-12-18T00:51:33.9962356Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2536, in get_fake_value 2024-12-18T00:51:33.9963108Z raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None 2024-12-18T00:51:33.9963853Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2471, in get_fake_value 2024-12-18T00:51:33.9964467Z ret_val = wrap_fake_exception( 2024-12-18T00:51:33.9964853Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:51:33.9965221Z ) 2024-12-18T00:51:33.9965755Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2017, in wrap_fake_exception 2024-12-18T00:51:33.9966375Z return fn() 2024-12-18T00:51:33.9966878Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2472, in 2024-12-18T00:51:33.9967536Z lambda: run_node(tx.output, node, args, kwargs, nnmodule) 2024-12-18T00:51:33.9967947Z ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9968533Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2604, in run_node 2024-12-18T00:51:33.9969265Z raise RuntimeError(make_error_message(e)).with_traceback( 2024-12-18T00:51:33.9969657Z e.__traceback__ 2024-12-18T00:51:33.9969908Z ) from e 2024-12-18T00:51:33.9970407Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/utils.py", line 2586, in run_node 2024-12-18T00:51:33.9970990Z return node.target(*args, **kwargs) 2024-12-18T00:51:33.9971309Z ~~~~~~~~~~~^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9971861Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_stats.py", line 21, in wrapper 2024-12-18T00:51:33.9972428Z return fn(*args, **kwargs) 2024-12-18T00:51:33.9973072Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1276, in __torch_dispatch__ 2024-12-18T00:51:33.9973787Z return self.dispatch(func, types, args, kwargs) 2024-12-18T00:51:33.9974146Z ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9974894Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1816, in dispatch 2024-12-18T00:51:33.9975612Z return self._cached_dispatch_impl(func, types, args, kwargs) 2024-12-18T00:51:33.9976045Z ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9976760Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 1377, in _cached_dispatch_impl 2024-12-18T00:51:33.9977514Z output = self._dispatch_impl(func, types, args, kwargs) 2024-12-18T00:51:33.9978220Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 2354, in _dispatch_impl 2024-12-18T00:51:33.9978921Z op_impl_out = op_impl(self, func, *args, **kwargs) 2024-12-18T00:51:33.9979594Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_impls.py", line 188, in constructors 2024-12-18T00:51:33.9980269Z with in_kernel_invocation_manager(fake_mode): 2024-12-18T00:51:33.9980629Z ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^ 2024-12-18T00:51:33.9981114Z File "/opt/conda/envs/py_3.13/lib/python3.13/contextlib.py", line 141, in __enter__ 2024-12-18T00:51:33.9981591Z return next(self.gen) 2024-12-18T00:51:33.9982253Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/fake_tensor.py", line 509, in in_kernel_invocation_manager 2024-12-18T00:51:33.9983064Z assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" 2024-12-18T00:51:33.9983488Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:33.9984171Z torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:51:33.9984850Z True, False 2024-12-18T00:51:33.9984989Z 2024-12-18T00:51:33.9985084Z from user code: 2024-12-18T00:51:33.9985688Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:51:33.9986420Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:51:33.9986675Z 2024-12-18T00:51:33.9986905Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:51:33.9987231Z 2024-12-18T00:51:33.9987235Z 2024-12-18T00:51:33.9987450Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:51:33.9987868Z import torch._dynamo 2024-12-18T00:51:33.9988171Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:51:33.9988428Z 2024-12-18T00:51:33.9988432Z 2024-12-18T00:51:33.9988618Z To execute this test, run the following from the base repo dir: 2024-12-18T00:51:33.9989297Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:51:33.9989797Z 2024-12-18T00:51:33.9990030Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:51:33.9991018Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-68a026d3ffd7dc52.xml - 2024-12-18T00:51:33.9991867Z =========================== short test summary info ============================ 2024-12-18T00:51:33.9993014Z FAILED [0.0558s] test_python_dispatch.py::TestPythonRegistration::test_returning_symint - torch._dynamo.exc.TorchRuntimeError: Failed running call_function (*(2, 3), **{}): 2024-12-18T00:51:33.9994059Z True, False 2024-12-18T00:51:33.9994233Z 2024-12-18T00:51:33.9994354Z from user code: 2024-12-18T00:51:33.9995002Z File "/var/lib/jenkins/workspace/test/test_python_dispatch.py", line 577, in torch_dynamo_resume_in_test_returning_symint_at_575 2024-12-18T00:51:33.9995789Z ft = fake_tensor_mode.from_tensor(torch.rand(2, 3)) 2024-12-18T00:51:33.9996046Z 2024-12-18T00:51:33.9996272Z Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information 2024-12-18T00:51:33.9996604Z 2024-12-18T00:51:33.9996608Z 2024-12-18T00:51:33.9996894Z You can suppress this exception and fall back to eager by setting: 2024-12-18T00:51:33.9997306Z import torch._dynamo 2024-12-18T00:51:33.9997628Z torch._dynamo.config.suppress_errors = True 2024-12-18T00:51:33.9998070Z 2024-12-18T00:51:33.9998075Z 2024-12-18T00:51:33.9998273Z To execute this test, run the following from the base repo dir: 2024-12-18T00:51:33.9998957Z PYTORCH_TEST_WITH_DYNAMO=1 python test/test_python_dispatch.py TestPythonRegistration.test_returning_symint 2024-12-18T00:51:34.0010271Z 2024-12-18T00:51:34.0010725Z This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 2024-12-18T00:51:34.0011478Z !!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!! 2024-12-18T00:51:34.0011956Z ============== 1 failed, 11 passed, 11 skipped, 2 rerun in 1.67s =============== 2024-12-18T00:51:34.0012366Z Got exit code 1 2024-12-18T00:51:34.0012639Z Retrying single test... 2024-12-18T00:51:34.0013251Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-3b2068afe02e9cd4.xml 2024-12-18T00:51:34.0013948Z ============================= test session starts ============================== 2024-12-18T00:51:34.0014524Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:51:34.0015046Z cachedir: .pytest_cache 2024-12-18T00:51:34.0015646Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:51:34.0016324Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:51:34.0016643Z configfile: pytest.ini 2024-12-18T00:51:34.0017276Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:51:34.0018055Z collecting ... collected 119 items / 118 deselected / 1 selected 2024-12-18T00:51:34.0018860Z stepcurrent: skipping 22 already run items. Running only test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint 2024-12-18T00:51:34.0019546Z Running 1 items in this shard 2024-12-18T00:51:34.0019751Z 2024-12-18T00:51:34.0020077Z test_python_dispatch.py::TestPythonRegistration::test_returning_symint PASSED [0.5190s] [100%] 2024-12-18T00:51:34.0020530Z 2024-12-18T00:51:34.0021103Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-3b2068afe02e9cd4.xml - 2024-12-18T00:51:34.0021968Z ====================== 1 passed, 118 deselected in 0.55s ======================= 2024-12-18T00:51:34.0022350Z Got exit code 0 2024-12-18T00:51:34.0022717Z Test succeeeded in new process, continuing with the rest of the tests 2024-12-18T00:51:34.0023479Z Test results will be stored in test-reports/python-pytest/test_python_dispatch/test_python_dispatch-6e7bf2ceb3aacf81.xml 2024-12-18T00:51:34.0024159Z ============================= test session starts ============================== 2024-12-18T00:51:34.0024943Z platform linux -- Python 3.13.0, pytest-7.3.2, pluggy-1.5.0 -- /opt/conda/envs/py_3.13/bin/python 2024-12-18T00:51:34.0025464Z cachedir: .pytest_cache 2024-12-18T00:51:34.0026075Z hypothesis profile 'pytorch_ci' -> database=None, max_examples=50, derandomize=True, suppress_health_check=[HealthCheck.too_slow] 2024-12-18T00:51:34.0026748Z rootdir: /var/lib/jenkins/workspace 2024-12-18T00:51:34.0027061Z configfile: pytest.ini 2024-12-18T00:51:34.0027699Z plugins: hypothesis-5.35.1, cpp-2.3.0, flakefinder-1.1.0, rerunfailures-14.0, subtests-0.13.1, xdist-3.3.1, xdoctest-1.1.0, typeguard-4.3.0 2024-12-18T00:51:34.0028474Z collecting ... collected 119 items / 23 deselected / 96 selected 2024-12-18T00:51:34.0028920Z stepcurrent: skipping 23 already run items. 2024-12-18T00:51:34.0029274Z Running 96 items in this shard 2024-12-18T00:51:34.0029461Z 2024-12-18T00:51:34.0029768Z test_python_dispatch.py::TestPythonDispatch::test_all_same_mode PASSED [0.0557s] [ 1%] 2024-12-18T00:51:34.0030473Z test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr PASSED [0.1192s] [ 2%] 2024-12-18T00:51:34.0031263Z test_python_dispatch.py::TestPythonDispatch::test_basic PASSED [0.0627s] [ 3%] 2024-12-18T00:51:34.0032017Z test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode PASSED [0.0529s] [ 4%] 2024-12-18T00:51:34.0032831Z test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor PASSED [0.0191s] [ 5%] 2024-12-18T00:51:34.0033558Z test_python_dispatch.py::TestPythonDispatch::test_custom_autograd PASSED [0.0947s] [ 6%] 2024-12-18T00:51:34.0034342Z test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes PASSED [0.2852s] [ 7%] 2024-12-18T00:51:34.0035190Z test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path PASSED [0.0850s] [ 8%] 2024-12-18T00:51:34.0036144Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass PASSED [0.0454s] [ 9%] 2024-12-18T00:51:34.0036961Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass PASSED [0.1915s] [ 10%] 2024-12-18T00:51:34.0037895Z test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type PASSED [0.0800s] [ 11%] 2024-12-18T00:51:34.0038870Z test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once PASSED [0.0343s] [ 12%] 2024-12-18T00:51:34.0039665Z test_python_dispatch.py::TestPythonDispatch::test_device_slowpath PASSED [0.1288s] [ 13%] 2024-12-18T00:51:34.0040365Z test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath PASSED [0.1493s] [ 14%] 2024-12-18T00:51:34.0041069Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call PASSED [0.0407s] [ 15%] 2024-12-18T00:51:34.0041844Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg PASSED [0.0415s] [ 16%] 2024-12-18T00:51:34.0042652Z test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd PASSED [0.0769s] [ 17%] 2024-12-18T00:51:34.0043488Z test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode PASSED [0.0201s] [ 18%] 2024-12-18T00:51:34.0044275Z test_python_dispatch.py::TestPythonDispatch::test_exception_handling PASSED [0.0199s] [ 19%] 2024-12-18T00:51:34.0044990Z test_python_dispatch.py::TestPythonDispatch::test_fancy_strides PASSED [0.0529s] [ 20%] 2024-12-18T00:51:34.0045656Z test_python_dispatch.py::TestPythonDispatch::test_format PASSED [0.1372s] [ 21%] 2024-12-18T00:51:34.0046303Z test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode PASSED [0.0132s] [ 22%] 2024-12-18T00:51:34.0046996Z test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack PASSED [0.0127s] [ 23%] 2024-12-18T00:51:34.0047781Z test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass PASSED [0.0773s] [ 25%] 2024-12-18T00:51:34.0048550Z test_python_dispatch.py::TestPythonDispatch::test_invalid_ret PASSED [0.0842s] [ 26%] 2024-12-18T00:51:34.0049353Z test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path PASSED [0.2212s] [ 27%] 2024-12-18T00:51:34.0050075Z test_python_dispatch.py::TestPythonDispatch::test_kwarg_only PASSED [0.0410s] [ 28%] 2024-12-18T00:51:34.0050822Z test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default PASSED [0.0374s] [ 29%] 2024-12-18T00:51:34.0051607Z test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path PASSED [0.2214s] [ 30%] 2024-12-18T00:51:34.0052283Z test_python_dispatch.py::TestPythonDispatch::test_like PASSED [0.0288s] [ 31%] 2024-12-18T00:51:34.0053271Z test_python_dispatch.py::TestPythonDispatch::test_list_ret SKIPPED [0.0677s] (This test passed, maybe we can remove `test/dynamo_skips/TestPythonDispatch.test_list_ret`) [ 32%] 2024-12-18T00:51:34.0054332Z test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass PASSED [0.2638s] [ 33%] 2024-12-18T00:51:34.0055094Z test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes PASSED [0.0222s] [ 34%] 2024-12-18T00:51:34.0055938Z test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc PASSED [0.0262s] [ 35%] 2024-12-18T00:51:34.0056812Z test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata PASSED [0.0668s] [ 36%] 2024-12-18T00:51:34.0057628Z test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug PASSED [0.0244s] [ 37%] 2024-12-18T00:51:34.0058334Z test_python_dispatch.py::TestPythonDispatch::test_mode_detection PASSED [0.0125s] [ 38%] 2024-12-18T00:51:34.0059073Z test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass PASSED [0.0196s] [ 39%] 2024-12-18T00:51:34.0059835Z test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass PASSED [0.0550s] [ 40%] 2024-12-18T00:51:34.0060630Z test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode PASSED [0.0431s] [ 41%] 2024-12-18T00:51:34.0061397Z test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode PASSED [0.0377s] [ 42%] 2024-12-18T00:51:34.0062090Z test_python_dispatch.py::TestPythonDispatch::test_new_ones PASSED [0.0281s] [ 43%] 2024-12-18T00:51:34.0063160Z test_python_dispatch.py::TestPythonDispatch::test_none_wrapping W1218 00:51:29.051000 1997 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] torch._dynamo hit config.cache_size_limit (8) 2024-12-18T00:51:34.0064540Z W1218 00:51:29.051000 1997 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] function: '__torch_dispatch__' (/var/lib/jenkins/workspace/test/test_python_dispatch.py:1940) 2024-12-18T00:51:34.0065670Z W1218 00:51:29.051000 1997 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] last reason: 4/0: GLOBAL_STATE changed: grad_mode 2024-12-18T00:51:34.0066675Z W1218 00:51:29.051000 1997 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] To log all recompilation reasons, use TORCH_LOGS="recompiles". 2024-12-18T00:51:34.0067912Z W1218 00:51:29.051000 1997 site-packages/torch/_dynamo/convert_frame.py:906] [4/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html. 2024-12-18T00:51:34.0068787Z PASSED [1.2459s] [ 44%] 2024-12-18T00:51:34.0069262Z test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode PASSED [0.0411s] [ 45%] 2024-12-18T00:51:34.0070011Z test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list PASSED [0.0609s] [ 46%] 2024-12-18T00:51:34.0070705Z test_python_dispatch.py::TestPythonDispatch::test_out PASSED [0.0359s] [ 47%] 2024-12-18T00:51:34.0071387Z test_python_dispatch.py::TestPythonDispatch::test_produce_real_type PASSED [0.0359s] [ 48%] 2024-12-18T00:51:34.0072100Z test_python_dispatch.py::TestPythonDispatch::test_record_stream PASSED [0.0200s] [ 50%] 2024-12-18T00:51:34.0072927Z test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride PASSED [0.0671s] [ 51%] 2024-12-18T00:51:34.0073740Z test_python_dispatch.py::TestPythonDispatch::test_return_stream PASSED [0.0531s] [ 52%] 2024-12-18T00:51:34.0074478Z test_python_dispatch.py::TestPythonDispatch::test_set_data PASSED [0.0589s] [ 53%] 2024-12-18T00:51:34.0075199Z test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach PASSED [0.0218s] [ 54%] 2024-12-18T00:51:34.0076044Z test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path PASSED [0.2233s] [ 55%] 2024-12-18T00:51:34.0076794Z test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass PASSED [0.0415s] [ 56%] 2024-12-18T00:51:34.0077515Z test_python_dispatch.py::TestPythonDispatch::test_storage PASSED [0.0584s] [ 57%] 2024-12-18T00:51:34.0078278Z test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object PASSED [0.0582s] [ 58%] 2024-12-18T00:51:34.0079099Z test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path PASSED [0.2220s] [ 59%] 2024-12-18T00:51:34.0079882Z test_python_dispatch.py::TestPythonDispatch::test_subclass_autograd_device_check PASSED [0.9072s] [ 60%] 2024-12-18T00:51:34.0080665Z test_python_dispatch.py::TestPythonDispatch::test_subclass_creation PASSED [0.0221s] [ 61%] 2024-12-18T00:51:34.0081453Z test_python_dispatch.py::TestPythonDispatch::test_subclass_priority PASSED [0.2291s] [ 62%] 2024-12-18T00:51:34.0082227Z test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path PASSED [0.1129s] [ 63%] 2024-12-18T00:51:34.0083051Z test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode PASSED [0.0689s] [ 64%] 2024-12-18T00:51:34.0083883Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic PASSED [0.0343s] [ 65%] 2024-12-18T00:51:34.0084727Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch PASSED [0.0405s] [ 66%] 2024-12-18T00:51:34.0085613Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority PASSED [0.0898s] [ 67%] 2024-12-18T00:51:34.0086492Z test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors PASSED [0.0417s] [ 68%] 2024-12-18T00:51:34.0087263Z test_python_dispatch.py::TestPythonDispatch::test_version PASSED [0.1123s] [ 69%] 2024-12-18T00:51:34.0088031Z test_python_dispatch.py::TestPythonDispatch::test_view_returns_alias_under_torch_dispatch PASSED [0.0207s] [ 70%] 2024-12-18T00:51:34.0088865Z test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately PASSED [0.0205s] [ 71%] 2024-12-18T00:51:34.0089633Z test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes PASSED [0.0219s] [ 72%] 2024-12-18T00:51:34.0090431Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys PASSED [0.0790s] [ 73%] 2024-12-18T00:51:34.0091362Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype PASSED [0.1277s] [ 75%] 2024-12-18T00:51:34.0092332Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_reentrant_dispatch_with_mode PASSED [0.0245s] [ 76%] 2024-12-18T00:51:34.0093207Z test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes PASSED [0.0535s] [ 77%] 2024-12-18T00:51:34.0093948Z test_python_dispatch.py::TestPythonDispatcher::test_basic PASSED [0.0526s] [ 78%] 2024-12-18T00:51:34.0094707Z test_python_dispatch.py::TestPythonDispatcher::test_lstsq PASSED [0.1185s] [ 79%] 2024-12-18T00:51:34.0095738Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32 SKIPPED [0.0165s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 80%] 2024-12-18T00:51:34.0098267Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu SKIPPED [0.0141s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/139056 for platform(s) dynamo. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 81%] 2024-12-18T00:51:34.0100678Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32 SKIPPED [0.0151s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 82%] 2024-12-18T00:51:34.0102384Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 83%] 2024-12-18T00:51:34.0103948Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 84%] 2024-12-18T00:51:34.0105537Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32 SKIPPED [0.0147s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 85%] 2024-12-18T00:51:34.0107124Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32 SKIPPED [0.0149s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 86%] 2024-12-18T00:51:34.0108767Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 87%] 2024-12-18T00:51:34.0110354Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32 SKIPPED [0.0157s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 88%] 2024-12-18T00:51:34.0111947Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 89%] 2024-12-18T00:51:34.0113608Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 90%] 2024-12-18T00:51:34.0115244Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32 SKIPPED [0.0161s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 91%] 2024-12-18T00:51:34.0116914Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32 SKIPPED [0.0154s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 92%] 2024-12-18T00:51:34.0119329Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_fft_fft2_cpu SKIPPED [0.0139s] (Test is disabled because an issue exists disabling it: https://github.com/pytorch/pytorch/issues/142021 for platform(s) dynamo. If you're seeing this on your local machine and would like to enable this test, please make sure CI is not set and you are not using the flag --import-disabled-tests.) [ 93%] 2024-12-18T00:51:34.0121614Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32 SKIPPED [0.0153s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 94%] 2024-12-18T00:51:34.0123051Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 95%] 2024-12-18T00:51:34.0125044Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu /opt/conda/envs/py_3.13/lib/python3.13/site-packages/_pytest/unraisableexception.py:78: PytestUnraisableExceptionWarning: Exception ignored in: 2024-12-18T00:51:34.0126333Z 2024-12-18T00:51:34.0126458Z Traceback (most recent call last): 2024-12-18T00:51:34.0127100Z File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/multiprocessing/reductions.py", line 50, in __del__ 2024-12-18T00:51:34.0127761Z self._free_weak_ref(self.cdata) 2024-12-18T00:51:34.0128049Z ^^^^^^^^^^^^^^^^^^^ 2024-12-18T00:51:34.0128624Z AttributeError: 'torch.multiprocessing.reductions.StorageWeakRef' object has no attribute '_free_weak_ref' 2024-12-18T00:51:34.0129248Z 2024-12-18T00:51:34.0129465Z warnings.warn(pytest.PytestUnraisableExceptionWarning(msg)) 2024-12-18T00:51:34.0129896Z PASSED [0.3104s] [ 96%] 2024-12-18T00:51:34.0130707Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32 SKIPPED [0.0156s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 97%] 2024-12-18T00:51:34.0132139Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32 SKIPPED [0.0160s] (Policy: we don't run OpInfo tests w/ Dynamo) [ 98%] 2024-12-18T00:51:34.0133553Z test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 SKIPPED [0.0158s] (Policy: we don't run OpInfo tests w/ Dynamo) [100%] 2024-12-18T00:51:34.0134314Z 2024-12-18T00:51:34.0134890Z - generated xml file: /var/lib/jenkins/workspace/test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-6e7bf2ceb3aacf81.xml - 2024-12-18T00:51:34.0135772Z ================ 76 passed, 20 skipped, 23 deselected in 8.37s ================= 2024-12-18T00:51:34.0136653Z The following tests failed and then succeeded when run in a new process['test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint'] 2024-12-18T00:51:34.0137295Z 2024-12-18T00:51:34.0137702Z FINISHED PRINTING LOG FILE of test_python_dispatch 1/1 (test/test-reports/test_python_dispatch_1.1_94b4652ac9ba6f1f_.log) 2024-12-18T00:51:34.0138213Z 2024-12-18T00:51:34.0138420Z Running test_mobile_optimizer 1/1 ... [2024-12-18 00:51:33.964597] 2024-12-18T00:51:34.0138837Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:51:34.0139872Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:51:33.965068] 2024-12-18T00:51:42.2413673Z 2024-12-18T00:51:42.2414613Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_2747b671e8e74458_.log 2024-12-18T00:51:42.2417759Z Running 7 items in this shard: test/test_mobile_optimizer.py::TestOptimizer::test_clone_module_with_class, test/test_mobile_optimizer.py::TestOptimizer::test_generate_mobile_module_lints, test/test_mobile_optimizer.py::TestOptimizer::test_hoist_conv_packed_params, test/test_mobile_optimizer.py::TestOptimizer::test_mobilenet_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_preserve_bundled_inputs_methods, test/test_mobile_optimizer.py::TestOptimizer::test_quantized_conv_no_asan_failures 2024-12-18T00:51:42.2420288Z 2024-12-18T00:51:42.2420497Z Running nn/test_convolution 1/1 ... [2024-12-18 00:51:42.241501] 2024-12-18T00:51:42.2420926Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:51:42.2421972Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_convolution.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:51:42.241820] 2024-12-18T00:57:06.1112989Z 2024-12-18T00:57:06.1114019Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_03fd51b60c87c27d_.log 2024-12-18T00:57:06.1437681Z Running 588 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv1d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_1x1, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_backward_twice, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias_v2, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_with_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_without_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_missing_argument, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_wbias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_half_cublas_gemm, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size_downsample_upsample, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose3d_correct_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv1d_issue_120547, test/nn/test_convolution.py::TestConvolutionNN::test_conv2d_discontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_issue_120406, test/nn/test_convolution.py::TestConvolutionNN::test_conv_backcompat, test/nn/test_convolution.py::TestConvolutionNN::test_conv_cudnn_memory_layout_dominance, test/nn/test_convolution.py::TestConvolutionNN::test_conv_invalid_groups, test/nn/test_convolution.py::TestConvolutionNN::test_conv_modules_raise_error_on_incorrect_input_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv_padding_mode, test/nn/test_convolution.py::TestConvolutionNN::test_conv_shapecheck, test/nn/test_convolution.py::TestConvolutionNN::test_conv_tbc, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_non_contiguous, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_noncontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_not_mutate_stride, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv1d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv3d, test/nn/test_convolution.py::TestConvolutionNN::test_mismatch_shape_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_nnpack_conv, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, test/nn/test_convolution.py::TestConvolutionNN::test_thnn_conv_strided_padded_dilated, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_large_workspace_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv3d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose3d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_contig_wrong_stride_cudnn_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_no_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_64bit_indexing_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_batch_channel3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_empty_channel3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen1d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen3d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_miopen_depthwise3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_cpu_input_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn1d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_cpu_input_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_cpu_input_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn3d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_batch_channel3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_mkldnn_empty_channel3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_contiguous_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_mismatch_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_groups_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_no_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_stride_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_strided_with_3D_input_and_weight_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_ic1_channels_last_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_nosplit_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_and_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose2d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose3d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transposed_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv2d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv3d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_conv_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float64 2024-12-18T00:57:06.1753966Z 2024-12-18T00:57:06.1754139Z Running test_nn 1/2 ... [2024-12-18 00:57:06.112420] 2024-12-18T00:57:06.1754740Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T00:57:06.1755812Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 00:57:06.112716] 2024-12-18T01:04:51.2460945Z 2024-12-18T01:04:51.2461784Z test_nn 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.2_b81b739b728fcfaa_.log 2024-12-18T01:04:51.2996662Z Running 1051 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_gaussian_nll_loss_scalar_var, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestNN::test_weighted_huber_loss, test/test_nn.py::TestNN::test_weighted_l1_loss_with_weights, test/test_nn.py::TestNN::test_weighted_mse_loss, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32 2024-12-18T01:04:51.3517776Z 2024-12-18T01:04:51.3517967Z Running test_nn 2/2 ... [2024-12-18 01:04:51.248056] 2024-12-18T01:04:51.3518366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:04:51.3519340Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:04:51.248362] 2024-12-18T01:11:34.9245397Z 2024-12-18T01:11:34.9246244Z test_nn 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_2.2_045b646911bad259_.log 2024-12-18T01:11:34.9832220Z Running 1128 items in this shard: test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_reduce, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_CTCLoss_critical_target_len, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cpu, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cuda, test/test_nn.py::TestNN::test_CTCLoss_long_targets, test/test_nn.py::TestNN::test_CTCLoss_typechecks, test/test_nn.py::TestNN::test_CTCLoss_zero_infinity, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same, test/test_nn.py::TestNN::test_Conv1d_pad_same2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv2d_padding, test/test_nn.py::TestNN::test_Conv2d_padding_cuda, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_strided, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_groups, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose3d, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_max, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx_cuda, test/test_nn.py::TestNN::test_Embedding_cuda, test/test_nn.py::TestNN::test_Embedding_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, test/test_nn.py::TestNN::test_Flatten_no_batch_dim, test/test_nn.py::TestNN::test_Flatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Fold, test/test_nn.py::TestNN::test_Fold_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_HuberLoss_delta, test/test_nn.py::TestNN::test_HuberLoss_delta_cuda, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim, test/test_nn.py::TestNN::test_Linear, test/test_nn.py::TestNN::test_Linear_cuda, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_ModuleDict, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args, test/test_nn.py::TestNN::test_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, test/test_nn.py::TestNN::test_RNN_change_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_no_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_with_dropout, test/test_nn.py::TestNN::test_RNN_cudnn_weight_norm, test/test_nn.py::TestNN::test_RNN_dropout, test/test_nn.py::TestNN::test_RNN_input_size_zero, test/test_nn.py::TestNN::test_RNN_nonlinearity, test/test_nn.py::TestNN::test_RNN_nonlinearity_passed_as_arg, test/test_nn.py::TestNN::test_RReLU, test/test_nn.py::TestNN::test_RReLU_with_up_down, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar_cuda, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReLU_no_batch_dim, test/test_nn.py::TestNN::test_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim, test/test_nn.py::TestNN::test_Sequential_add, test/test_nn.py::TestNN::test_Sequential_append, test/test_nn.py::TestNN::test_Sequential_delitem, test/test_nn.py::TestNN::test_Sequential_imul, test/test_nn.py::TestNN::test_Sequential_insert, test/test_nn.py::TestNN::test_Sequential_insert_fail_case, test/test_nn.py::TestNN::test_Sequential_mul, test/test_nn.py::TestNN::test_Sequential_pop, test/test_nn.py::TestNN::test_Sequential_setitem, test/test_nn.py::TestNN::test_Sequential_setitem_named, test/test_nn.py::TestNN::test_SiLU_no_batch_dim, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_Softplus_no_batch_dim, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softsign_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Unfold_int_input, test/test_nn.py::TestNN::test_Unfold_int_input_cuda, test/test_nn.py::TestNN::test_adaptive_log_softmax, test/test_nn.py::TestNN::test_add_module, test/test_nn.py::TestNN::test_affine_grid, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_3, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_weight_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_loss_always_nonnegative, test/test_nn.py::TestNN::test_bce_loss_broadcasts_weights, test/test_nn.py::TestNN::test_bce_loss_input_range, test/test_nn.py::TestNN::test_bce_loss_size_mismatch, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_pos_weights, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bce_with_logits_raises_if_target_and_input_are_different_size, test/test_nn.py::TestNN::test_bce_with_logits_stability, test/test_nn.py::TestNN::test_bilinear_broadcasting, test/test_nn.py::TestNN::test_bilinear_no_bias, test/test_nn.py::TestNN::test_bilinear_non_contiguous, test/test_nn.py::TestNN::test_broadcast_not_requiring_grad, test/test_nn.py::TestNN::test_buffer_not_persistent, test/test_nn.py::TestNN::test_buffer_not_persistent_assign, test/test_nn.py::TestNN::test_buffer_not_persistent_del, test/test_nn.py::TestNN::test_buffer_not_persistent_overwrite, test/test_nn.py::TestNN::test_call_supports_python_dict_output, test/test_nn.py::TestNN::test_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_children, test/test_nn.py::TestNN::test_container_copy, test/test_nn.py::TestNN::test_convert_sync_batchnorm, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_diff_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_nonexpandable_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_invalid_shape, test/test_nn.py::TestNN::test_cosine_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, test/test_nn.py::TestNN::test_cross_entropy_loss_zero_div, test/test_nn.py::TestNN::test_cudnn_forward_exception, test/test_nn.py::TestNN::test_cudnn_weight_format, test/test_nn.py::TestNN::test_dir, test/test_nn.py::TestNN::test_dir_digit, test/test_nn.py::TestNN::test_elu_inplace_gradgrad, test/test_nn.py::TestNN::test_elu_inplace_on_view, test/test_nn.py::TestNN::test_error_RNN_seq_len_zero, test/test_nn.py::TestNN::test_extra_state_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fold_invalid_arg, test/test_nn.py::TestNN::test_get_buffer_from_submodules, test/test_nn.py::TestNN::test_getattr_with_property, test/test_nn.py::TestNN::test_grid_sample_nearest_neighbor_rounding_mode_consistency, test/test_nn.py::TestNN::test_interpolate, test/test_nn.py::TestNN::test_interpolate_bicubic_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_buffer_overflow, test/test_nn.py::TestNN::test_interpolate_illegal_memory_access, test/test_nn.py::TestNN::test_interpolate_linear_1d, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_1d, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d, test/test_nn.py::TestNN::test_interpolate_nearest_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d, test/test_nn.py::TestNN::test_kl_div_log_softmax_target, test/test_nn.py::TestNN::test_kl_div_with_diff_type, test/test_nn.py::TestNN::test_kl_div_with_diff_type_log_target, test/test_nn.py::TestNN::test_layer_norm_eps, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightStrided, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSR, test/test_nn.py::TestNN::test_linear_broadcasting, test/test_nn.py::TestNN::test_linear_raise_on_scalar_input, test/test_nn.py::TestNN::test_log_softmax_dim0, test/test_nn.py::TestNN::test_log_softmax_dim0_cuda, test/test_nn.py::TestNN::test_log_softmax_dim3, test/test_nn.py::TestNN::test_log_softmax_dim3_cuda, test/test_nn.py::TestNN::test_log_softmax_lastdim, test/test_nn.py::TestNN::test_log_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_log_softmax_scalar_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial, test/test_nn.py::TestNN::test_log_softmax_spatial_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial_special, test/test_nn.py::TestNN::test_margin_ranking_loss_margin_no_reduce, test/test_nn.py::TestNN::test_max_pool1d_invalid_output_size, test/test_nn.py::TestNN::test_module_apply_inplace_op, test/test_nn.py::TestNN::test_module_to_argparse, test/test_nn.py::TestNN::test_mse_loss_size_warning, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_nn.py::TestNN::test_named_children, test/test_nn.py::TestNN::test_native_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_nested_tensor_from_mask_error, test/test_nn.py::TestNN::test_no_grad, test/test_nn.py::TestNN::test_non_leaf_parameters, test/test_nn.py::TestNN::test_normalize, test/test_nn.py::TestNN::test_pad_scalar_error, test/test_nn.py::TestNN::test_pairwise_distance, test/test_nn.py::TestNN::test_parameter_assignment, test/test_nn.py::TestNN::test_parameterlistdict_pickle, test/test_nn.py::TestNN::test_parameters_and_named_parameters, test/test_nn.py::TestNN::test_parameters_to_vector, test/test_nn.py::TestNN::test_parse_to, test/test_nn.py::TestNN::test_partial_flat_weights, test/test_nn.py::TestNN::test_pdist, test/test_nn.py::TestNN::test_pdist_cpu_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_cuda_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_empty_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_projections_lstm_check_device, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_not_tensor, test/test_nn.py::TestNN::test_register_parameter_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, test/test_nn.py::TestNN::test_softmax_functional_dim3_cuda, test/test_nn.py::TestNN::test_softmax_functional_scalar, test/test_nn.py::TestNN::test_softmax_functional_scalar_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_cuda, test/test_nn.py::TestNN::test_softmax_spatial_dtype, test/test_nn.py::TestNN::test_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_accuracy_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_backward_elemt, test/test_nn.py::TestNN::test_threshold_bfloat16_half, test/test_nn.py::TestNN::test_threshold_int, test/test_nn.py::TestNN::test_train_errors_for_invalid_mode, test/test_nn.py::TestNN::test_transformer_layer_args_check, test/test_nn.py::TestNN::test_transformerdecoder, test/test_nn.py::TestNN::test_transformerdecoderlayer, test/test_nn.py::TestNN::test_triplet_margin_loss, test/test_nn.py::TestNN::test_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_unflatten_invalid_arg, test/test_nn.py::TestNN::test_upsamplingLinear1d, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_spatial_invariance, test/test_nn.py::TestNN::test_vector_to_parameters, test/test_nn.py::TestNN::test_weight_norm, test/test_nn.py::TestNN::test_weight_norm_pickle, test/test_nn.py::TestNN::test_zero_grad, test/test_nn.py::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, test/test_nn.py::TestNNDeviceTypeCPU::test_BatchNorm_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_Bilinear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_empty_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GRU_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_raises_error_if_one_value_per_group_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LocalResponseNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate45_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate90_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_update_stats_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_channel_shuffle_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_error_if_nonfinite_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_index_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_all_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_bfloat16_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_weight_bias_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_logsigmoid_out_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_lstmcell_backward_only_one_output_grad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_TxT_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_devices_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_mask_types_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_non_recursive_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_all_ignored_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_mean_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_target_dim_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_mismatched_batch_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_replicatepad_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_retain_variables_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_save_lstm_compatibility_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_silu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smoothl1loss_backward_zero_beta_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_low_threshold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_negative_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_gelu_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_rescale_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32 2024-12-18T01:11:35.0405932Z 2024-12-18T01:11:35.0406187Z Running test_multiprocessing_spawn 1/1 ... [2024-12-18 01:11:34.926572] 2024-12-18T01:11:35.0406666Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:11:35.0407743Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing_spawn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:11:34.926941] 2024-12-18T01:14:26.8443656Z 2024-12-18T01:14:26.8444853Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_2ce599322796c149_.log 2024-12-18T01:14:26.8457058Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2024-12-18T01:14:26.8468293Z 2024-12-18T01:14:26.8468489Z Running test_overrides 1/1 ... [2024-12-18 01:14:26.844507] 2024-12-18T01:14:26.8468954Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:14:26.8470015Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_overrides.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:14:26.844809] 2024-12-18T01:19:03.2218770Z 2024-12-18T01:19:03.2219741Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_e7f29296011a3936_.log 2024-12-18T01:19:03.2700158Z Running 1466 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__version___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_data___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_device___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_dtype___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_imag___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cuda___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_ipu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_leaf___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_maia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_meta___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mkldnn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mps___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_mtia___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_nested___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_quantized___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_sparse_csr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_vulkan___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xla___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_xpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_itemsize___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_layout___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mH___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mT___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_name___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_names___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_nbytes___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_ndim___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_output_nr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_real___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_requires_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_retains_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_shape___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_volatile___get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___add__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___and__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___array_wrap__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___bool__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___complex__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___contains__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___cuda_array_interface_____get__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___deepcopy__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___div__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___dlpack__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___dlpack_device__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___eq__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___float__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___floordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___format__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ge__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___getitem__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___gt__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___iadd__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___iand__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___idiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ifloordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ilshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___imod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___imul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___index__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___int__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___invert__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ior__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___irshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___isub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ixor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___le__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___len__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___long__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___lshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___lt__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___matmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___mod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___mul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ne__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___nonzero__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___or__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___radd__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rand__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rdiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___reduce_ex__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___repr__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___reversed__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rfloordiv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rlshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmatmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmod__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rmul__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___ror__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rpow__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rrshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rshift__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rsub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___rxor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___setitem__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___setstate__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___sub__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___truediv__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor___xor__, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__autocast_to_full_precision, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__autocast_to_reduced_precision, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__clear_non_serializable_cached_data, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__coalesced_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__dimI, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__dimV, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__is_view, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nested_tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nested_tensor_storage_offsets, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nested_tensor_strides, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__nnz, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__sparse_mask_projection, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__update_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor__values, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_abs, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_abs_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_absolute_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acos, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acos_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_acosh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addbmm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcdiv_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addcmul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addmv_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_addr_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_align_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_align_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_all, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_amax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_amin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_angle, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_any, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_apply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccos_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arccosh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arcsinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_arctanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_asinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_atanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_backward, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_baddbmm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bernoulli_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bfloat16, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_left_shift_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_right_shift_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bitwise_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_bool, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_byte, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cauchy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ccol_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cdouble, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ceil_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cfloat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_chalf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_char, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_max_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clamp_min_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clip, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clip_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_clone, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_coalesce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_col_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_conj_physical_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_copysign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cos, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cos_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cosh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cov, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cross, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_crow_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cuda, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumprod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_cumsum_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_data_ptr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_deg2rad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dense_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_det, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_detach_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_diff, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_digamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dim_order, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_div, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_div_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_double, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_element_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_eq, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_eq_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erf_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_erfinv_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expand, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expand_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_expm1_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_exponential_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fill_diagonal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fix, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fix_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flip, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_float_power_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gather, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_geometric_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ger, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_greater_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_half, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_has_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_heaviside_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_histc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_hypot_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_i0_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_pinned, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_set_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_shared, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_istft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_item, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kron, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_median, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mode, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mtia, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ndimension, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nelement, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_norm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numpy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_outer, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_permute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pin_memory, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_positive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qscheme, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_random_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_record_stream, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_refine_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_post_accumulate_grad_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_requires_grad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_sparse_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_retain_grad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_roll, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_row_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_set_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_share_memory_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_short, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_smm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_mask, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_and_clear_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sspaddmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_std, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_stft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_offset, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum_to_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_svd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_mkldnn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_sparse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tolist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_topk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trace, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_untyped_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_values, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_var, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_where, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_zero_, test/test_overrides.py::TestTorchFunctionOverride::test_base, test/test_overrides.py::TestTorchFunctionOverride::test_grad, test/test_overrides.py::TestTorchFunctionOverride::test_has_torch_function_non_sequence, test/test_overrides.py::TestTorchFunctionOverride::test_mean_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_mm_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_pow_rpow, test/test_overrides.py::TestTorchFunctionOverride::test_precedence_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_tensor_subclass_propagation, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cond, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eig, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eigh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eigvals, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eigvalsh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_householder_product, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_inv, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_inv_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_ldl_factor, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_ldl_factor_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_ldl_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_lstsq, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_lu_factor, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_lu_factor_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_matrix_rank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_multi_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_pinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_solve_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_solve_triangular, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_svdvals, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_tensorinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_tensorsolve, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_vander, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_vecdot, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_vector_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_gelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_linear, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_log_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_one_hot, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_scaled_dot_product_attention, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_softplus, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__nn_softshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_airy_ai, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_bessel_j0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_bessel_j1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_bessel_y0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_bessel_y1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_chebyshev_polynomial_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_chebyshev_polynomial_u, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_chebyshev_polynomial_v, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_chebyshev_polynomial_w, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_entr, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erfcx, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_expit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_gammainc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_gammaincc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_gammaln, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_hermite_polynomial_h, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_hermite_polynomial_he, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_i0e, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_i1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_i1e, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_laguerre_polynomial_l, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_legendre_polynomial_p, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_log_ndtr, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_i1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_k0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_k1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_multigammaln, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_ndtr, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_ndtri, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_psi, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_u, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_v, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_w, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_spherical_bessel_j0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlog1py, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_zeta, test/test_overrides.py::TestTorchFunctionOverride::test_torch__assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__conj_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__functional_assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__fw_primal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lobpcg_lobpcg, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_pca_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_svd_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__make_dual_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__native_batch_norm_legit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__neg_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__reshape_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__rowwise_prune, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sparse_broadcast_to_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_linear_prepack, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_quantized_linear_prepacked, test/test_overrides.py::TestTorchFunctionOverride::test_torch_abs, test/test_overrides.py::TestTorchFunctionOverride::test_torch_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inner, test/test_overrides.py::TestTorchFunctionOverride::test_torch_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log10, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mish, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mse_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_head_attention_forward, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_normalize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_pad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu6, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_silu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_smooth_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softsign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_tanhshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_with_distance_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_constant_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_kaiming_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_torch_norm_except_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nuclear_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_numel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ones_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_outer, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pairwise_distance, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_unshuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polar, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_positive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_channel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor_dynamic, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rand_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randint_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randn_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_torch_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_roll, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_torch_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_saddmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_searchsorted, test/test_overrides.py::TestTorchFunctionOverride::test_torch_segment_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_smm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_square, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_float, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_int, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_ite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_topk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trace, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapz, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tril, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unflatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unfold_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_where, test/test_overrides.py::TestTorchFunctionOverride::test_torch_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_zeros_like, test/test_overrides.py::TestTorchFunctionOverride::test_user_implementation_raises, test/test_overrides.py::TestEinsumOverride::test_wrapper, test/test_overrides.py::TestGradCheckOverride::test_gradcheck, test/test_overrides.py::TestNamedTuple::test_max, test/test_overrides.py::TestGradNewOnesOverride::test_newones, test/test_overrides.py::TestPickle::test_pickle, test/test_overrides.py::TestBroadcastAllOverride::test_broadcast_all, test/test_overrides.py::TestWrapTorchFunction::test_wrap_torch_function, test/test_overrides.py::TestIndexing::test_getitem, test/test_overrides.py::TestIndexing::test_getitem_subclass, test/test_overrides.py::TestIndexing::test_setitem, test/test_overrides.py::TestIndexing::test_setitem_subclass, test/test_overrides.py::TestIndexing::test_setitem_val, test/test_overrides.py::TestIterator::test_iterator, test/test_overrides.py::TestRNN::test_rnn, test/test_overrides.py::TestDisabledTorchFunction::test_parameter_does_not_prevent_dispatch, test/test_overrides.py::TestResolveName::test_resolve_name, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_standalone_class, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_tensor_subclass, test/test_overrides.py::TestDisabledUserWarnings::test_no_implicit_user_warning_for_deprecated_functions, test/test_overrides.py::TestTorchFunctionMode::test_all_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_basic, test/test_overrides.py::TestTorchFunctionMode::test_custom_device_type, test/test_overrides.py::TestTorchFunctionMode::test_device_context_semantics, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_subclass, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_torch_function_ctx, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_mode, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_not_mode, test/test_overrides.py::TestTorchFunctionMode::test_distributions_bernoulli, test/test_overrides.py::TestTorchFunctionMode::test_error_using_class_method_on_mode, test/test_overrides.py::TestTorchFunctionMode::test_factory_override, test/test_overrides.py::TestTorchFunctionMode::test_get_cur_mode, test/test_overrides.py::TestTorchFunctionMode::test_get_mode_stack, test/test_overrides.py::TestTorchFunctionMode::test_getitem_call, test/test_overrides.py::TestTorchFunctionMode::test_mode_notimplemented_loop, test/test_overrides.py::TestTorchFunctionMode::test_modes_handle_first, test/test_overrides.py::TestTorchFunctionMode::test_modes_return_notimplemented, test/test_overrides.py::TestTorchFunctionMode::test_nested_modes_with_python_has_torch_function, test/test_overrides.py::TestTorchFunctionMode::test_nested_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_nn_parse_to, test/test_overrides.py::TestTorchFunctionMode::test_reentrant_mode_idiom, test/test_overrides.py::TestTorchFunctionMode::test_restacking_with_ancestor, test/test_overrides.py::TestTorchFunctionMode::test_subclass_hash, test/test_overrides.py::TestTorchFunctionMode::test_torch_function_all_disabled_api, test/test_overrides.py::TestTorchFunctionMode::test_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes 2024-12-18T01:19:03.3156339Z 2024-12-18T01:19:03.3156630Z Running distributions/test_distributions 1/2 ... [2024-12-18 01:19:03.223624] 2024-12-18T01:19:03.3157134Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:19:03.3158239Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:19:03.223933] 2024-12-18T01:27:20.4989580Z 2024-12-18T01:27:20.4991078Z distributions/test_distributions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.2_95697e8b5c2a0833_.log 2024-12-18T01:27:20.5044413Z Running 130 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_distribution_expand, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, test/distributions/test_distributions.py::TestDistributions::test_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, test/distributions/test_distributions.py::TestDistributions::test_wishart_moments, test/distributions/test_distributions.py::TestDistributions::test_wishart_properties, test/distributions/test_distributions.py::TestDistributions::test_wishart_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_zero_excluded_binomial, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_alpha, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_beta, test/distributions/test_distributions.py::TestRsample::test_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape_vectorized_n, test/distributions/test_distributions.py::TestDistributionShapes::test_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_sample 2024-12-18T01:27:20.5093320Z 2024-12-18T01:27:20.5093639Z Running distributions/test_distributions 2/2 ... [2024-12-18 01:27:20.499300] 2024-12-18T01:27:20.5094127Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:27:20.5095230Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:27:20.499657] 2024-12-18T01:36:32.2019525Z 2024-12-18T01:36:32.2020995Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_439243b68ee6db67_.log 2024-12-18T01:36:32.2057656Z Running 95 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_wishart_shape, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_variance 2024-12-18T01:36:32.2093242Z 2024-12-18T01:36:32.2093446Z Running test_autoload_disable 1/1 ... [2024-12-18 01:36:32.202308] 2024-12-18T01:36:34.7176274Z running install 2024-12-18T01:36:34.7177530Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T01:36:34.7178317Z !! 2024-12-18T01:36:34.7178447Z 2024-12-18T01:36:34.7178577Z ******************************************************************************** 2024-12-18T01:36:34.7179009Z Please avoid running ``setup.py`` directly. 2024-12-18T01:36:34.7179435Z Instead, use pypa/build, pypa/installer or other 2024-12-18T01:36:34.7179815Z standards-based tools. 2024-12-18T01:36:34.7180004Z 2024-12-18T01:36:34.7180400Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T01:36:34.7180939Z ******************************************************************************** 2024-12-18T01:36:34.7181196Z 2024-12-18T01:36:34.7181292Z !! 2024-12-18T01:36:34.7181527Z self.initialize_options() 2024-12-18T01:36:34.7315581Z running build 2024-12-18T01:36:34.7315961Z running build_py 2024-12-18T01:36:34.7393839Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T01:36:34.7396558Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T01:36:34.7400580Z running build_ext 2024-12-18T01:36:34.8220099Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T01:36:34.8220982Z creating build/temp.linux-x86_64-cpython-313 2024-12-18T01:36:34.8226922Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T01:36:36.2423573Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T01:36:36.2424595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T01:36:36.2425470Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2024-12-18T01:36:36.2426023Z from extension.cpp:1: 2024-12-18T01:36:36.2427542Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T01:36:36.2428388Z extension.cpp:45:53: required from here 2024-12-18T01:36:36.2429843Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T01:36:36.2433145Z 1539 | class class_ : public detail::generic_type { 2024-12-18T01:36:36.2433665Z | ^~~~~~ 2024-12-18T01:36:36.2436447Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T01:36:36.2438964Z extension.cpp:45:53: required from here 2024-12-18T01:36:36.2442357Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2024-12-18T01:36:36.2446012Z 1599 | with_internals([&](internals &internals) { 2024-12-18T01:36:36.2446415Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:36:36.2446967Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T01:36:36.2447561Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:36:36.2448006Z 1601 | : internals.registered_types_cpp; 2024-12-18T01:36:36.2448432Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:36:36.2448868Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T01:36:36.2449292Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:36:36.2449703Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T01:36:36.2450245Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:36:36.2450573Z 1604 | }); 2024-12-18T01:36:36.2450842Z | ~ 2024-12-18T01:36:36.2453972Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2024-12-18T01:36:36.6807199Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T01:36:36.6812329Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T01:36:37.8043104Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2024-12-18T01:36:38.1757507Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T01:36:38.1761845Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T01:36:39.7073425Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:36:39.7074392Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:36:39.7075192Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:36:39.7076349Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:36:39.7077029Z from rng_extension.cpp:6: 2024-12-18T01:36:39.7077848Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:36:39.7078647Z 1123 | # pragma unroll 2024-12-18T01:36:39.7078895Z | 2024-12-18T01:36:39.7079435Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T01:36:39.7080320Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:36:39.7081281Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:36:39.7082509Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:36:39.7083749Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:36:39.7084493Z from rng_extension.cpp:6: 2024-12-18T01:36:39.7085281Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:36:39.7086048Z 59 | #pragma unroll 2024-12-18T01:36:39.7086301Z | 2024-12-18T01:36:39.7086967Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:36:39.7087729Z 72 | #pragma unroll 2024-12-18T01:36:39.7087965Z | 2024-12-18T01:36:39.7088678Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:36:39.7089435Z 87 | #pragma unroll 2024-12-18T01:36:39.7089684Z | 2024-12-18T01:36:39.7090221Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T01:36:39.7091100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:36:39.7091897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:36:39.7092675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:36:39.7093571Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:36:39.7094242Z from rng_extension.cpp:6: 2024-12-18T01:36:39.7095037Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:36:39.7095819Z 153 | #pragma unroll 2024-12-18T01:36:39.7096061Z | 2024-12-18T01:36:39.7099277Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2024-12-18T01:36:40.1230824Z running install_lib 2024-12-18T01:36:40.1311779Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T01:36:40.1405120Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T01:36:40.1505237Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T01:36:40.1602046Z running install_egg_info 2024-12-18T01:36:40.1775544Z running egg_info 2024-12-18T01:36:40.1844414Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T01:36:40.1847770Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T01:36:40.1849762Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T01:36:40.1851691Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T01:36:40.1924508Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:36:40.1938648Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:36:40.1940508Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2024-12-18T01:36:40.1942167Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2024-12-18T01:36:40.1947943Z running install_scripts 2024-12-18T01:36:43.3274916Z 2024-12-18T01:36:43.3275581Z Running tests... 2024-12-18T01:36:43.3276040Z ---------------------------------------------------------------------- 2024-12-18T01:36:43.4541889Z . 2024-12-18T01:36:43.4542327Z ---------------------------------------------------------------------- 2024-12-18T01:36:43.4542709Z Ran 1 test in 0.127s 2024-12-18T01:36:43.4542886Z 2024-12-18T01:36:43.4542981Z OK 2024-12-18T01:36:43.4543107Z 2024-12-18T01:36:43.4543217Z Generating XML reports... 2024-12-18T01:36:44.1203307Z Running doctests 1/1 ... [2024-12-18 01:36:44.119963] 2024-12-18T01:36:44.2311656Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2024-12-18T01:36:44.2312189Z Listing tests 2024-12-18T01:36:44.5206657Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1496. 2024-12-18T01:36:44.5207553Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.5207927Z 2024-12-18T01:36:44.5208102Z dim_order(ambiguity_check=False) -> tuple 2024-12-18T01:36:44.5208332Z 2024-12-18T01:36:44.5208574Z Returns the uniquely determined tuple of int describing the dim order or 2024-12-18T01:36:44.5209055Z physical layout of :attr:`self`. 2024-12-18T01:36:44.5209265Z 2024-12-18T01:36:44.5209459Z The dim order represents how dimensions are laid out in memory, 2024-12-18T01:36:44.5209939Z starting from the outermost to the innermost dimension. 2024-12-18T01:36:44.5210213Z 2024-12-18T01:36:44.5210421Z Note that the dim order may not always be uniquely determined. 2024-12-18T01:36:44.5211103Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2024-12-18T01:36:44.5211982Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2024-12-18T01:36:44.5212727Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2024-12-18T01:36:44.5213410Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2024-12-18T01:36:44.5213958Z Otherwise, it will raise TypeError. 2024-12-18T01:36:44.5214393Z 2024-12-18T01:36:44.5214495Z Args: 2024-12-18T01:36:44.5214934Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2024-12-18T01:36:44.5215361Z 2024-12-18T01:36:44.5215493Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2024-12-18T01:36:44.5215795Z (0, 1, 2, 3) 2024-12-18T01:36:44.5216100Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2024-12-18T01:36:44.5216465Z (0, 2, 1, 3) 2024-12-18T01:36:44.5216808Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2024-12-18T01:36:44.5217218Z (0, 2, 3, 1) 2024-12-18T01:36:44.5217466Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2024-12-18T01:36:44.5217775Z (0, 1, 2, 3) 2024-12-18T01:36:44.5218008Z >>> try: 2024-12-18T01:36:44.5218312Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2024-12-18T01:36:44.5218768Z ... except RuntimeError as e: 2024-12-18T01:36:44.5219051Z ... print(e) 2024-12-18T01:36:44.5219517Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2024-12-18T01:36:44.5220058Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2024-12-18T01:36:44.5220491Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2024-12-18T01:36:44.5221014Z ... ) # It can be mapped to contiguous format 2024-12-18T01:36:44.5221333Z (0, 1, 2, 3) 2024-12-18T01:36:44.5221566Z >>> try: 2024-12-18T01:36:44.5221881Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2024-12-18T01:36:44.5222289Z ... except TypeError as e: 2024-12-18T01:36:44.5222576Z ... print(e) 2024-12-18T01:36:44.5222949Z The ambiguity_check argument must be a bool or a list of memory formats. 2024-12-18T01:36:44.5223415Z .. warning:: 2024-12-18T01:36:44.5223749Z The dim_order tensor API is experimental and subject to change. 2024-12-18T01:36:44.5224050Z 2024-12-18T01:36:44.5224360Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.5224721Z 2024-12-18T01:36:44.5750041Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=431. 2024-12-18T01:36:44.5751110Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.5752074Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-12-18T01:36:44.5752423Z 2024-12-18T01:36:44.5752600Z This is helpful when you want to visualize data over some 2024-12-18T01:36:44.5753047Z range of inputs. See below for a plotting example. 2024-12-18T01:36:44.5753321Z 2024-12-18T01:36:44.5753493Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-12-18T01:36:44.5753967Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-12-18T01:36:44.5754467Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-12-18T01:36:44.5754939Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-12-18T01:36:44.5755394Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-12-18T01:36:44.5755893Z to the result shape. 2024-12-18T01:36:44.5756090Z 2024-12-18T01:36:44.5756208Z .. note:: 2024-12-18T01:36:44.5756523Z 0D inputs are treated equivalently to 1D inputs of a 2024-12-18T01:36:44.5756904Z single element. 2024-12-18T01:36:44.5757075Z 2024-12-18T01:36:44.5757169Z .. warning:: 2024-12-18T01:36:44.5757507Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-12-18T01:36:44.5757971Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-12-18T01:36:44.5758255Z 2024-12-18T01:36:44.5758407Z In the future `torch.meshgrid` will transition to 2024-12-18T01:36:44.5758788Z `indexing='xy'` as the default. 2024-12-18T01:36:44.5759003Z 2024-12-18T01:36:44.5759210Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-12-18T01:36:44.5759839Z this issue with the goal of migrating to NumPy's behavior. 2024-12-18T01:36:44.5760162Z 2024-12-18T01:36:44.5760269Z .. seealso:: 2024-12-18T01:36:44.5760417Z 2024-12-18T01:36:44.5760589Z :func:`torch.cartesian_prod` has the same effect but it 2024-12-18T01:36:44.5761014Z collects the data in a tensor of vectors. 2024-12-18T01:36:44.5761264Z 2024-12-18T01:36:44.5761353Z Args: 2024-12-18T01:36:44.5761751Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-12-18T01:36:44.5762298Z treated as tensors of size :math:`(1,)` automatically 2024-12-18T01:36:44.5762571Z 2024-12-18T01:36:44.5762760Z indexing: (str, optional): the indexing mode, either "xy" 2024-12-18T01:36:44.5763209Z or "ij", defaults to "ij". See warning for future changes. 2024-12-18T01:36:44.5763553Z 2024-12-18T01:36:44.5763711Z If "xy" is selected, the first dimension corresponds 2024-12-18T01:36:44.5764147Z to the cardinality of the second input and the second 2024-12-18T01:36:44.5764598Z dimension corresponds to the cardinality of the first 2024-12-18T01:36:44.5764975Z input. 2024-12-18T01:36:44.5765130Z 2024-12-18T01:36:44.5765353Z If "ij" is selected, the dimensions are in the same 2024-12-18T01:36:44.5765741Z order as the cardinality of the inputs. 2024-12-18T01:36:44.5765991Z 2024-12-18T01:36:44.5766080Z Returns: 2024-12-18T01:36:44.5766482Z seq (sequence of Tensors): If the input has :math:`N` 2024-12-18T01:36:44.5767196Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-12-18T01:36:44.5767705Z output will also have :math:`N` tensors, where each tensor 2024-12-18T01:36:44.5768367Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-12-18T01:36:44.5768824Z 2024-12-18T01:36:44.5768995Z Example:: 2024-12-18T01:36:44.5769224Z 2024-12-18T01:36:44.5769343Z >>> x = torch.tensor([1, 2, 3]) 2024-12-18T01:36:44.5769675Z >>> y = torch.tensor([4, 5, 6]) 2024-12-18T01:36:44.5769888Z 2024-12-18T01:36:44.5770177Z Observe the element-wise pairings across the grid, (1, 4), 2024-12-18T01:36:44.5770979Z (1, 5), ..., (3, 6). This is the same thing as the 2024-12-18T01:36:44.5771334Z cartesian product. 2024-12-18T01:36:44.5771676Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-12-18T01:36:44.5772042Z >>> grid_x 2024-12-18T01:36:44.5772298Z tensor([[1, 1, 1], 2024-12-18T01:36:44.5772578Z [2, 2, 2], 2024-12-18T01:36:44.5772850Z [3, 3, 3]]) 2024-12-18T01:36:44.5773105Z >>> grid_y 2024-12-18T01:36:44.5773363Z tensor([[4, 5, 6], 2024-12-18T01:36:44.5773637Z [4, 5, 6], 2024-12-18T01:36:44.5773906Z [4, 5, 6]]) 2024-12-18T01:36:44.5774082Z 2024-12-18T01:36:44.5774264Z This correspondence can be seen when these grids are 2024-12-18T01:36:44.5774638Z stacked properly. 2024-12-18T01:36:44.5775024Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-12-18T01:36:44.5775472Z ... torch.cartesian_prod(x, y)) 2024-12-18T01:36:44.5775807Z True 2024-12-18T01:36:44.5775943Z 2024-12-18T01:36:44.5776132Z `torch.meshgrid` is commonly used to produce a grid for 2024-12-18T01:36:44.5776522Z plotting. 2024-12-18T01:36:44.5776815Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-12-18T01:36:44.5777193Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-12-18T01:36:44.5777554Z >>> import matplotlib.pyplot as plt 2024-12-18T01:36:44.5777933Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-12-18T01:36:44.5778301Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-12-18T01:36:44.5778681Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-12-18T01:36:44.5779121Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-12-18T01:36:44.5779491Z >>> ax = plt.axes(projection='3d') 2024-12-18T01:36:44.5779865Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-12-18T01:36:44.5780239Z >>> plt.show() 2024-12-18T01:36:44.5780423Z 2024-12-18T01:36:44.5780561Z .. image:: ../_static/img/meshgrid.png 2024-12-18T01:36:44.5780893Z :width: 512 2024-12-18T01:36:44.5781050Z 2024-12-18T01:36:44.5781152Z 2024-12-18T01:36:44.5781516Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.5781890Z 2024-12-18T01:36:44.5782408Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=820. 2024-12-18T01:36:44.5783320Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.5784069Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-12-18T01:36:44.5784546Z 2024-12-18T01:36:44.5784698Z Returns the unique elements of the input tensor. 2024-12-18T01:36:44.5784970Z 2024-12-18T01:36:44.5785295Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-12-18T01:36:44.5785906Z this function also eliminates non-consecutive duplicate values. 2024-12-18T01:36:44.5786229Z 2024-12-18T01:36:44.5786455Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-12-18T01:36:44.5787079Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-12-18T01:36:44.5787755Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-12-18T01:36:44.5788334Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-12-18T01:36:44.5788622Z 2024-12-18T01:36:44.5788749Z Args: 2024-12-18T01:36:44.5788993Z input (Tensor): the input tensor 2024-12-18T01:36:44.5789427Z sorted (bool): Whether to sort the unique elements in ascending order 2024-12-18T01:36:44.5789866Z before returning as output. 2024-12-18T01:36:44.5790292Z return_inverse (bool): Whether to also return the indices for where 2024-12-18T01:36:44.5790822Z elements in the original input ended up in the returned unique list. 2024-12-18T01:36:44.5791380Z return_counts (bool): Whether to also return the counts for each unique 2024-12-18T01:36:44.5791812Z element. 2024-12-18T01:36:44.5792182Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-12-18T01:36:44.5792713Z unique of the flattened input is returned. Otherwise, each of the 2024-12-18T01:36:44.5793242Z tensors indexed by the given dimension is treated as one of the 2024-12-18T01:36:44.5793765Z elements to apply the unique operation upon. See examples for more 2024-12-18T01:36:44.5794204Z details. Default: ``None`` 2024-12-18T01:36:44.5794423Z 2024-12-18T01:36:44.5794515Z Returns: 2024-12-18T01:36:44.5794933Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-12-18T01:36:44.5795324Z 2024-12-18T01:36:44.5795533Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-12-18T01:36:44.5796077Z - **inverse_indices** (*Tensor*): (optional) if 2024-12-18T01:36:44.5796507Z :attr:`return_inverse` is True, there will be an additional 2024-12-18T01:36:44.5797012Z returned tensor (same shape as input) representing the indices 2024-12-18T01:36:44.5797538Z for where elements in the original input map to in the output; 2024-12-18T01:36:44.5798536Z otherwise, this function will only return a single tensor. 2024-12-18T01:36:44.5799127Z - **counts** (*Tensor*): (optional) if 2024-12-18T01:36:44.5799618Z :attr:`return_counts` is True, there will be an additional 2024-12-18T01:36:44.5800103Z returned tensor (same shape as output or output.size(dim), 2024-12-18T01:36:44.5800604Z if dim was specified) representing the number of occurrences 2024-12-18T01:36:44.5801034Z for each unique value or tensor. 2024-12-18T01:36:44.5801258Z 2024-12-18T01:36:44.5801372Z Example:: 2024-12-18T01:36:44.5801501Z 2024-12-18T01:36:44.5801724Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-12-18T01:36:44.5802130Z >>> output 2024-12-18T01:36:44.5802374Z tensor([1, 2, 3]) 2024-12-18T01:36:44.5802551Z 2024-12-18T01:36:44.5802680Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:36:44.5803202Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:36:44.5803644Z >>> output 2024-12-18T01:36:44.5803890Z tensor([1, 2, 3]) 2024-12-18T01:36:44.5804148Z >>> inverse_indices 2024-12-18T01:36:44.5804427Z tensor([0, 2, 1, 2]) 2024-12-18T01:36:44.5804612Z 2024-12-18T01:36:44.5804740Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:36:44.5805259Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:36:44.5805703Z >>> output 2024-12-18T01:36:44.5805933Z tensor([1, 2, 3]) 2024-12-18T01:36:44.5806198Z >>> inverse_indices 2024-12-18T01:36:44.5806466Z tensor([[0, 2], 2024-12-18T01:36:44.5806717Z [1, 2]]) 2024-12-18T01:36:44.5806871Z 2024-12-18T01:36:44.5806983Z >>> a = torch.tensor([ 2024-12-18T01:36:44.5807243Z ... [ 2024-12-18T01:36:44.5807478Z ... [1, 1, 0, 0], 2024-12-18T01:36:44.5807761Z ... [1, 1, 0, 0], 2024-12-18T01:36:44.5808038Z ... [0, 0, 1, 1], 2024-12-18T01:36:44.5808349Z ... ], 2024-12-18T01:36:44.5808572Z ... [ 2024-12-18T01:36:44.5808809Z ... [0, 0, 1, 1], 2024-12-18T01:36:44.5809086Z ... [0, 0, 1, 1], 2024-12-18T01:36:44.5809362Z ... [1, 1, 1, 1], 2024-12-18T01:36:44.5809639Z ... ], 2024-12-18T01:36:44.5809862Z ... [ 2024-12-18T01:36:44.5810105Z ... [1, 1, 0, 0], 2024-12-18T01:36:44.5810383Z ... [1, 1, 0, 0], 2024-12-18T01:36:44.5810664Z ... [0, 0, 1, 1], 2024-12-18T01:36:44.5810923Z ... ], 2024-12-18T01:36:44.5811157Z ... ]) 2024-12-18T01:36:44.5811297Z 2024-12-18T01:36:44.5811508Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-12-18T01:36:44.5812047Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-12-18T01:36:44.5812508Z >>> # each other, so one of them will be removed. 2024-12-18T01:36:44.5812860Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-12-18T01:36:44.5813157Z tensor(True) 2024-12-18T01:36:44.5813440Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-12-18T01:36:44.5813777Z >>> a_unique_dim0 2024-12-18T01:36:44.5814049Z tensor([[[0, 0, 1, 1], 2024-12-18T01:36:44.5814331Z [0, 0, 1, 1], 2024-12-18T01:36:44.5814591Z [1, 1, 1, 1]], 2024-12-18T01:36:44.5814873Z [[1, 1, 0, 0], 2024-12-18T01:36:44.5815143Z [1, 1, 0, 0], 2024-12-18T01:36:44.5815416Z [0, 0, 1, 1]]]) 2024-12-18T01:36:44.5815594Z 2024-12-18T01:36:44.5815822Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-12-18T01:36:44.5816241Z >>> # `a_unique_dim0`: 2024-12-18T01:36:44.5816555Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-12-18T01:36:44.5816899Z tensor(True) 2024-12-18T01:36:44.5817181Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-12-18T01:36:44.5817515Z tensor(True) 2024-12-18T01:36:44.5817700Z 2024-12-18T01:36:44.5817902Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-12-18T01:36:44.5818408Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-12-18T01:36:44.5818815Z >>> # them will be removed. 2024-12-18T01:36:44.5819129Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-12-18T01:36:44.5819436Z tensor(True) 2024-12-18T01:36:44.5819700Z >>> torch.unique(a, dim=1) 2024-12-18T01:36:44.5819987Z tensor([[[0, 0, 1, 1], 2024-12-18T01:36:44.5820268Z [1, 1, 0, 0]], 2024-12-18T01:36:44.5820536Z [[1, 1, 1, 1], 2024-12-18T01:36:44.5820800Z [0, 0, 1, 1]], 2024-12-18T01:36:44.5821054Z [[0, 0, 1, 1], 2024-12-18T01:36:44.5821322Z [1, 1, 0, 0]]]) 2024-12-18T01:36:44.5821540Z 2024-12-18T01:36:44.5821749Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-12-18T01:36:44.5822360Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-12-18T01:36:44.5822814Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-12-18T01:36:44.5823223Z >>> # sub-tensors will be removed. 2024-12-18T01:36:44.5823544Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-12-18T01:36:44.5823896Z tensor(True) 2024-12-18T01:36:44.5824159Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-12-18T01:36:44.5824466Z tensor(True) 2024-12-18T01:36:44.5824733Z >>> torch.unique(a, dim=2) 2024-12-18T01:36:44.5825021Z tensor([[[0, 1], 2024-12-18T01:36:44.5825285Z [0, 1], 2024-12-18T01:36:44.5825544Z [1, 0]], 2024-12-18T01:36:44.5825804Z [[1, 0], 2024-12-18T01:36:44.5826058Z [1, 0], 2024-12-18T01:36:44.5826302Z [1, 1]], 2024-12-18T01:36:44.5826557Z [[0, 1], 2024-12-18T01:36:44.5826841Z [0, 1], 2024-12-18T01:36:44.5827101Z [1, 0]]]) 2024-12-18T01:36:44.5827348Z 2024-12-18T01:36:44.5827719Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.5828092Z 2024-12-18T01:36:44.5932754Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560. 2024-12-18T01:36:44.5933647Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.5934037Z 2024-12-18T01:36:44.5934200Z Load a model from a github repo or a local directory. 2024-12-18T01:36:44.5934462Z 2024-12-18T01:36:44.5934700Z Note: Loading a model is the typical use case, but this can also be used to 2024-12-18T01:36:44.5935254Z for loading other objects such as tokenizers, loss functions, etc. 2024-12-18T01:36:44.5935573Z 2024-12-18T01:36:44.5935750Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-12-18T01:36:44.5936191Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-12-18T01:36:44.5936577Z ref (a tag or a branch). 2024-12-18T01:36:44.5936755Z 2024-12-18T01:36:44.5936920Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-12-18T01:36:44.5937305Z path to a local directory. 2024-12-18T01:36:44.5937480Z 2024-12-18T01:36:44.5937578Z Args: 2024-12-18T01:36:44.5937819Z repo_or_dir (str): If ``source`` is 'github', 2024-12-18T01:36:44.5938343Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-12-18T01:36:44.5939179Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-12-18T01:36:44.5939851Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-12-18T01:36:44.5940808Z If ``source`` is 'local' then it should be a path to a local directory. 2024-12-18T01:36:44.5941343Z model (str): the name of a callable (entrypoint) defined in the 2024-12-18T01:36:44.5941762Z repo/dir's ``hubconf.py``. 2024-12-18T01:36:44.5942265Z *args (optional): the corresponding args for callable ``model``. 2024-12-18T01:36:44.5942745Z source (str, optional): 'github' or 'local'. Specifies how 2024-12-18T01:36:44.5943208Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-12-18T01:36:44.5943710Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-12-18T01:36:44.5944421Z This parameter was introduced in v1.12 and helps ensuring that users 2024-12-18T01:36:44.5944900Z only run code from repos that they trust. 2024-12-18T01:36:44.5945289Z 2024-12-18T01:36:44.5945643Z - If ``False``, a prompt will ask the user whether the repo should 2024-12-18T01:36:44.5946256Z be trusted. 2024-12-18T01:36:44.5946605Z - If ``True``, the repo will be added to the trusted list and loaded 2024-12-18T01:36:44.5947115Z without requiring explicit confirmation. 2024-12-18T01:36:44.5947535Z - If ``"check"``, the repo will be checked against the list of 2024-12-18T01:36:44.5948037Z trusted repos in the cache. If it is not present in that list, the 2024-12-18T01:36:44.5948544Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-12-18T01:36:44.5949091Z - If ``None``: this will raise a warning, inviting the user to set 2024-12-18T01:36:44.5949586Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-12-18T01:36:44.5950102Z is only present for backward compatibility and will be removed in 2024-12-18T01:36:44.5950516Z v2.0. 2024-12-18T01:36:44.5950652Z 2024-12-18T01:36:44.5950871Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-12-18T01:36:44.5951390Z force_reload (bool, optional): whether to force a fresh download of 2024-12-18T01:36:44.5951911Z the github repo unconditionally. Does not have any effect if 2024-12-18T01:36:44.5952381Z ``source = 'local'``. Default is ``False``. 2024-12-18T01:36:44.5952825Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-12-18T01:36:44.5953351Z local caches. Note that the message about first download cannot be 2024-12-18T01:36:44.5953831Z muted. Does not have any effect if ``source = 'local'``. 2024-12-18T01:36:44.5954219Z Default is ``True``. 2024-12-18T01:36:44.5954707Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-12-18T01:36:44.5955392Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-12-18T01:36:44.5956156Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-12-18T01:36:44.5956728Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-12-18T01:36:44.5957231Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-12-18T01:36:44.5957569Z 2024-12-18T01:36:44.5957663Z Returns: 2024-12-18T01:36:44.5957982Z The output of the ``model`` callable when called with the given 2024-12-18T01:36:44.5958390Z ``*args`` and ``**kwargs``. 2024-12-18T01:36:44.5958574Z 2024-12-18T01:36:44.5958680Z Example: 2024-12-18T01:36:44.5958948Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:36:44.5959287Z >>> # from a github repo 2024-12-18T01:36:44.5959589Z >>> repo = "pytorch/vision" 2024-12-18T01:36:44.5959888Z >>> model = torch.hub.load( 2024-12-18T01:36:44.5960270Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-12-18T01:36:44.5960661Z ... ) 2024-12-18T01:36:44.5960879Z >>> # from a local directory 2024-12-18T01:36:44.5961204Z >>> path = "/some/local/path/pytorch/vision" 2024-12-18T01:36:44.5961547Z >>> # xdoctest: +SKIP 2024-12-18T01:36:44.5961964Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-12-18T01:36:44.5962324Z 2024-12-18T01:36:44.5962586Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.5963620Z 2024-12-18T01:36:44.5964086Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=687. 2024-12-18T01:36:44.5964922Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.5965449Z Download object at the given URL to a local path. 2024-12-18T01:36:44.5965712Z 2024-12-18T01:36:44.5965799Z Args: 2024-12-18T01:36:44.5966050Z url (str): URL of the object to download 2024-12-18T01:36:44.5966515Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-12-18T01:36:44.5967175Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-12-18T01:36:44.5967747Z Default: None 2024-12-18T01:36:44.5968166Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-12-18T01:36:44.5968629Z Default: True 2024-12-18T01:36:44.5968794Z 2024-12-18T01:36:44.5968899Z Example: 2024-12-18T01:36:44.5969172Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:36:44.5969524Z >>> # xdoctest: +REQUIRES(POSIX) 2024-12-18T01:36:44.5969867Z >>> torch.hub.download_url_to_file( 2024-12-18T01:36:44.5970368Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-12-18T01:36:44.5970818Z ... "/tmp/temporary_file", 2024-12-18T01:36:44.5971122Z ... ) 2024-12-18T01:36:44.5971254Z 2024-12-18T01:36:44.5971340Z 2024-12-18T01:36:44.5971704Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.5972073Z 2024-12-18T01:36:44.5972572Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=812. 2024-12-18T01:36:44.5973451Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.5973979Z Loads the Torch serialized object at the given URL. 2024-12-18T01:36:44.5974234Z 2024-12-18T01:36:44.5974432Z If downloaded file is a zip file, it will be automatically 2024-12-18T01:36:44.5974823Z decompressed. 2024-12-18T01:36:44.5974967Z 2024-12-18T01:36:44.5975187Z If the object is already present in `model_dir`, it's deserialized and 2024-12-18T01:36:44.5975611Z returned. 2024-12-18T01:36:44.5975963Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-12-18T01:36:44.5976487Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-12-18T01:36:44.5976790Z 2024-12-18T01:36:44.5976890Z Args: 2024-12-18T01:36:44.5977129Z url (str): URL of the object to download 2024-12-18T01:36:44.5977560Z model_dir (str, optional): directory in which to save the object 2024-12-18T01:36:44.5978218Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-12-18T01:36:44.5978923Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-12-18T01:36:44.5979389Z Default: True 2024-12-18T01:36:44.5979878Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-12-18T01:36:44.5980521Z ``filename-.ext`` where ```` is the first eight or more 2024-12-18T01:36:44.5981081Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-12-18T01:36:44.5981611Z ensure unique names and to verify the contents of the file. 2024-12-18T01:36:44.5982014Z Default: False 2024-12-18T01:36:44.5982514Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-12-18T01:36:44.5983273Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-12-18T01:36:44.5983949Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-12-18T01:36:44.5984360Z 2024-12-18T01:36:44.5984453Z Example: 2024-12-18T01:36:44.5984723Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:36:44.5985120Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-12-18T01:36:44.5985606Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-12-18T01:36:44.5986025Z ... ) 2024-12-18T01:36:44.5986151Z 2024-12-18T01:36:44.5986237Z 2024-12-18T01:36:44.5986602Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.5986969Z 2024-12-18T01:36:44.6014805Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=360. 2024-12-18T01:36:44.6015737Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:44.6016334Z Registers the function implementation as the fallback for the given key. 2024-12-18T01:36:44.6016682Z 2024-12-18T01:36:44.6016909Z This function only works for a library with global namespace ("_"). 2024-12-18T01:36:44.6017234Z 2024-12-18T01:36:44.6017337Z Args: 2024-12-18T01:36:44.6017736Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-12-18T01:36:44.6018297Z to register a fallthrough. 2024-12-18T01:36:44.6018842Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-12-18T01:36:44.6019442Z the dispatch key that the library was created with. 2024-12-18T01:36:44.6020087Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-12-18T01:36:44.6020886Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-12-18T01:36:44.6021314Z 2024-12-18T01:36:44.6021471Z Example:: 2024-12-18T01:36:44.6021739Z >>> my_lib = Library("_", "IMPL") 2024-12-18T01:36:44.6022102Z >>> def fallback_kernel(op, *args, **kwargs): 2024-12-18T01:36:44.6022485Z >>> # Handle all autocast ops generically 2024-12-18T01:36:44.6022820Z >>> # ... 2024-12-18T01:36:44.6023122Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:36:44.6023469Z 2024-12-18T01:36:44.6024205Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-12-18T01:36:44.6024926Z 2024-12-18T01:36:44.6025060Z my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:36:44.6025387Z ^ 2024-12-18T01:36:44.6078308Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=725. 2024-12-18T01:36:44.6079153Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:44.6079737Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-12-18T01:36:44.6080087Z 2024-12-18T01:36:44.6080268Z Also sometimes known as a "meta kernel", "abstract impl". 2024-12-18T01:36:44.6080566Z 2024-12-18T01:36:44.6080810Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-12-18T01:36:44.6081394Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-12-18T01:36:44.6081969Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-12-18T01:36:44.6082470Z what the properties of the output Tensors are. 2024-12-18T01:36:44.6082723Z 2024-12-18T01:36:44.6082953Z The FakeTensor implementation has the same signature as the operator. 2024-12-18T01:36:44.6083513Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-12-18T01:36:44.6095259Z implementation, assume that all Tensor inputs to the operator are 2024-12-18T01:36:44.6096087Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-12-18T01:36:44.6096628Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-12-18T01:36:44.6097191Z The FakeTensor implementation must consist of only PyTorch operations 2024-12-18T01:36:44.6097734Z (and may not directly access the storage or data of any input or 2024-12-18T01:36:44.6098341Z intermediate Tensors). 2024-12-18T01:36:44.6098537Z 2024-12-18T01:36:44.6098693Z This API may be used as a decorator (see examples). 2024-12-18T01:36:44.6098966Z 2024-12-18T01:36:44.6099109Z For a detailed guide on custom ops, please see 2024-12-18T01:36:44.6099611Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-12-18T01:36:44.6099951Z 2024-12-18T01:36:44.6100133Z Examples: 2024-12-18T01:36:44.6100373Z >>> import torch 2024-12-18T01:36:44.6100642Z >>> import numpy as np 2024-12-18T01:36:44.6100947Z >>> from torch import Tensor 2024-12-18T01:36:44.6101251Z >>> 2024-12-18T01:36:44.6101579Z >>> # Example 1: an operator without data-dependent output shape 2024-12-18T01:36:44.6102114Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-12-18T01:36:44.6102700Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-12-18T01:36:44.6103226Z >>> raise NotImplementedError("Implementation goes here") 2024-12-18T01:36:44.6103613Z >>> 2024-12-18T01:36:44.6103922Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-12-18T01:36:44.6104314Z >>> def _(x, weight, bias): 2024-12-18T01:36:44.6104611Z >>> assert x.dim() == 2 2024-12-18T01:36:44.6104928Z >>> assert weight.dim() == 2 2024-12-18T01:36:44.6105261Z >>> assert bias.dim() == 1 2024-12-18T01:36:44.6105603Z >>> assert x.shape[1] == weight.shape[1] 2024-12-18T01:36:44.6106020Z >>> assert weight.shape[0] == bias.shape[0] 2024-12-18T01:36:44.6106394Z >>> assert x.device == weight.device 2024-12-18T01:36:44.6106706Z >>> 2024-12-18T01:36:44.6106960Z >>> return (x @ weight.t()) + bias 2024-12-18T01:36:44.6107281Z >>> 2024-12-18T01:36:44.6107586Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-12-18T01:36:44.6107981Z >>> x = torch.randn(2, 3) 2024-12-18T01:36:44.6108280Z >>> w = torch.randn(3, 3) 2024-12-18T01:36:44.6108588Z >>> b = torch.randn(3) 2024-12-18T01:36:44.6108928Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-12-18T01:36:44.6109275Z >>> 2024-12-18T01:36:44.6109495Z >>> assert y.shape == (2, 3) 2024-12-18T01:36:44.6109794Z >>> 2024-12-18T01:36:44.6110099Z >>> # Example 2: an operator with data-dependent output shape 2024-12-18T01:36:44.6110615Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-12-18T01:36:44.6111082Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-12-18T01:36:44.6111445Z >>> x_np = x.numpy(force=True) 2024-12-18T01:36:44.6111787Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-12-18T01:36:44.6112170Z >>> return torch.tensor(res, device=x.device) 2024-12-18T01:36:44.6112513Z >>> 2024-12-18T01:36:44.6112825Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-12-18T01:36:44.6113206Z >>> def _(x): 2024-12-18T01:36:44.6113510Z >>> # Number of nonzero-elements is data-dependent. 2024-12-18T01:36:44.6113933Z >>> # Since we cannot peek at the data in an fake impl, 2024-12-18T01:36:44.6114361Z >>> # we use the ctx object to construct a new symint that 2024-12-18T01:36:44.6114766Z >>> # represents the data-dependent size. 2024-12-18T01:36:44.6115134Z >>> ctx = torch.library.get_ctx() 2024-12-18T01:36:44.6115467Z >>> nnz = ctx.new_dynamic_size() 2024-12-18T01:36:44.6115927Z >>> shape = [nnz, x.dim()] 2024-12-18T01:36:44.6116355Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-12-18T01:36:44.6116726Z >>> return result 2024-12-18T01:36:44.6117008Z >>> 2024-12-18T01:36:44.6117312Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:36:44.6117702Z >>> 2024-12-18T01:36:44.6117948Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-12-18T01:36:44.6118419Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-12-18T01:36:44.6118897Z >>> trace.print_readable() 2024-12-18T01:36:44.6119180Z >>> 2024-12-18T01:36:44.6119538Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-12-18T01:36:44.6119886Z 2024-12-18T01:36:44.6119977Z 2024-12-18T01:36:44.6120642Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-12-18T01:36:44.6121259Z 2024-12-18T01:36:44.6121361Z _._ = None 2024-12-18T01:36:44.6121583Z ^ 2024-12-18T01:36:44.6122176Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=846. 2024-12-18T01:36:44.6123081Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.6123600Z Register a backward formula for this custom op. 2024-12-18T01:36:44.6123866Z 2024-12-18T01:36:44.6124069Z In order for an operator to work with autograd, you need to register 2024-12-18T01:36:44.6124490Z a backward formula: 2024-12-18T01:36:44.6124870Z 1. You must tell us how to compute gradients during the backward pass 2024-12-18T01:36:44.6125302Z by providing us a "backward" function. 2024-12-18T01:36:44.6125751Z 2. If you need any values from the forward to compute gradients, you can 2024-12-18T01:36:44.6126229Z use `setup_context` to save values for backward. 2024-12-18T01:36:44.6126506Z 2024-12-18T01:36:44.6126748Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-12-18T01:36:44.6127282Z - ``grads`` is one or more gradients. The number of gradients matches 2024-12-18T01:36:44.6127724Z the number of outputs of the operator. 2024-12-18T01:36:44.6128156Z The ``ctx`` object is `the same ctx object `_ used by 2024-12-18T01:36:44.6128728Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-12-18T01:36:44.6129249Z same as :meth:`torch.autograd.Function.backward`. 2024-12-18T01:36:44.6129515Z 2024-12-18T01:36:44.6129734Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-12-18T01:36:44.6130285Z Please save quantities needed for backward onto the ``ctx`` object via 2024-12-18T01:36:44.6130869Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-12-18T01:36:44.6131412Z or assigning them as attributes of ``ctx``. If your custom op has 2024-12-18T01:36:44.6131945Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-12-18T01:36:44.6132466Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-12-18T01:36:44.6132767Z 2024-12-18T01:36:44.6132999Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-12-18T01:36:44.6133566Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-12-18T01:36:44.6134172Z not depend on or mutate global state. If you need a non-traceable backward, 2024-12-18T01:36:44.6135097Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-12-18T01:36:44.6135652Z 2024-12-18T01:36:44.6135993Z If you need different autograd behavior on different devices, then we 2024-12-18T01:36:44.6136558Z recommend creating two different custom operators, one for each device 2024-12-18T01:36:44.6137135Z that needs different behavior, and switching between them at runtime. 2024-12-18T01:36:44.6137468Z 2024-12-18T01:36:44.6137633Z Examples: 2024-12-18T01:36:44.6137875Z >>> import torch 2024-12-18T01:36:44.6138145Z >>> import numpy as np 2024-12-18T01:36:44.6138447Z >>> from torch import Tensor 2024-12-18T01:36:44.6138750Z >>> 2024-12-18T01:36:44.6139102Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-12-18T01:36:44.6139548Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-12-18T01:36:44.6139880Z >>> x_np = x.cpu().numpy() 2024-12-18T01:36:44.6140200Z >>> y_np = np.sin(x_np) 2024-12-18T01:36:44.6140566Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:36:44.6140934Z >>> 2024-12-18T01:36:44.6141220Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-12-18T01:36:44.6141575Z >>> x, = inputs 2024-12-18T01:36:44.6141896Z >>> ctx.save_for_backward(x) 2024-12-18T01:36:44.6142209Z >>> 2024-12-18T01:36:44.6142452Z >>> def backward(ctx, grad): 2024-12-18T01:36:44.6142769Z >>> x, = ctx.saved_tensors 2024-12-18T01:36:44.6143072Z >>> return grad * x.cos() 2024-12-18T01:36:44.6143367Z >>> 2024-12-18T01:36:44.6143628Z >>> torch.library.register_autograd( 2024-12-18T01:36:44.6144059Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-12-18T01:36:44.6144449Z ... ) 2024-12-18T01:36:44.6144669Z >>> 2024-12-18T01:36:44.6144920Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:36:44.6145262Z >>> y = numpy_sin(x) 2024-12-18T01:36:44.6145604Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:36:44.6146016Z >>> assert torch.allclose(grad_x, x.cos()) 2024-12-18T01:36:44.6146343Z >>> 2024-12-18T01:36:44.6146590Z >>> # Example with a keyword-only arg 2024-12-18T01:36:44.6147020Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:36:44.6147515Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-12-18T01:36:44.6147878Z >>> x_np = x.cpu().numpy() 2024-12-18T01:36:44.6148187Z >>> y_np = x_np * val 2024-12-18T01:36:44.6148542Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:36:44.6148897Z >>> 2024-12-18T01:36:44.6149249Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-12-18T01:36:44.6149705Z >>> ctx.val = keyword_only_inputs["val"] 2024-12-18T01:36:44.6150029Z >>> 2024-12-18T01:36:44.6150258Z >>> def backward(ctx, grad): 2024-12-18T01:36:44.6150569Z >>> return grad * ctx.val 2024-12-18T01:36:44.6150861Z >>> 2024-12-18T01:36:44.6151103Z >>> torch.library.register_autograd( 2024-12-18T01:36:44.6151512Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-12-18T01:36:44.6151896Z ... ) 2024-12-18T01:36:44.6152123Z >>> 2024-12-18T01:36:44.6152379Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:36:44.6152713Z >>> y = numpy_mul(x, val=3.14) 2024-12-18T01:36:44.6153093Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:36:44.6153556Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-12-18T01:36:44.6153838Z 2024-12-18T01:36:44.6153938Z 2024-12-18T01:36:44.6154306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.6154662Z 2024-12-18T01:36:44.6155148Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1258. 2024-12-18T01:36:44.6156059Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.6156633Z Given an operator and some sample arguments, tests if the operator is 2024-12-18T01:36:44.6157073Z registered correctly. 2024-12-18T01:36:44.6157259Z 2024-12-18T01:36:44.6157475Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-12-18T01:36:44.6158086Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-12-18T01:36:44.6158674Z and these APIs require that the functions you pass them satisfy certain 2024-12-18T01:36:44.6159251Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-12-18T01:36:44.6159737Z ``opcheck`` tests these metadata and properties. 2024-12-18T01:36:44.6160006Z 2024-12-18T01:36:44.6160131Z Concretely, we test the following: 2024-12-18T01:36:44.6160359Z 2024-12-18T01:36:44.6160539Z - test_schema: If the schema matches the implementation of 2024-12-18T01:36:44.6161058Z the operator. For example: if the schema specifies a Tensor is mutated, 2024-12-18T01:36:44.6161605Z then we check the implementation mutates the Tensor. If the schema 2024-12-18T01:36:44.6162152Z specifies that we return a new Tensor, then we check that the 2024-12-18T01:36:44.6162663Z implementation returns a new Tensor (instead of an existing one or 2024-12-18T01:36:44.6163109Z a view of an existing one). 2024-12-18T01:36:44.6163516Z - test_autograd_registration: If the operator supports training 2024-12-18T01:36:44.6164032Z (autograd): we check that its autograd formula is registered via 2024-12-18T01:36:44.6164591Z torch.library.register_autograd or a manual registration to one 2024-12-18T01:36:44.6165123Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2024-12-18T01:36:44.6165567Z registrations may lead to undefined behavior. 2024-12-18T01:36:44.6166000Z - test_faketensor: If the operator has a FakeTensor kernel 2024-12-18T01:36:44.6166468Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-12-18T01:36:44.6166968Z but not sufficient) for the operator to work with PyTorch compilation 2024-12-18T01:36:44.6167515Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2024-12-18T01:36:44.6168056Z (also sometimes known as a meta kernel) was registered for the 2024-12-18T01:36:44.6168544Z operator and that it is correct. This test takes the result of 2024-12-18T01:36:44.6169042Z running the operator on real tensors and the result of running 2024-12-18T01:36:44.6169539Z the operator on FakeTensors and checks that they have the same 2024-12-18T01:36:44.6170001Z Tensor metadata (sizes/strides/dtype/device/etc). 2024-12-18T01:36:44.6170454Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-12-18T01:36:44.6170942Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-12-18T01:36:44.6171431Z This checks that the outputs (and gradients, if applicable) are the 2024-12-18T01:36:44.6171906Z same under eager-mode PyTorch and torch.compile. 2024-12-18T01:36:44.6172355Z This test is a superset of ``test_faketensor`` and is an e2e test; 2024-12-18T01:36:44.6172826Z other things it tests are that the operator supports 2024-12-18T01:36:44.6173313Z functionalization and that the backward pass (if it exists) also 2024-12-18T01:36:44.6173766Z supports FakeTensor and functionalization. 2024-12-18T01:36:44.6174017Z 2024-12-18T01:36:44.6174209Z For best results, please call ``opcheck`` multiple times with a 2024-12-18T01:36:44.6174692Z representative set of inputs. If your operator supports 2024-12-18T01:36:44.6175203Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-12-18T01:36:44.6175766Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-12-18T01:36:44.6176258Z use ``opcheck`` with inputs on all supported devices. 2024-12-18T01:36:44.6176516Z 2024-12-18T01:36:44.6176604Z Args: 2024-12-18T01:36:44.6176902Z op: The operator. Must either be a function decorated with 2024-12-18T01:36:44.6177401Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-12-18T01:36:44.6177939Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-12-18T01:36:44.6178403Z args: The args to the operator 2024-12-18T01:36:44.6178727Z kwargs: The kwargs to the operator 2024-12-18T01:36:44.6179116Z test_utils: Tests that we should run. Default: all of them. 2024-12-18T01:36:44.6179540Z Example: ("test_schema", "test_faketensor") 2024-12-18T01:36:44.6179979Z raise_exception: If we should raise an exception on the first 2024-12-18T01:36:44.6180455Z error. If False, we will return a dict with information 2024-12-18T01:36:44.6180849Z on if each test passed or not. 2024-12-18T01:36:44.6181064Z 2024-12-18T01:36:44.6181157Z .. warning:: 2024-12-18T01:36:44.6181302Z 2024-12-18T01:36:44.6181516Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-12-18T01:36:44.6182080Z opcheck tests if your usage of torch.library APIs is correct while 2024-12-18T01:36:44.6182615Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-12-18T01:36:44.6183154Z mathematically correct. Use both to test custom ops that support 2024-12-18T01:36:44.6183585Z gradient computation. 2024-12-18T01:36:44.6183768Z 2024-12-18T01:36:44.6183855Z Example: 2024-12-18T01:36:44.6183999Z 2024-12-18T01:36:44.6184162Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:44.6184610Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:36:44.6185068Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2024-12-18T01:36:44.6185422Z >>> x_np = x.numpy(force=True) 2024-12-18T01:36:44.6185735Z >>> z_np = x_np * y 2024-12-18T01:36:44.6186042Z >>> return torch.from_numpy(z_np).to(x.device) 2024-12-18T01:36:44.6186374Z >>> 2024-12-18T01:36:44.6186609Z >>> @numpy_mul.register_fake 2024-12-18T01:36:44.6186912Z >>> def _(x, y): 2024-12-18T01:36:44.6187214Z >>> return torch.empty_like(x) 2024-12-18T01:36:44.6187510Z >>> 2024-12-18T01:36:44.6187759Z >>> def setup_context(ctx, inputs, output): 2024-12-18T01:36:44.6188091Z >>> y, = inputs 2024-12-18T01:36:44.6188351Z >>> ctx.y = y 2024-12-18T01:36:44.6188591Z >>> 2024-12-18T01:36:44.6188820Z >>> def backward(ctx, grad): 2024-12-18T01:36:44.6189130Z >>> return grad * ctx.y, None 2024-12-18T01:36:44.6189430Z >>> 2024-12-18T01:36:44.6189771Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2024-12-18T01:36:44.6190180Z >>> 2024-12-18T01:36:44.6190399Z >>> sample_inputs = [ 2024-12-18T01:36:44.6190681Z >>> (torch.randn(3), 3.14), 2024-12-18T01:36:44.6191014Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-12-18T01:36:44.6191395Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-12-18T01:36:44.6191843Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-12-18T01:36:44.6192227Z >>> ] 2024-12-18T01:36:44.6192440Z >>> 2024-12-18T01:36:44.6192671Z >>> for args in sample_inputs: 2024-12-18T01:36:44.6193019Z >>> torch.library.opcheck(numpy_mul, args) 2024-12-18T01:36:44.6193257Z 2024-12-18T01:36:44.6193350Z 2024-12-18T01:36:44.6193699Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.6194063Z 2024-12-18T01:36:44.6561029Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1226. 2024-12-18T01:36:44.6562686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.6564015Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2024-12-18T01:36:44.6564904Z 2024-12-18T01:36:44.6565232Z Loads an object saved with :func:`torch.save` from a file. 2024-12-18T01:36:44.6565793Z 2024-12-18T01:36:44.6566243Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-12-18T01:36:44.6567513Z which underlie tensors, specially. They are first deserialized on the 2024-12-18T01:36:44.6568583Z CPU and are then moved to the device they were saved from. If this fails 2024-12-18T01:36:44.6569639Z (e.g. because the run time system doesn't have certain devices), an exception 2024-12-18T01:36:44.6570753Z is raised. However, storages can be dynamically remapped to an alternative 2024-12-18T01:36:44.6571737Z set of devices using the :attr:`map_location` argument. 2024-12-18T01:36:44.6572268Z 2024-12-18T01:36:44.6572720Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-12-18T01:36:44.6573824Z storage with two arguments: storage and location. The storage argument 2024-12-18T01:36:44.6575072Z will be the initial deserialization of the storage, residing on the CPU. 2024-12-18T01:36:44.6576135Z Each serialized storage has a location tag associated with it which 2024-12-18T01:36:44.6577137Z identifies the device it was saved from, and this tag is the second 2024-12-18T01:36:44.6578187Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-12-18T01:36:44.6579289Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-12-18T01:36:44.6580384Z :attr:`map_location` should return either ``None`` or a storage. If 2024-12-18T01:36:44.6581236Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-12-18T01:36:44.6582201Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-12-18T01:36:44.6583090Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-12-18T01:36:44.6583662Z 2024-12-18T01:36:44.6583978Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-12-18T01:36:44.6585044Z a device tag, it indicates the location where all tensors should be loaded. 2024-12-18T01:36:44.6585608Z 2024-12-18T01:36:44.6586054Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-12-18T01:36:44.6587042Z appearing in the file (keys), to ones that specify where to put the 2024-12-18T01:36:44.6587612Z storages (values). 2024-12-18T01:36:44.6587821Z 2024-12-18T01:36:44.6588118Z User extensions can register their own location tags and tagging and 2024-12-18T01:36:44.6588924Z deserialization methods using :func:`torch.serialization.register_package`. 2024-12-18T01:36:44.6589437Z 2024-12-18T01:36:44.6589551Z Args: 2024-12-18T01:36:44.6590131Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-12-18T01:36:44.6590928Z or a string or os.PathLike object containing a file name 2024-12-18T01:36:44.6591731Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-12-18T01:36:44.6592418Z locations 2024-12-18T01:36:44.6592924Z pickle_module: module used for unpickling metadata and objects (has to 2024-12-18T01:36:44.6593594Z match the :attr:`pickle_module` used to serialize file) 2024-12-18T01:36:44.6594244Z weights_only: Indicates whether unpickler should be restricted to 2024-12-18T01:36:44.6594894Z loading only tensors, primitive types, dictionaries 2024-12-18T01:36:44.6595526Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-12-18T01:36:44.6596243Z See :ref:`weights-only` for more details. 2024-12-18T01:36:44.6597000Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-12-18T01:36:44.6598227Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-12-18T01:36:44.6599283Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-12-18T01:36:44.6600427Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-12-18T01:36:44.6601317Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-12-18T01:36:44.6602092Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-12-18T01:36:44.6602855Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-12-18T01:36:44.6603435Z :attr:`errors=...`. 2024-12-18T01:36:44.6603673Z 2024-12-18T01:36:44.6603826Z .. warning:: 2024-12-18T01:36:44.6604298Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-12-18T01:36:44.6604990Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-12-18T01:36:44.6605857Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-12-18T01:36:44.6606719Z during unpickling. Never load data that could have come from an untrusted 2024-12-18T01:36:44.6607612Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-12-18T01:36:44.6608165Z 2024-12-18T01:36:44.6608301Z .. note:: 2024-12-18T01:36:44.6608892Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-12-18T01:36:44.6609736Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-12-18T01:36:44.6610617Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-12-18T01:36:44.6611167Z 2024-12-18T01:36:44.6611283Z .. note:: 2024-12-18T01:36:44.6611805Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-12-18T01:36:44.6612614Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-12-18T01:36:44.6613410Z when loading files saved by Python 2 in Python 3. If this default 2024-12-18T01:36:44.6614162Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-12-18T01:36:44.6615051Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-12-18T01:36:44.6615880Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-12-18T01:36:44.6616687Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-12-18T01:36:44.6617148Z 2024-12-18T01:36:44.6617282Z Example: 2024-12-18T01:36:44.6617659Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-12-18T01:36:44.6618162Z >>> torch.load("tensors.pt", weights_only=True) 2024-12-18T01:36:44.6618657Z # Load all tensors onto the CPU 2024-12-18T01:36:44.6619278Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-12-18T01:36:44.6620064Z # Load all tensors onto the CPU, using a function 2024-12-18T01:36:44.6620547Z >>> torch.load( 2024-12-18T01:36:44.6621083Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-12-18T01:36:44.6621672Z ... ) 2024-12-18T01:36:44.6621992Z # Load all tensors onto GPU 1 2024-12-18T01:36:44.6622393Z >>> torch.load( 2024-12-18T01:36:44.6622732Z ... "tensors.pt", 2024-12-18T01:36:44.6623195Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-12-18T01:36:44.6623705Z ... weights_only=True, 2024-12-18T01:36:44.6624142Z ... ) # type: ignore[attr-defined] 2024-12-18T01:36:44.6624604Z # Map tensors from GPU 1 to GPU 0 2024-12-18T01:36:44.6625265Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-12-18T01:36:44.6625914Z # Load tensor from io.BytesIO object 2024-12-18T01:36:44.6626537Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-12-18T01:36:44.6627235Z >>> with open("tensor.pt", "rb") as f: 2024-12-18T01:36:44.6627796Z ... buffer = io.BytesIO(f.read()) 2024-12-18T01:36:44.6628256Z >>> torch.load(buffer, weights_only=False) 2024-12-18T01:36:44.6628800Z # Load a module with 'ascii' encoding for unpickling 2024-12-18T01:36:44.6629566Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-12-18T01:36:44.6630302Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-12-18T01:36:44.6630843Z 2024-12-18T01:36:44.6631338Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.6631826Z 2024-12-18T01:36:44.7648565Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=21. 2024-12-18T01:36:44.7649685Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:44.7650254Z Check if there is an available :ref:`accelerator`. 2024-12-18T01:36:44.7650568Z 2024-12-18T01:36:44.7650660Z Returns: 2024-12-18T01:36:44.7651075Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2024-12-18T01:36:44.7651477Z 2024-12-18T01:36:44.7651599Z Example:: 2024-12-18T01:36:44.7651748Z 2024-12-18T01:36:44.7652078Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:44.7652557Z 2024-12-18T01:36:44.7653215Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2024-12-18T01:36:44.7653858Z 2024-12-18T01:36:44.7654128Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:44.7654590Z ^ 2024-12-18T01:36:44.7666726Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=110. 2024-12-18T01:36:44.7667616Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:44.7668186Z Wait for all kernels in all streams on the given device to complete. 2024-12-18T01:36:44.7668500Z 2024-12-18T01:36:44.7668606Z Args: 2024-12-18T01:36:44.7669064Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2024-12-18T01:36:44.7669706Z the current :ref:`accelerator` device type. If not given, 2024-12-18T01:36:44.7670245Z use :func:`torch.accelerator.current_device_idx` by default. 2024-12-18T01:36:44.7670559Z 2024-12-18T01:36:44.7670871Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2024-12-18T01:36:44.7671302Z 2024-12-18T01:36:44.7671399Z Example:: 2024-12-18T01:36:44.7671533Z 2024-12-18T01:36:44.7671689Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:44.7672227Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:44.7672759Z >>> start_event = torch.Event(enable_timing=True) 2024-12-18T01:36:44.7673142Z >>> end_event = torch.Event(enable_timing=True) 2024-12-18T01:36:44.7673505Z >>> start_event.record() 2024-12-18T01:36:44.7673946Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2024-12-18T01:36:44.7674421Z >>> sum = torch.sum(tensor) 2024-12-18T01:36:44.7674740Z >>> end_event.record() 2024-12-18T01:36:44.7675050Z >>> torch.accelerator.synchronize() 2024-12-18T01:36:44.7675466Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2024-12-18T01:36:44.7675914Z 2024-12-18T01:36:44.7676581Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2024-12-18T01:36:44.7677230Z 2024-12-18T01:36:44.7677506Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:44.7678048Z ^ 2024-12-18T01:36:44.7901406Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=343. 2024-12-18T01:36:44.7902300Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:44.7902841Z Retrieves the CUDA runtime API module. 2024-12-18T01:36:44.7903075Z 2024-12-18T01:36:44.7903082Z 2024-12-18T01:36:44.7903388Z This function initializes the CUDA runtime environment if it is not already 2024-12-18T01:36:44.7904038Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-12-18T01:36:44.7904604Z runtime API module provides access to various CUDA runtime functions. 2024-12-18T01:36:44.7905072Z 2024-12-18T01:36:44.7905178Z Args: 2024-12-18T01:36:44.7905395Z ``None`` 2024-12-18T01:36:44.7905589Z 2024-12-18T01:36:44.7905682Z Returns: 2024-12-18T01:36:44.7905970Z module: The CUDA runtime API module (_cudart). 2024-12-18T01:36:44.7906272Z 2024-12-18T01:36:44.7906372Z Raises: 2024-12-18T01:36:44.7906741Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-12-18T01:36:44.7907549Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-12-18T01:36:44.7908091Z 2024-12-18T01:36:44.7908229Z Example of CUDA operations with profiling: 2024-12-18T01:36:44.7908623Z >>> import torch 2024-12-18T01:36:44.7908930Z >>> from torch.cuda import cudart, check_error 2024-12-18T01:36:44.7909320Z >>> import os 2024-12-18T01:36:44.7909573Z >>> 2024-12-18T01:36:44.7909807Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-12-18T01:36:44.7910174Z >>> 2024-12-18T01:36:44.7910458Z >>> def perform_cuda_operations_with_streams(): 2024-12-18T01:36:44.7910940Z >>> stream = torch.cuda.Stream() 2024-12-18T01:36:44.7911298Z >>> with torch.cuda.stream(stream): 2024-12-18T01:36:44.7911721Z >>> x = torch.randn(100, 100, device='cuda') 2024-12-18T01:36:44.7912083Z >>> y = torch.randn(100, 100, device='cuda') 2024-12-18T01:36:44.7912488Z >>> z = torch.mul(x, y) 2024-12-18T01:36:44.7912801Z >>> return z 2024-12-18T01:36:44.7913069Z >>> 2024-12-18T01:36:44.7913357Z >>> torch.cuda.synchronize() 2024-12-18T01:36:44.7913694Z >>> print("====== Start nsys profiling ======") 2024-12-18T01:36:44.7914138Z >>> check_error(cudart().cudaProfilerStart()) 2024-12-18T01:36:44.7914534Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-12-18T01:36:44.7915002Z >>> result = perform_cuda_operations_with_streams() 2024-12-18T01:36:44.7915439Z >>> print("CUDA operations completed.") 2024-12-18T01:36:44.7915964Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-12-18T01:36:44.7916442Z >>> print("====== End nsys profiling ======") 2024-12-18T01:36:44.7916693Z 2024-12-18T01:36:44.7916910Z To run this example and save the profiling information, execute: 2024-12-18T01:36:44.7917629Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:36:44.7918141Z 2024-12-18T01:36:44.7918408Z This command profiles the CUDA operations in the provided script and saves 2024-12-18T01:36:44.7919010Z the profiling information to a file named `trace_name.prof`. 2024-12-18T01:36:44.7919616Z The `--profile-from-start off` option ensures that profiling starts only 2024-12-18T01:36:44.7920168Z after the `cudaProfilerStart` call in the script. 2024-12-18T01:36:44.7920651Z The `--csv` and `--print-summary` options format the profiling output as a 2024-12-18T01:36:44.7921187Z CSV file and print a summary, respectively. 2024-12-18T01:36:44.7921738Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-12-18T01:36:44.7922419Z overwrite of the output file if it already exists. 2024-12-18T01:36:44.7922757Z 2024-12-18T01:36:44.7923571Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-12-18T01:36:44.7924375Z 2024-12-18T01:36:44.7924778Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:36:44.7925373Z ^ 2024-12-18T01:36:44.8058330Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2024-12-18T01:36:44.8059278Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.8059762Z 2024-12-18T01:36:44.8060008Z Append the given callback function to this ``Future``, which will be run 2024-12-18T01:36:44.8060607Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-12-18T01:36:44.8061191Z the same ``Future``, but the order in which they will be executed cannot 2024-12-18T01:36:44.8061754Z be guaranteed (to enforce a certain order consider chaining: 2024-12-18T01:36:44.8062260Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-12-18T01:36:44.8062893Z is the reference to this ``Future``. The callback function can use the 2024-12-18T01:36:44.8063482Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-12-18T01:36:44.8064086Z already completed, the given callback will be run immediately inline. 2024-12-18T01:36:44.8064429Z 2024-12-18T01:36:44.8064654Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-12-18T01:36:44.8065212Z callback might be invoked while the async kernels that are populating 2024-12-18T01:36:44.8065834Z those tensors haven't yet finished executing on the device. However, the 2024-12-18T01:36:44.8066498Z callback will be invoked with some dedicated streams set as current 2024-12-18T01:36:44.8067082Z (fetched from a global pool) which will be synchronized with those 2024-12-18T01:36:44.8067611Z kernels. Hence any operation performed by the callback on these tensors 2024-12-18T01:36:44.8068218Z will be scheduled on the device after the kernels complete. In other 2024-12-18T01:36:44.8068797Z words, as long as the callback doesn't switch streams, it can safely 2024-12-18T01:36:44.8069449Z manipulate the result without any additional synchronization. This is 2024-12-18T01:36:44.8069956Z similar to the non-blocking behavior of :meth:`wait`. 2024-12-18T01:36:44.8070280Z 2024-12-18T01:36:44.8070505Z Similarly, if the callback returns a value that contains tensors that 2024-12-18T01:36:44.8071070Z reside on a GPU, it can do so even if the kernels that are producing 2024-12-18T01:36:44.8071656Z these tensors are still running on the device, as long as the callback 2024-12-18T01:36:44.8072193Z didn't change streams during its execution. If one wants to change 2024-12-18T01:36:44.8072781Z streams, one must be careful to re-synchronize them with the original 2024-12-18T01:36:44.8073379Z streams, that is, those that were current when the callback was invoked. 2024-12-18T01:36:44.8073702Z 2024-12-18T01:36:44.8073816Z Args: 2024-12-18T01:36:44.8074178Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-12-18T01:36:44.8074654Z the only argument. 2024-12-18T01:36:44.8074899Z 2024-12-18T01:36:44.8074992Z Returns: 2024-12-18T01:36:44.8075290Z A new ``Future`` object that holds the return value of the 2024-12-18T01:36:44.8075888Z ``callback`` and will be marked as completed when the given 2024-12-18T01:36:44.8076327Z ``callback`` finishes. 2024-12-18T01:36:44.8076521Z 2024-12-18T01:36:44.8076709Z .. note:: Note that if the callback function throws, either 2024-12-18T01:36:44.8077264Z through the original future being completed with an exception and 2024-12-18T01:36:44.8077847Z calling ``fut.wait()``, or through other code in the callback, the 2024-12-18T01:36:44.8078469Z future returned by ``then`` will be marked appropriately with the 2024-12-18T01:36:44.8079013Z encountered error. However, if this callback later completes 2024-12-18T01:36:44.8079584Z additional futures, those futures are not marked as completed with 2024-12-18T01:36:44.8080183Z an error and the user is responsible for handling completion/waiting 2024-12-18T01:36:44.8080619Z on those futures independently. 2024-12-18T01:36:44.8080890Z 2024-12-18T01:36:44.8081005Z Example:: 2024-12-18T01:36:44.8081288Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:36:44.8081685Z >>> def callback(fut): 2024-12-18T01:36:44.8082001Z ... print(f"RPC return value is {fut.wait()}.") 2024-12-18T01:36:44.8082416Z >>> fut = torch.futures.Future() 2024-12-18T01:36:44.8082841Z >>> # The inserted callback will print the return value when 2024-12-18T01:36:44.8083312Z >>> # receiving the response from "worker1" 2024-12-18T01:36:44.8083644Z >>> cb_fut = fut.then(callback) 2024-12-18T01:36:44.8084015Z >>> chain_cb_fut = cb_fut.then( 2024-12-18T01:36:44.8084371Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-12-18T01:36:44.8084780Z ... ) 2024-12-18T01:36:44.8085002Z >>> fut.set_result(5) 2024-12-18T01:36:44.8085319Z RPC return value is 5. 2024-12-18T01:36:44.8085637Z Chained cb done. None 2024-12-18T01:36:44.8085824Z 2024-12-18T01:36:44.8086095Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.8086502Z 2024-12-18T01:36:44.8087095Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=209. 2024-12-18T01:36:44.8088035Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.8088452Z 2024-12-18T01:36:44.8088712Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-12-18T01:36:44.8089312Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-12-18T01:36:44.8089741Z cannot be marked completed twice. 2024-12-18T01:36:44.8090009Z 2024-12-18T01:36:44.8090227Z If the result contains tensors that reside on GPUs, this method can be 2024-12-18T01:36:44.8090825Z called even if the asynchronous kernels that are populating those 2024-12-18T01:36:44.8091359Z tensors haven't yet completed running on the device, provided that the 2024-12-18T01:36:44.8091976Z streams on which those kernels were enqueued are set as the current ones 2024-12-18T01:36:44.8092589Z when this method is called. Put simply, it's safe to call this method 2024-12-18T01:36:44.8093176Z immediately after launching those kernels, without any additional 2024-12-18T01:36:44.8093759Z synchronization, as long as one doesn't change streams in between. This 2024-12-18T01:36:44.8094355Z method will record events on all the relevant current streams and will 2024-12-18T01:36:44.8094953Z use them to ensure proper scheduling for all the consumers of this 2024-12-18T01:36:44.8095362Z ``Future``. 2024-12-18T01:36:44.8095537Z 2024-12-18T01:36:44.8095643Z Args: 2024-12-18T01:36:44.8095917Z result (object): the result object of this ``Future``. 2024-12-18T01:36:44.8096198Z 2024-12-18T01:36:44.8096296Z Example:: 2024-12-18T01:36:44.8096577Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:36:44.8096988Z >>> import threading 2024-12-18T01:36:44.8097257Z >>> import time 2024-12-18T01:36:44.8097509Z >>> def slow_set_future(fut, value): 2024-12-18T01:36:44.8097835Z ... time.sleep(0.5) 2024-12-18T01:36:44.8098305Z ... fut.set_result(value) 2024-12-18T01:36:44.8098619Z >>> fut = torch.futures.Future() 2024-12-18T01:36:44.8098939Z >>> t = threading.Thread( 2024-12-18T01:36:44.8099288Z ... target=slow_set_future, 2024-12-18T01:36:44.8099599Z ... args=(fut, torch.ones(2) * 3) 2024-12-18T01:36:44.8099904Z ... ) 2024-12-18T01:36:44.8100126Z >>> t.start() 2024-12-18T01:36:44.8100437Z >>> print(fut.wait()) 2024-12-18T01:36:44.8100706Z tensor([3., 3.]) 2024-12-18T01:36:44.8100953Z >>> t.join() 2024-12-18T01:36:44.8101089Z 2024-12-18T01:36:44.8101352Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.8101711Z 2024-12-18T01:36:44.8202985Z msg = Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144. 2024-12-18T01:36:44.8203931Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.8204524Z Compiles compute shader from source and allows one to invoke kernels 2024-12-18T01:36:44.8204994Z defined there from the comfort of Python runtime 2024-12-18T01:36:44.8205357Z Example:: 2024-12-18T01:36:44.8205585Z 2024-12-18T01:36:44.8205725Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2024-12-18T01:36:44.8206103Z >>> lib = torch.mps._compile_shader( 2024-12-18T01:36:44.8206701Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2024-12-18T01:36:44.8207266Z ... ) 2024-12-18T01:36:44.8207530Z >>> x = torch.zeros(16, device="mps") 2024-12-18T01:36:44.8207864Z >>> lib.full(x, 3.14) 2024-12-18T01:36:44.8208191Z 2024-12-18T01:36:44.8208569Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.8208926Z 2024-12-18T01:36:44.8424709Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2024-12-18T01:36:44.8425696Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:44.8426223Z Return the sum of each row of the given sparse tensor. 2024-12-18T01:36:44.8426504Z 2024-12-18T01:36:44.8426729Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-12-18T01:36:44.8427348Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-12-18T01:36:44.8427879Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-12-18T01:36:44.8428368Z returns a dense tensor instead of a sparse tensor. 2024-12-18T01:36:44.8428638Z 2024-12-18T01:36:44.8428912Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-12-18T01:36:44.8429476Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-12-18T01:36:44.8429794Z 2024-12-18T01:36:44.8430016Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-12-18T01:36:44.8430600Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-12-18T01:36:44.8430963Z 2024-12-18T01:36:44.8431057Z Args: 2024-12-18T01:36:44.8431326Z input (Tensor): the input sparse tensor 2024-12-18T01:36:44.8431843Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-12-18T01:36:44.8432322Z over all dims. 2024-12-18T01:36:44.8432771Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-12-18T01:36:44.8433269Z Default: dtype of :attr:`input`. 2024-12-18T01:36:44.8433505Z 2024-12-18T01:36:44.8433608Z Example:: 2024-12-18T01:36:44.8433741Z 2024-12-18T01:36:44.8433847Z >>> nnz = 3 2024-12-18T01:36:44.8434096Z >>> dims = [5, 5, 2, 3] 2024-12-18T01:36:44.8434428Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-12-18T01:36:44.8434885Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-12-18T01:36:44.8435311Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-12-18T01:36:44.8435709Z >>> size = torch.Size(dims) 2024-12-18T01:36:44.8436067Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:44.8436440Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-12-18T01:36:44.8436777Z >>> S 2024-12-18T01:36:44.8437075Z tensor(indices=tensor([[2, 0, 3], 2024-12-18T01:36:44.8437400Z [2, 4, 1]]), 2024-12-18T01:36:44.8437747Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-12-18T01:36:44.8438113Z [ 0.3411, 0.0918, -0.2312]], 2024-12-18T01:36:44.8438332Z 2024-12-18T01:36:44.8438449Z [[ 0.5348, 0.0634, -2.0494], 2024-12-18T01:36:44.8438790Z [-0.7125, -1.0646, 2.1844]], 2024-12-18T01:36:44.8439024Z 2024-12-18T01:36:44.8439136Z [[ 0.1276, 0.1874, -0.6334], 2024-12-18T01:36:44.8439477Z [-1.9682, -0.5340, 0.7483]]]), 2024-12-18T01:36:44.8439849Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:36:44.8440140Z 2024-12-18T01:36:44.8440342Z # when sum over only part of sparse_dims, return a sparse tensor 2024-12-18T01:36:44.8440752Z >>> torch.sparse.sum(S, [1, 3]) 2024-12-18T01:36:44.8441090Z tensor(indices=tensor([[0, 2, 3]]), 2024-12-18T01:36:44.8441428Z values=tensor([[-1.4512, 0.4073], 2024-12-18T01:36:44.8441767Z [-0.8901, 0.2017], 2024-12-18T01:36:44.8442095Z [-0.3183, -1.7539]]), 2024-12-18T01:36:44.8442486Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:36:44.8442746Z 2024-12-18T01:36:44.8442903Z # when sum over all sparse dim, return a dense tensor 2024-12-18T01:36:44.8443275Z # with summed dims squeezed 2024-12-18T01:36:44.8443594Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-12-18T01:36:44.8443921Z tensor([-2.6596, -1.1450]) 2024-12-18T01:36:44.8444191Z 2024-12-18T01:36:44.8444561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:44.8444936Z 2024-12-18T01:36:45.5278591Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=40. 2024-12-18T01:36:45.5279624Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:45.5279999Z 2024-12-18T01:36:45.5280233Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-12-18T01:36:45.5280769Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-12-18T01:36:45.5281286Z pushes the map into PyTorch operations called by ``func``, effectively 2024-12-18T01:36:45.5281735Z vectorizing those operations. 2024-12-18T01:36:45.5281938Z 2024-12-18T01:36:45.5282148Z vmap is useful for handling batch dimensions: one can write a function 2024-12-18T01:36:45.5282670Z ``func`` that runs on examples and then lift it to a function that can 2024-12-18T01:36:45.5283196Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-12-18T01:36:45.5283696Z compute batched gradients when composed with autograd. 2024-12-18T01:36:45.5283969Z 2024-12-18T01:36:45.5284088Z .. note:: 2024-12-18T01:36:45.5284405Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-12-18T01:36:45.5284828Z convenience. Use whichever one you'd like. 2024-12-18T01:36:45.5285062Z 2024-12-18T01:36:45.5285160Z Args: 2024-12-18T01:36:45.5285491Z func (function): A Python function that takes one or more arguments. 2024-12-18T01:36:45.5285918Z Must return one or more Tensors. 2024-12-18T01:36:45.5286343Z in_dims (int or nested structure): Specifies which dimension of the 2024-12-18T01:36:45.5286833Z inputs should be mapped over. ``in_dims`` should have a 2024-12-18T01:36:45.5287308Z structure like the inputs. If the ``in_dim`` for a particular 2024-12-18T01:36:45.5287801Z input is None, then that indicates there is no map dimension. 2024-12-18T01:36:45.5288197Z Default: 0. 2024-12-18T01:36:45.5288541Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-12-18T01:36:45.5289054Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-12-18T01:36:45.5289603Z it should have one element per output. Default: 0. 2024-12-18T01:36:45.5290063Z randomness (str): Specifies whether the randomness in this 2024-12-18T01:36:45.5290577Z vmap should be the same or different across batches. If 'different', 2024-12-18T01:36:45.5291093Z the randomness for each batch will be different. If 'same', the 2024-12-18T01:36:45.5291625Z randomness will be the same across batches. If 'error', any calls to 2024-12-18T01:36:45.5292168Z random functions will error. Default: 'error'. WARNING: this flag 2024-12-18T01:36:45.5292697Z only applies to random PyTorch operations and does not apply to 2024-12-18T01:36:45.5293151Z Python's random module or numpy randomness. 2024-12-18T01:36:45.5293632Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-12-18T01:36:45.5294246Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-12-18T01:36:45.5294838Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-12-18T01:36:45.5295477Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-12-18T01:36:45.5295868Z 2024-12-18T01:36:45.5295959Z Returns: 2024-12-18T01:36:45.5296326Z Returns a new "batched" function. It takes the same inputs as 2024-12-18T01:36:45.5296811Z ``func``, except each input has an extra dimension at the index 2024-12-18T01:36:45.5297290Z specified by ``in_dims``. It takes returns the same outputs as 2024-12-18T01:36:45.5297781Z ``func``, except each output has an extra dimension at the index 2024-12-18T01:36:45.5298373Z specified by ``out_dims``. 2024-12-18T01:36:45.5298561Z 2024-12-18T01:36:45.5298667Z .. warning: 2024-12-18T01:36:45.5299006Z :func:`vmap` works best with functional-style code. Please do not 2024-12-18T01:36:45.5299544Z perform any side-effects in ``func``, with the exception of 2024-12-18T01:36:45.5300075Z in-place PyTorch operations. Examples of side-effects include mutating 2024-12-18T01:36:45.5300646Z Python data structures and assigning values to variables not captured 2024-12-18T01:36:45.5301076Z in ``func``. 2024-12-18T01:36:45.5301215Z 2024-12-18T01:36:45.5301463Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-12-18T01:36:45.5302034Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-12-18T01:36:45.5302564Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-12-18T01:36:45.5302898Z 2024-12-18T01:36:45.5303060Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:36:45.5303517Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-12-18T01:36:45.5303960Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:36:45.5304304Z >>> batched_dot(x, y) 2024-12-18T01:36:45.5304469Z 2024-12-18T01:36:45.5304709Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-12-18T01:36:45.5305149Z model authoring experience. 2024-12-18T01:36:45.5305345Z 2024-12-18T01:36:45.5305456Z >>> batch_size, feature_size = 3, 5 2024-12-18T01:36:45.5305844Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-12-18T01:36:45.5306217Z >>> 2024-12-18T01:36:45.5306443Z >>> def model(feature_vec): 2024-12-18T01:36:45.5306753Z >>> # Very simple linear model with activation 2024-12-18T01:36:45.5307126Z >>> return feature_vec.dot(weights).relu() 2024-12-18T01:36:45.5307456Z >>> 2024-12-18T01:36:45.5307726Z >>> examples = torch.randn(batch_size, feature_size) 2024-12-18T01:36:45.5308108Z >>> result = torch.vmap(model)(examples) 2024-12-18T01:36:45.5308330Z 2024-12-18T01:36:45.5308575Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-12-18T01:36:45.5309172Z or impossible to batch. One example is higher-order gradient computation. 2024-12-18T01:36:45.5309818Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-12-18T01:36:45.5310385Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-12-18T01:36:45.5310976Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-12-18T01:36:45.5311576Z we can vectorize the whole computation, computing the Jacobian in a single 2024-12-18T01:36:45.5312017Z call to ``autograd.grad``. 2024-12-18T01:36:45.5312205Z 2024-12-18T01:36:45.5312295Z >>> # Setup 2024-12-18T01:36:45.5312525Z >>> N = 5 2024-12-18T01:36:45.5312759Z >>> f = lambda x: x ** 2 2024-12-18T01:36:45.5313065Z >>> x = torch.randn(N, requires_grad=True) 2024-12-18T01:36:45.5313377Z >>> y = f(x) 2024-12-18T01:36:45.5313617Z >>> I_N = torch.eye(N) 2024-12-18T01:36:45.5313933Z >>> 2024-12-18T01:36:45.5314163Z >>> # Sequential approach 2024-12-18T01:36:45.5314571Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-12-18T01:36:45.5315010Z >>> for v in I_N.unbind()] 2024-12-18T01:36:45.5315365Z >>> jacobian = torch.stack(jacobian_rows) 2024-12-18T01:36:45.5315758Z >>> 2024-12-18T01:36:45.5316007Z >>> # vectorized gradient computation 2024-12-18T01:36:45.5316330Z >>> def get_vjp(v): 2024-12-18T01:36:45.5316660Z >>> return torch.autograd.grad(y, x, v) 2024-12-18T01:36:45.5317018Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-12-18T01:36:45.5317248Z 2024-12-18T01:36:45.5317504Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-12-18T01:36:45.5317887Z 2024-12-18T01:36:45.5318032Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:36:45.5318562Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-12-18T01:36:45.5319081Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-12-18T01:36:45.5319509Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-12-18T01:36:45.5319757Z 2024-12-18T01:36:45.5319991Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-12-18T01:36:45.5320496Z the dimension that each inputs are batched along as 2024-12-18T01:36:45.5320767Z 2024-12-18T01:36:45.5320909Z >>> torch.dot # [N], [N] -> [] 2024-12-18T01:36:45.5321387Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-12-18T01:36:45.5321848Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:36:45.5322314Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-12-18T01:36:45.5322676Z 2024-12-18T01:36:45.5322929Z If there are multiple inputs each of which is batched along different dimensions, 2024-12-18T01:36:45.5323506Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-12-18T01:36:45.5323826Z 2024-12-18T01:36:45.5323970Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:36:45.5324458Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-12-18T01:36:45.5324930Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:36:45.5325388Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-12-18T01:36:45.5325747Z 2024-12-18T01:36:45.5325982Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-12-18T01:36:45.5326448Z matching the shape of the input: 2024-12-18T01:36:45.5326660Z 2024-12-18T01:36:45.5326804Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-12-18T01:36:45.5327179Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:36:45.5327518Z >>> input = {'x': x, 'y': y} 2024-12-18T01:36:45.5327878Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-12-18T01:36:45.5328274Z >>> batched_dot(input) 2024-12-18T01:36:45.5328457Z 2024-12-18T01:36:45.5328730Z By default, the output is batched along the first dimension. However, it can be batched 2024-12-18T01:36:45.5329280Z along any dimension by using ``out_dims`` 2024-12-18T01:36:45.5329504Z 2024-12-18T01:36:45.5329624Z >>> f = lambda x: x ** 2 2024-12-18T01:36:45.5329914Z >>> x = torch.randn(2, 5) 2024-12-18T01:36:45.5330241Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-12-18T01:36:45.5330587Z >>> batched_pow(x) # [5, 2] 2024-12-18T01:36:45.5330770Z 2024-12-18T01:36:45.5331068Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-12-18T01:36:45.5331565Z accept kwargs 2024-12-18T01:36:45.5331699Z 2024-12-18T01:36:45.5331815Z >>> x = torch.randn([2, 5]) 2024-12-18T01:36:45.5332095Z >>> def fn(x, scale=4.): 2024-12-18T01:36:45.5332376Z >>> return x * scale 2024-12-18T01:36:45.5332641Z >>> 2024-12-18T01:36:45.5332911Z >>> batched_pow = torch.vmap(fn) 2024-12-18T01:36:45.5333267Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-12-18T01:36:45.5333734Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-12-18T01:36:45.5334084Z 2024-12-18T01:36:45.5334192Z .. note:: 2024-12-18T01:36:45.5334544Z vmap does not provide general autobatching or handle variable-length 2024-12-18T01:36:45.5334990Z sequences out of the box. 2024-12-18T01:36:45.5335177Z 2024-12-18T01:36:45.5335476Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:45.5335833Z 2024-12-18T01:36:46.8199406Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=20. 2024-12-18T01:36:46.8200314Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:46.8200954Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-12-18T01:36:46.8201356Z 2024-12-18T01:36:46.8201584Z This is a more structured way of using triton kernels with PyTorch. 2024-12-18T01:36:46.8202384Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2024-12-18T01:36:46.8203015Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2024-12-18T01:36:46.8203500Z that is simpler; 2024-12-18T01:36:46.8203927Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2024-12-18T01:36:46.8204550Z want to create an operator that behaves like PyTorch built-in operators. 2024-12-18T01:36:46.8205130Z For example, you may use a ``torch.library`` wrapper API to define the 2024-12-18T01:36:46.8205904Z behavior of the triton kernel when passed a tensor subclass or under 2024-12-18T01:36:46.8206569Z a TorchDispatchMode. 2024-12-18T01:36:46.8206931Z 2024-12-18T01:36:46.8207349Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2024-12-18T01:36:46.8207837Z when the implementation 2024-12-18T01:36:46.8208255Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-12-18T01:36:46.8208771Z custom operators as opaque (:func:`torch.compile` and 2024-12-18T01:36:46.8209294Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-12-18T01:36:46.8209861Z makes the implementation visible to these subsystems, allowing them 2024-12-18T01:36:46.8210317Z to optimize the triton kernel(s). 2024-12-18T01:36:46.8210542Z 2024-12-18T01:36:46.8210742Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-12-18T01:36:46.8211277Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-12-18T01:36:46.8211823Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2024-12-18T01:36:46.8212130Z 2024-12-18T01:36:46.8212240Z Args: 2024-12-18T01:36:46.8212680Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-12-18T01:36:46.8213241Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-12-18T01:36:46.8213758Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-12-18T01:36:46.8214381Z To avoid name collisions, please use your project name as the namespace; 2024-12-18T01:36:46.8214951Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-12-18T01:36:46.8215555Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-12-18T01:36:46.8216185Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-12-18T01:36:46.8216813Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-12-18T01:36:46.8217386Z schema (None | str): A schema string for the operator. If None 2024-12-18T01:36:46.8217893Z (recommended) we'll infer a schema for the operator from its type 2024-12-18T01:36:46.8218487Z annotations. We recommend letting us infer a schema unless you 2024-12-18T01:36:46.8218938Z have a specific reason not to. 2024-12-18T01:36:46.8219330Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-12-18T01:36:46.8219595Z 2024-12-18T01:36:46.8219711Z Example:: 2024-12-18T01:36:46.8219846Z 2024-12-18T01:36:46.8220002Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:46.8220348Z >>> import torch 2024-12-18T01:36:46.8220739Z >>> from torch._library import triton_op, wrap_triton 2024-12-18T01:36:46.8221110Z >>> 2024-12-18T01:36:46.8221347Z >>> import triton 2024-12-18T01:36:46.8221651Z >>> from triton import language as tl 2024-12-18T01:36:46.8221968Z >>> 2024-12-18T01:36:46.8222203Z >>> @triton.jit 2024-12-18T01:36:46.8222472Z >>> def add_kernel( 2024-12-18T01:36:46.8222754Z >>> in_ptr0, 2024-12-18T01:36:46.8223010Z >>> in_ptr1, 2024-12-18T01:36:46.8223287Z >>> out_ptr, 2024-12-18T01:36:46.8223560Z >>> n_elements, 2024-12-18T01:36:46.8223901Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:36:46.8224235Z >>> ): 2024-12-18T01:36:46.8224488Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:36:46.8224844Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:36:46.8225237Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:36:46.8225628Z >>> mask = offsets < n_elements 2024-12-18T01:36:46.8225999Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:36:46.8226377Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:36:46.8226733Z >>> output = x + y 2024-12-18T01:36:46.8227079Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:36:46.8227442Z >>> 2024-12-18T01:36:46.8227719Z >>> @triton_op("mylib::add", mutates_args={}) 2024-12-18T01:36:46.8228165Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-12-18T01:36:46.8228589Z >>> output = torch.empty_like(x) 2024-12-18T01:36:46.8228945Z >>> n_elements = output.numel() 2024-12-18T01:36:46.8229272Z >>> 2024-12-18T01:36:46.8229514Z >>> def grid(meta): 2024-12-18T01:36:46.8229885Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:36:46.8230266Z >>> 2024-12-18T01:36:46.8230593Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2024-12-18T01:36:46.8231092Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-12-18T01:36:46.8231505Z >>> return output 2024-12-18T01:36:46.8231785Z >>> 2024-12-18T01:36:46.8240978Z >>> @torch.compile 2024-12-18T01:36:46.8241369Z >>> def f(x, y): 2024-12-18T01:36:46.8241658Z >>> return add(x, y) 2024-12-18T01:36:46.8241941Z >>> 2024-12-18T01:36:46.8242204Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:36:46.8242584Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:36:46.8242915Z >>> 2024-12-18T01:36:46.8243157Z >>> z = f(x, y) 2024-12-18T01:36:46.8243521Z >>> assert torch.allclose(z, x + y) 2024-12-18T01:36:46.8243772Z 2024-12-18T01:36:46.8243866Z 2024-12-18T01:36:46.8244248Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:46.8244631Z 2024-12-18T01:36:46.8245131Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=183. 2024-12-18T01:36:46.8245999Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:46.8246574Z Allows capture of a triton kernel into a graph via make_fx or 2024-12-18T01:36:46.8246987Z non-strict ``torch.export``. 2024-12-18T01:36:46.8247197Z 2024-12-18T01:36:46.8247385Z These technologies perform Dispatcher-based tracing (via 2024-12-18T01:36:46.8247926Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-12-18T01:36:46.8248454Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2024-12-18T01:36:46.8248897Z can actually be traced into a graph. 2024-12-18T01:36:46.8249119Z 2024-12-18T01:36:46.8249350Z Please use this API together with :func:`torch.library.triton_op`. 2024-12-18T01:36:46.8249673Z 2024-12-18T01:36:46.8249771Z Examples: 2024-12-18T01:36:46.8249922Z 2024-12-18T01:36:46.8250067Z >>> # xdoctest: +SKIP 2024-12-18T01:36:46.8250367Z >>> import torch 2024-12-18T01:36:46.8250647Z >>> import triton 2024-12-18T01:36:46.8250952Z >>> from triton import language as tl 2024-12-18T01:36:46.8251365Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:36:46.8251808Z >>> from torch.library import wrap_triton 2024-12-18T01:36:46.8252150Z >>> 2024-12-18T01:36:46.8252385Z >>> @triton.jit 2024-12-18T01:36:46.8252659Z >>> def add_kernel( 2024-12-18T01:36:46.8252926Z >>> in_ptr0, 2024-12-18T01:36:46.8253196Z >>> in_ptr1, 2024-12-18T01:36:46.8253499Z >>> out_ptr, 2024-12-18T01:36:46.8253769Z >>> n_elements, 2024-12-18T01:36:46.8254072Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:36:46.8254381Z >>> ): 2024-12-18T01:36:46.8254642Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:36:46.8254993Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:36:46.8255388Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:36:46.8255773Z >>> mask = offsets < n_elements 2024-12-18T01:36:46.8256124Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:36:46.8256516Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:36:46.8256877Z >>> output = x + y 2024-12-18T01:36:46.8257236Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:36:46.8257603Z >>> 2024-12-18T01:36:46.8257834Z >>> def add(x, y): 2024-12-18T01:36:46.8258147Z >>> output = torch.empty_like(x) 2024-12-18T01:36:46.8258507Z >>> n_elements = output.numel() 2024-12-18T01:36:46.8258835Z >>> 2024-12-18T01:36:46.8259082Z >>> def grid_fn(meta): 2024-12-18T01:36:46.8259456Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:36:46.8259849Z >>> 2024-12-18T01:36:46.8260197Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-12-18T01:36:46.8260629Z >>> return output 2024-12-18T01:36:46.8260924Z >>> 2024-12-18T01:36:46.8261176Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:36:46.8261543Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:36:46.8261892Z >>> gm = make_fx(add)(x, y) 2024-12-18T01:36:46.8262211Z >>> print(gm.code) 2024-12-18T01:36:46.8262517Z >>> # def forward(self, x_1, y_1): 2024-12-18T01:36:46.8262975Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-12-18T01:36:46.8263583Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-12-18T01:36:46.8264149Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-12-18T01:36:46.8264532Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-12-18T01:36:46.8264930Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-12-18T01:36:46.8265349Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-12-18T01:36:46.8265686Z >>> # }) 2024-12-18T01:36:46.8265970Z >>> # return empty_like 2024-12-18T01:36:46.8266188Z 2024-12-18T01:36:46.8266283Z 2024-12-18T01:36:46.8266676Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:46.8267039Z 2024-12-18T01:36:46.8916028Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=330. 2024-12-18T01:36:46.8917208Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:46.8917711Z 2024-12-18T01:36:46.8918046Z Raises an AssertionError if two items are not equal up to desired 2024-12-18T01:36:46.8918752Z precision. 2024-12-18T01:36:46.8918998Z 2024-12-18T01:36:46.8919282Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:36:46.8919837Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:36:46.8920336Z instead of this function for more consistent floating point 2024-12-18T01:36:46.8920751Z comparisons. 2024-12-18T01:36:46.8920914Z 2024-12-18T01:36:46.8921146Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-12-18T01:36:46.8921474Z 2024-12-18T01:36:46.8921653Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-12-18T01:36:46.8921925Z 2024-12-18T01:36:46.8922188Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:36:46.8922976Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-12-18T01:36:46.8923640Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-12-18T01:36:46.8924133Z delegates to assert_array_almost_equal 2024-12-18T01:36:46.8924354Z 2024-12-18T01:36:46.8924468Z Parameters 2024-12-18T01:36:46.8924709Z ---------- 2024-12-18T01:36:46.8924945Z actual : array_like 2024-12-18T01:36:46.8925219Z The object to check. 2024-12-18T01:36:46.8925503Z desired : array_like 2024-12-18T01:36:46.8925779Z The expected object. 2024-12-18T01:36:46.8926052Z decimal : int, optional 2024-12-18T01:36:46.8926348Z Desired precision, default is 7. 2024-12-18T01:36:46.8926678Z err_msg : str, optional 2024-12-18T01:36:46.8927086Z The error message to be printed in case of failure. 2024-12-18T01:36:46.8927537Z verbose : bool, optional 2024-12-18T01:36:46.8928063Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:46.8928498Z 2024-12-18T01:36:46.8928592Z Raises 2024-12-18T01:36:46.8928857Z ------ 2024-12-18T01:36:46.8929134Z AssertionError 2024-12-18T01:36:46.8929487Z If actual and desired are not equal up to specified precision. 2024-12-18T01:36:46.8929793Z 2024-12-18T01:36:46.8929902Z See Also 2024-12-18T01:36:46.8930114Z -------- 2024-12-18T01:36:46.8930483Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:46.8930979Z relative and/or absolute precision. 2024-12-18T01:36:46.8931439Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:46.8931757Z 2024-12-18T01:36:46.8931869Z Examples 2024-12-18T01:36:46.8932080Z -------- 2024-12-18T01:36:46.8932381Z >>> from torch._numpy.testing import assert_almost_equal 2024-12-18T01:36:46.8932806Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-12-18T01:36:46.8933238Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-12-18T01:36:46.8933651Z Traceback (most recent call last): 2024-12-18T01:36:46.8933951Z ... 2024-12-18T01:36:46.8934185Z AssertionError: 2024-12-18T01:36:46.8934550Z Arrays are not almost equal to 10 decimals 2024-12-18T01:36:46.8934891Z ACTUAL: 2.3333333333333 2024-12-18T01:36:46.8935170Z DESIRED: 2.33333334 2024-12-18T01:36:46.8935327Z 2024-12-18T01:36:46.8935483Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-12-18T01:36:46.8935897Z ... np.array([1.0,2.33333334]), decimal=9) 2024-12-18T01:36:46.8936264Z Traceback (most recent call last): 2024-12-18T01:36:46.8936572Z ... 2024-12-18T01:36:46.8936801Z AssertionError: 2024-12-18T01:36:46.8937080Z Arrays are not almost equal to 9 decimals 2024-12-18T01:36:46.8937413Z 2024-12-18T01:36:46.8937647Z Mismatched elements: 1 / 2 (50%) 2024-12-18T01:36:46.8937985Z Max absolute difference: 6.666699636781459e-09 2024-12-18T01:36:46.8938365Z Max relative difference: 2.8571569790287484e-09 2024-12-18T01:36:46.8938795Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:36:46.8939193Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:36:46.8939443Z 2024-12-18T01:36:46.8939448Z 2024-12-18T01:36:46.8939714Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:46.8940078Z 2024-12-18T01:36:46.8940801Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=455. 2024-12-18T01:36:46.8941728Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:46.8942099Z 2024-12-18T01:36:46.8942334Z Raises an AssertionError if two items are not equal up to significant 2024-12-18T01:36:46.8942769Z digits. 2024-12-18T01:36:46.8942892Z 2024-12-18T01:36:46.8943074Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:36:46.8943546Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:36:46.8944040Z instead of this function for more consistent floating point 2024-12-18T01:36:46.8944485Z comparisons. 2024-12-18T01:36:46.8944650Z 2024-12-18T01:36:46.8944847Z Given two numbers, check that they are approximately equal. 2024-12-18T01:36:46.8945355Z Approximately equal is defined as the number of significant digits 2024-12-18T01:36:46.8945787Z that agree. 2024-12-18T01:36:46.8945928Z 2024-12-18T01:36:46.8946024Z Parameters 2024-12-18T01:36:46.8946256Z ---------- 2024-12-18T01:36:46.8946489Z actual : scalar 2024-12-18T01:36:46.8946730Z The object to check. 2024-12-18T01:36:46.8947009Z desired : scalar 2024-12-18T01:36:46.8947268Z The expected object. 2024-12-18T01:36:46.8947556Z significant : int, optional 2024-12-18T01:36:46.8947872Z Desired precision, default is 7. 2024-12-18T01:36:46.8948184Z err_msg : str, optional 2024-12-18T01:36:46.8948509Z The error message to be printed in case of failure. 2024-12-18T01:36:46.8948889Z verbose : bool, optional 2024-12-18T01:36:46.8949275Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:46.8949593Z 2024-12-18T01:36:46.8949698Z Raises 2024-12-18T01:36:46.8949907Z ------ 2024-12-18T01:36:46.8950137Z AssertionError 2024-12-18T01:36:46.8950481Z If actual and desired are not equal up to specified precision. 2024-12-18T01:36:46.8950787Z 2024-12-18T01:36:46.8950894Z See Also 2024-12-18T01:36:46.8951119Z -------- 2024-12-18T01:36:46.8951479Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:46.8951981Z relative and/or absolute precision. 2024-12-18T01:36:46.8952445Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:46.8952762Z 2024-12-18T01:36:46.8952873Z Examples 2024-12-18T01:36:46.8953101Z -------- 2024-12-18T01:36:46.8953492Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-12-18T01:36:46.8954141Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-12-18T01:36:46.8954641Z ... significant=8) 2024-12-18T01:36:46.8955168Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-12-18T01:36:46.8955755Z ... significant=8) 2024-12-18T01:36:46.8956118Z Traceback (most recent call last): 2024-12-18T01:36:46.8956421Z ... 2024-12-18T01:36:46.8956660Z AssertionError: 2024-12-18T01:36:46.8956970Z Items are not equal to 8 significant digits: 2024-12-18T01:36:46.8957320Z ACTUAL: 1.234567e-21 2024-12-18T01:36:46.8957596Z DESIRED: 1.2345672e-21 2024-12-18T01:36:46.8957760Z 2024-12-18T01:36:46.8957942Z the evaluated condition that raises the exception is 2024-12-18T01:36:46.8958211Z 2024-12-18T01:36:46.8958395Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-12-18T01:36:46.8958786Z True 2024-12-18T01:36:46.8958919Z 2024-12-18T01:36:46.8958923Z 2024-12-18T01:36:46.8959219Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:46.8959599Z 2024-12-18T01:36:46.8960152Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=734. 2024-12-18T01:36:46.8961078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:46.8961449Z 2024-12-18T01:36:46.8961676Z Raises an AssertionError if two array_like objects are not equal. 2024-12-18T01:36:46.8962027Z 2024-12-18T01:36:46.8962261Z Given two array_like objects, check that the shape is equal and all 2024-12-18T01:36:46.8962796Z elements of these objects are equal (but see the Notes for the special 2024-12-18T01:36:46.8963339Z handling of a scalar). An exception is raised at shape mismatch or 2024-12-18T01:36:46.8963888Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-12-18T01:36:46.8964456Z are compared like numbers, no assertion is raised if both objects have 2024-12-18T01:36:46.8964903Z NaNs in the same positions. 2024-12-18T01:36:46.8965092Z 2024-12-18T01:36:46.8965346Z The usual caution for verifying equality with floating point numbers is 2024-12-18T01:36:46.8965786Z advised. 2024-12-18T01:36:46.8965925Z 2024-12-18T01:36:46.8966022Z Parameters 2024-12-18T01:36:46.8966260Z ---------- 2024-12-18T01:36:46.8966492Z x : array_like 2024-12-18T01:36:46.8966735Z The actual object to check. 2024-12-18T01:36:46.8967028Z y : array_like 2024-12-18T01:36:46.8967287Z The desired, expected object. 2024-12-18T01:36:46.8967597Z err_msg : str, optional 2024-12-18T01:36:46.8967925Z The error message to be printed in case of failure. 2024-12-18T01:36:46.8968290Z verbose : bool, optional 2024-12-18T01:36:46.8968681Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:46.8969108Z strict : bool, optional 2024-12-18T01:36:46.8969480Z If True, raise an AssertionError when either the shape or the data 2024-12-18T01:36:46.8969980Z type of the array_like objects does not match. The special 2024-12-18T01:36:46.8970480Z handling for scalars mentioned in the Notes section is disabled. 2024-12-18T01:36:46.8970796Z 2024-12-18T01:36:46.8970887Z Raises 2024-12-18T01:36:46.8971108Z ------ 2024-12-18T01:36:46.8971343Z AssertionError 2024-12-18T01:36:46.8971634Z If actual and desired objects are not equal. 2024-12-18T01:36:46.8971881Z 2024-12-18T01:36:46.8971989Z See Also 2024-12-18T01:36:46.8972201Z -------- 2024-12-18T01:36:46.8972575Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:46.8973064Z relative and/or absolute precision. 2024-12-18T01:36:46.8973518Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:46.8973837Z 2024-12-18T01:36:46.8973940Z Notes 2024-12-18T01:36:46.8974145Z ----- 2024-12-18T01:36:46.8974461Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-12-18T01:36:46.8974997Z function checks that each element of the array_like object is equal to 2024-12-18T01:36:46.8975568Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-12-18T01:36:46.8975933Z 2024-12-18T01:36:46.8976041Z Examples 2024-12-18T01:36:46.8976259Z -------- 2024-12-18T01:36:46.8976527Z The first assert does not raise an exception: 2024-12-18T01:36:46.8976783Z 2024-12-18T01:36:46.8976940Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-12-18T01:36:46.8977347Z ... [np.exp(0),2.33333, np.nan]) 2024-12-18T01:36:46.8977588Z 2024-12-18T01:36:46.8977835Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-12-18T01:36:46.8978296Z functions for these cases instead: 2024-12-18T01:36:46.8978509Z 2024-12-18T01:36:46.8978656Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-12-18T01:36:46.8979049Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-12-18T01:36:46.8979421Z ... rtol=1e-10, atol=0) 2024-12-18T01:36:46.8979676Z 2024-12-18T01:36:46.8979912Z As mentioned in the Notes section, `assert_array_equal` has special 2024-12-18T01:36:46.8980758Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-12-18T01:36:46.8981165Z 2024-12-18T01:36:46.8981315Z >>> x = np.full((2, 5), fill_value=3) 2024-12-18T01:36:46.8981731Z >>> np.testing.assert_array_equal(x, 3) 2024-12-18T01:36:46.8982043Z 2024-12-18T01:36:46.8982323Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-12-18T01:36:46.8982758Z array: 2024-12-18T01:36:46.8982880Z 2024-12-18T01:36:46.8983050Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-12-18T01:36:46.8983437Z Traceback (most recent call last): 2024-12-18T01:36:46.8983739Z ... 2024-12-18T01:36:46.8983968Z AssertionError: 2024-12-18T01:36:46.8984225Z Arrays are not equal 2024-12-18T01:36:46.8984484Z 2024-12-18T01:36:46.8984721Z (shapes (2, 5), () mismatch) 2024-12-18T01:36:46.8985004Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-12-18T01:36:46.8985312Z [3, 3, 3, 3, 3]]) 2024-12-18T01:36:46.8985582Z y: torch.ndarray(3) 2024-12-18T01:36:46.8985793Z 2024-12-18T01:36:46.8986032Z The `strict` parameter also ensures that the array data types match: 2024-12-18T01:36:46.8986356Z 2024-12-18T01:36:46.8986473Z >>> x = np.array([2, 2, 2]) 2024-12-18T01:36:46.8986772Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-12-18T01:36:46.8987171Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-12-18T01:36:46.8987558Z Traceback (most recent call last): 2024-12-18T01:36:46.8987864Z ... 2024-12-18T01:36:46.8988097Z AssertionError: 2024-12-18T01:36:46.8988344Z Arrays are not equal 2024-12-18T01:36:46.8988607Z 2024-12-18T01:36:46.8988907Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-12-18T01:36:46.8989279Z x: torch.ndarray([2, 2, 2]) 2024-12-18T01:36:46.8989578Z y: torch.ndarray([2., 2., 2.]) 2024-12-18T01:36:46.8989764Z 2024-12-18T01:36:46.8990018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:46.8990395Z 2024-12-18T01:36:46.8990965Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=840. 2024-12-18T01:36:46.8991909Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:46.8992295Z 2024-12-18T01:36:46.8992510Z Raises an AssertionError if two objects are not equal up to desired 2024-12-18T01:36:46.8992941Z precision. 2024-12-18T01:36:46.8993073Z 2024-12-18T01:36:46.8993272Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:36:46.8993746Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:36:46.8994227Z instead of this function for more consistent floating point 2024-12-18T01:36:46.8994637Z comparisons. 2024-12-18T01:36:46.8994814Z 2024-12-18T01:36:46.8995056Z The test verifies identical shapes and that the elements of ``actual`` and 2024-12-18T01:36:46.8995518Z ``desired`` satisfy. 2024-12-18T01:36:46.8995764Z 2024-12-18T01:36:46.8995921Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-12-18T01:36:46.8996207Z 2024-12-18T01:36:46.8996448Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:36:46.8997025Z actual implementation did up to rounding vagaries. An exception is raised 2024-12-18T01:36:46.8997634Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-12-18T01:36:46.8998432Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-12-18T01:36:46.8998906Z objects have NaNs in the same positions. 2024-12-18T01:36:46.8999134Z 2024-12-18T01:36:46.8999253Z Parameters 2024-12-18T01:36:46.8999473Z ---------- 2024-12-18T01:36:46.8999714Z x : array_like 2024-12-18T01:36:46.8999973Z The actual object to check. 2024-12-18T01:36:46.9000267Z y : array_like 2024-12-18T01:36:46.9000531Z The desired, expected object. 2024-12-18T01:36:46.9000919Z decimal : int, optional 2024-12-18T01:36:46.9001212Z Desired precision, default is 6. 2024-12-18T01:36:46.9001546Z err_msg : str, optional 2024-12-18T01:36:46.9001884Z The error message to be printed in case of failure. 2024-12-18T01:36:46.9002268Z verbose : bool, optional 2024-12-18T01:36:46.9002646Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:46.9002976Z 2024-12-18T01:36:46.9003067Z Raises 2024-12-18T01:36:46.9003283Z ------ 2024-12-18T01:36:46.9003554Z AssertionError 2024-12-18T01:36:46.9003897Z If actual and desired are not equal up to specified precision. 2024-12-18T01:36:46.9004197Z 2024-12-18T01:36:46.9004291Z See Also 2024-12-18T01:36:46.9004513Z -------- 2024-12-18T01:36:46.9004880Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:46.9005371Z relative and/or absolute precision. 2024-12-18T01:36:46.9005829Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:46.9006152Z 2024-12-18T01:36:46.9006245Z Examples 2024-12-18T01:36:46.9006478Z -------- 2024-12-18T01:36:46.9006782Z the first assert does not raise an exception 2024-12-18T01:36:46.9007025Z 2024-12-18T01:36:46.9007214Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-12-18T01:36:46.9007628Z ... [1.0,2.333,np.nan]) 2024-12-18T01:36:46.9007860Z 2024-12-18T01:36:46.9008054Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:36:46.9008475Z ... [1.0,2.33339,np.nan], decimal=5) 2024-12-18T01:36:46.9008846Z Traceback (most recent call last): 2024-12-18T01:36:46.9009151Z ... 2024-12-18T01:36:46.9009382Z AssertionError: 2024-12-18T01:36:46.9009663Z Arrays are not almost equal to 5 decimals 2024-12-18T01:36:46.9009981Z 2024-12-18T01:36:46.9010230Z Mismatched elements: 1 / 3 (33.3%) 2024-12-18T01:36:46.9010572Z Max absolute difference: 5.999999999994898e-05 2024-12-18T01:36:46.9010954Z Max relative difference: 2.5713661239633743e-05 2024-12-18T01:36:46.9011372Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:36:46.9011810Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-12-18T01:36:46.9012092Z 2024-12-18T01:36:46.9012272Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:36:46.9012701Z ... [1.0,2.33333, 5], decimal=5) 2024-12-18T01:36:46.9013064Z Traceback (most recent call last): 2024-12-18T01:36:46.9013366Z ... 2024-12-18T01:36:46.9013581Z AssertionError: 2024-12-18T01:36:46.9013859Z Arrays are not almost equal to 5 decimals 2024-12-18T01:36:46.9014187Z 2024-12-18T01:36:46.9014451Z x and y nan location mismatch: 2024-12-18T01:36:46.9014877Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:36:46.9015300Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-12-18T01:36:46.9015584Z 2024-12-18T01:36:46.9015591Z 2024-12-18T01:36:46.9015841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:46.9016219Z 2024-12-18T01:36:46.9016810Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-12-18T01:36:46.9017813Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:46.9018407Z Context manager that resets warning registry for catching warnings 2024-12-18T01:36:46.9018743Z 2024-12-18T01:36:46.9018986Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-12-18T01:36:46.9019578Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-12-18T01:36:46.9020160Z it impossible to retrigger the warning in this module, whatever you put in 2024-12-18T01:36:46.9020744Z the warnings filters. This context manager accepts a sequence of `modules` 2024-12-18T01:36:46.9021279Z as a keyword argument to its constructor and: 2024-12-18T01:36:46.9021540Z 2024-12-18T01:36:46.9021769Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-12-18T01:36:46.9022208Z on entry; 2024-12-18T01:36:46.9022545Z * resets ``__warningregistry__`` to its previous state on exit. 2024-12-18T01:36:46.9022843Z 2024-12-18T01:36:46.9023078Z This makes it possible to trigger any warning afresh inside the context 2024-12-18T01:36:46.9023622Z manager without disturbing the state of warnings outside. 2024-12-18T01:36:46.9023932Z 2024-12-18T01:36:46.9024168Z For compatibility with Python 3.0, please consider all arguments to be 2024-12-18T01:36:46.9024621Z keyword-only. 2024-12-18T01:36:46.9024781Z 2024-12-18T01:36:46.9024881Z Parameters 2024-12-18T01:36:46.9025126Z ---------- 2024-12-18T01:36:46.9025377Z record : bool, optional 2024-12-18T01:36:46.9025745Z Specifies whether warnings should be captured by a custom 2024-12-18T01:36:46.9026289Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-12-18T01:36:46.9026887Z returned by the context manager. Otherwise None is returned by the 2024-12-18T01:36:46.9027455Z context manager. The objects appended to the list are arguments whose 2024-12-18T01:36:46.9027982Z attributes mirror the arguments to ``showwarning()``. 2024-12-18T01:36:46.9028368Z modules : sequence, optional 2024-12-18T01:36:46.9028801Z Sequence of modules for which to reset warnings registry on entry and 2024-12-18T01:36:46.9029344Z restore on exit. To work correctly, all 'ignore' filters should 2024-12-18T01:36:46.9029777Z filter by one of these modules. 2024-12-18T01:36:46.9029992Z 2024-12-18T01:36:46.9030105Z Examples 2024-12-18T01:36:46.9030334Z -------- 2024-12-18T01:36:46.9030582Z >>> import warnings 2024-12-18T01:36:46.9030949Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-12-18T01:36:46.9031403Z ... modules=[np.core.fromnumeric]): 2024-12-18T01:36:46.9031782Z ... warnings.simplefilter('always') 2024-12-18T01:36:46.9032257Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-12-18T01:36:46.9032768Z ... # do something that raises a warning but ignore those in 2024-12-18T01:36:46.9033175Z ... # np.core.fromnumeric 2024-12-18T01:36:46.9033479Z 2024-12-18T01:36:46.9033858Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:46.9034224Z 2024-12-18T01:36:47.0858580Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2024-12-18T01:36:47.0859538Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.0860129Z Applies a 1D convolution over a quantized input signal composed of 2024-12-18T01:36:47.0860585Z several quantized input planes. 2024-12-18T01:36:47.0860855Z 2024-12-18T01:36:47.0861075Z For details on input arguments, parameters, and implementation see 2024-12-18T01:36:47.0861539Z :class:`~torch.nn.Conv1d`. 2024-12-18T01:36:47.0861915Z 2024-12-18T01:36:47.0862062Z .. note:: 2024-12-18T01:36:47.0862418Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-12-18T01:36:47.0862735Z 2024-12-18T01:36:47.0862829Z .. note:: 2024-12-18T01:36:47.0863156Z Only `torch.quint8` is supported for the input data type. 2024-12-18T01:36:47.0863464Z 2024-12-18T01:36:47.0863468Z 2024-12-18T01:36:47.0863566Z Attributes: 2024-12-18T01:36:47.0863936Z weight (Tensor): packed tensor derived from the learnable weight 2024-12-18T01:36:47.0864381Z parameter. 2024-12-18T01:36:47.0864742Z scale (Tensor): scalar for the output scale 2024-12-18T01:36:47.0865161Z zero_point (Tensor): scalar for the output zero point 2024-12-18T01:36:47.0865445Z 2024-12-18T01:36:47.0865659Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-12-18T01:36:47.0865931Z 2024-12-18T01:36:47.0866033Z Examples:: 2024-12-18T01:36:47.0866189Z 2024-12-18T01:36:47.0866345Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-12-18T01:36:47.0866749Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-12-18T01:36:47.0867122Z >>> input = torch.randn(20, 16, 100) 2024-12-18T01:36:47.0867458Z >>> # quantize input to quint8 2024-12-18T01:36:47.0867838Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.0868252Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-12-18T01:36:47.0868722Z ... dtype=torch.quint8) 2024-12-18T01:36:47.0869078Z >>> output = m(q_input) 2024-12-18T01:36:47.0869267Z 2024-12-18T01:36:47.0869358Z 2024-12-18T01:36:47.0869735Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.0870109Z 2024-12-18T01:36:47.1085093Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-12-18T01:36:47.1086165Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.1086679Z A quantized long short-term memory (LSTM). 2024-12-18T01:36:47.1086912Z 2024-12-18T01:36:47.1087203Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-12-18T01:36:47.1087592Z 2024-12-18T01:36:47.1087709Z Attributes: 2024-12-18T01:36:47.1087972Z layers : instances of the `_LSTMLayer` 2024-12-18T01:36:47.1088222Z 2024-12-18T01:36:47.1088325Z .. note:: 2024-12-18T01:36:47.1088688Z To access the weights and biases, you need to access them per layer. 2024-12-18T01:36:47.1089202Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-12-18T01:36:47.1089489Z 2024-12-18T01:36:47.1089605Z Examples:: 2024-12-18T01:36:47.1089864Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.1090164Z >>> custom_module_config = { 2024-12-18T01:36:47.1090532Z ... 'float_to_observed_custom_module_class': { 2024-12-18T01:36:47.1090926Z ... nn.LSTM: nn.quantizable.LSTM, 2024-12-18T01:36:47.1091263Z ... }, 2024-12-18T01:36:47.1091565Z ... 'observed_to_quantized_custom_module_class': { 2024-12-18T01:36:47.1091973Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-12-18T01:36:47.1092328Z ... } 2024-12-18T01:36:47.1092572Z ... } 2024-12-18T01:36:47.1092931Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-12-18T01:36:47.1093493Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-12-18T01:36:47.1093904Z 2024-12-18T01:36:47.1094282Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.1094657Z 2024-12-18T01:36:47.2193351Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-12-18T01:36:47.2194445Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.2195184Z Squashes the sparse masks into the appropriate tensors. 2024-12-18T01:36:47.2195467Z 2024-12-18T01:36:47.2195810Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-12-18T01:36:47.2196321Z the module will have a `sparse_params` dict attached to it. 2024-12-18T01:36:47.2196631Z 2024-12-18T01:36:47.2196725Z Args: 2024-12-18T01:36:47.2197054Z params_to_keep: List of keys to save in the module or a dict 2024-12-18T01:36:47.2197527Z representing the modules and keys that will have 2024-12-18T01:36:47.2198093Z sparsity parameters saved 2024-12-18T01:36:47.2198541Z params_to_keep_per_layer: Dict to specify the params that should be 2024-12-18T01:36:47.2199105Z saved for specific layers. The keys in the dict 2024-12-18T01:36:47.2199574Z should be the module fqn, while the values should 2024-12-18T01:36:47.2200022Z be a list of strings with the names of the variables 2024-12-18T01:36:47.2200430Z to save in the `sparse_params` 2024-12-18T01:36:47.2200668Z 2024-12-18T01:36:47.2200778Z Examples: 2024-12-18T01:36:47.2201119Z >>> # xdoctest: +SKIP("locals are undefined") 2024-12-18T01:36:47.2201478Z >>> # Don't save any sparse params 2024-12-18T01:36:47.2201827Z >>> sparsifier.squash_mask() 2024-12-18T01:36:47.2202198Z >>> hasattr(model.submodule1, 'sparse_params') 2024-12-18T01:36:47.2202547Z False 2024-12-18T01:36:47.2202689Z 2024-12-18T01:36:47.2202827Z >>> # Keep sparse params per layer 2024-12-18T01:36:47.2203162Z >>> sparsifier.squash_mask( 2024-12-18T01:36:47.2203508Z ... params_to_keep_per_layer={ 2024-12-18T01:36:47.2203919Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-12-18T01:36:47.2204306Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:36:47.2204644Z ... }) 2024-12-18T01:36:47.2204968Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:36:47.2205333Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:47.2205697Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:36:47.2206068Z {'baz': 0.1} 2024-12-18T01:36:47.2206228Z 2024-12-18T01:36:47.2206371Z >>> # Keep sparse params for all layers 2024-12-18T01:36:47.2206785Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-12-18T01:36:47.2207229Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:36:47.2207605Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:47.2207970Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:36:47.2208347Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:47.2208537Z 2024-12-18T01:36:47.2208749Z >>> # Keep some sparse params for all layers, and specific ones for 2024-12-18T01:36:47.2209151Z >>> # some other layers 2024-12-18T01:36:47.2209470Z >>> sparsifier.squash_mask( 2024-12-18T01:36:47.2209811Z ... params_to_keep=('foo', 'bar'), 2024-12-18T01:36:47.2210169Z ... params_to_keep_per_layer={ 2024-12-18T01:36:47.2210526Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:36:47.2210862Z ... }) 2024-12-18T01:36:47.2211169Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:36:47.2211542Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:47.2211900Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:36:47.2212288Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-12-18T01:36:47.2212602Z 2024-12-18T01:36:47.2212971Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.2213344Z 2024-12-18T01:36:47.3106593Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-12-18T01:36:47.3107645Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.3108032Z 2024-12-18T01:36:47.3108298Z Config object that specifies the supported data types passed as arguments to 2024-12-18T01:36:47.3108900Z quantize ops in the reference model spec, for input and output activations, 2024-12-18T01:36:47.3109355Z weights, and biases. 2024-12-18T01:36:47.3109509Z 2024-12-18T01:36:47.3109680Z For example, consider the following reference model: 2024-12-18T01:36:47.3109944Z 2024-12-18T01:36:47.3110108Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-12-18T01:36:47.3110545Z 2024-12-18T01:36:47.3110757Z The pattern in the square brackets refers to the reference pattern of 2024-12-18T01:36:47.3111344Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-12-18T01:36:47.3111929Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-12-18T01:36:47.3112495Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-12-18T01:36:47.3113052Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-12-18T01:36:47.3113556Z the second quantize op (quant2). 2024-12-18T01:36:47.3113753Z 2024-12-18T01:36:47.3113967Z Note that the dtype here does not refer to the interface dtypes of the 2024-12-18T01:36:47.3114502Z op. For example, the "input dtype" here is not the dtype of the input 2024-12-18T01:36:47.3115032Z tensor passed to the quantized linear op. Though it can still be the 2024-12-18T01:36:47.3115554Z same as the interface dtype, this is not always the case, e.g. the 2024-12-18T01:36:47.3116156Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-12-18T01:36:47.3116705Z specified in the DTypeConfig would still be quint8. The semantics of 2024-12-18T01:36:47.3117283Z dtypes here are the same as the semantics of the dtypes specified in 2024-12-18T01:36:47.3117710Z the observers. 2024-12-18T01:36:47.3117859Z 2024-12-18T01:36:47.3118062Z These dtypes are matched against the ones specified in the user's 2024-12-18T01:36:47.3118596Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-12-18T01:36:47.3119147Z specified in the DTypeConfig (if any), then we will quantize the given 2024-12-18T01:36:47.3119697Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-12-18T01:36:47.3120147Z the pattern will not be quantized. 2024-12-18T01:36:47.3120370Z 2024-12-18T01:36:47.3120494Z Example usage:: 2024-12-18T01:36:47.3120650Z 2024-12-18T01:36:47.3120757Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:36:47.3121072Z >>> dtype_config1 = DTypeConfig( 2024-12-18T01:36:47.3121402Z ... input_dtype=torch.quint8, 2024-12-18T01:36:47.3121715Z ... output_dtype=torch.quint8, 2024-12-18T01:36:47.3122042Z ... weight_dtype=torch.qint8, 2024-12-18T01:36:47.3122362Z ... bias_dtype=torch.float) 2024-12-18T01:36:47.3122560Z 2024-12-18T01:36:47.3122680Z >>> dtype_config2 = DTypeConfig( 2024-12-18T01:36:47.3123026Z ... input_dtype=DTypeWithConstraints( 2024-12-18T01:36:47.3123362Z ... dtype=torch.quint8, 2024-12-18T01:36:47.3123677Z ... quant_min_lower_bound=0, 2024-12-18T01:36:47.3124003Z ... quant_max_upper_bound=255, 2024-12-18T01:36:47.3124311Z ... ), 2024-12-18T01:36:47.3124581Z ... output_dtype=DTypeWithConstraints( 2024-12-18T01:36:47.3124912Z ... dtype=torch.quint8, 2024-12-18T01:36:47.3125225Z ... quant_min_lower_bound=0, 2024-12-18T01:36:47.3125550Z ... quant_max_upper_bound=255, 2024-12-18T01:36:47.3125859Z ... ), 2024-12-18T01:36:47.3126132Z ... weight_dtype=DTypeWithConstraints( 2024-12-18T01:36:47.3126464Z ... dtype=torch.qint8, 2024-12-18T01:36:47.3126785Z ... quant_min_lower_bound=-128, 2024-12-18T01:36:47.3127183Z ... quant_max_upper_bound=127, 2024-12-18T01:36:47.3127495Z ... ), 2024-12-18T01:36:47.3127742Z ... bias_dtype=torch.float) 2024-12-18T01:36:47.3127941Z 2024-12-18T01:36:47.3128052Z >>> dtype_config1.input_dtype 2024-12-18T01:36:47.3128355Z torch.quint8 2024-12-18T01:36:47.3128510Z 2024-12-18T01:36:47.3128620Z >>> dtype_config2.input_dtype 2024-12-18T01:36:47.3128913Z torch.quint8 2024-12-18T01:36:47.3129052Z 2024-12-18T01:36:47.3129201Z >>> dtype_config2.input_dtype_with_constraints 2024-12-18T01:36:47.3129964Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-12-18T01:36:47.3130609Z 2024-12-18T01:36:47.3130859Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.3131267Z 2024-12-18T01:36:47.4334885Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-12-18T01:36:47.4337304Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.4338040Z 2024-12-18T01:36:47.4339194Z Takes in optional filter values and generates two tables with desired information. 2024-12-18T01:36:47.4339962Z 2024-12-18T01:36:47.4340346Z The generated tables are presented in both a list-of-lists format 2024-12-18T01:36:47.4340975Z 2024-12-18T01:36:47.4341355Z The reason for the two tables are that they handle different things: 2024-12-18T01:36:47.4342178Z 1.) the first table handles all tensor level information 2024-12-18T01:36:47.4343048Z 2.) the second table handles and displays all channel based information 2024-12-18T01:36:47.4343700Z 2024-12-18T01:36:47.4344450Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:36:47.4345911Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:36:47.4347316Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:36:47.4348187Z 2024-12-18T01:36:47.4348353Z Tensor table columns: 2024-12-18T01:36:47.4348960Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:47.4349463Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:36:47.4349732Z 2024-12-18T01:36:47.4349863Z Per-Channel table columns: 2024-12-18T01:36:47.4350262Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:47.4350823Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:36:47.4351109Z 2024-12-18T01:36:47.4351215Z Args: 2024-12-18T01:36:47.4351611Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:36:47.4352103Z contain this filter substring 2024-12-18T01:36:47.4352485Z Default = "", results in all the features being printed 2024-12-18T01:36:47.4353023Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:47.4353651Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:47.4354005Z 2024-12-18T01:36:47.4354132Z Returns a dictionary with two keys: 2024-12-18T01:36:47.4354508Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-12-18T01:36:47.4354914Z "tensor_level_info", "channel_level_info" 2024-12-18T01:36:47.4355263Z Each key maps to a tuple with: 2024-12-18T01:36:47.4355604Z A list of the headers of each table 2024-12-18T01:36:47.4356161Z A list of lists containing the table information row by row 2024-12-18T01:36:47.4356976Z The 0th index row will contain the headers of the columns 2024-12-18T01:36:47.4357833Z The rest of the rows will contain data 2024-12-18T01:36:47.4358270Z 2024-12-18T01:36:47.4358429Z Example Use: 2024-12-18T01:36:47.4358884Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:47.4359579Z >>> mod_report_visualizer.generate_filtered_tables( 2024-12-18T01:36:47.4360284Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:47.4360890Z ... module_fqn_filter = "block1" 2024-12-18T01:36:47.4361763Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-12-18T01:36:47.4362497Z 2024-12-18T01:36:47.4362961Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.4363659Z 2024-12-18T01:36:47.4365355Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-12-18T01:36:47.4367940Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.4368655Z 2024-12-18T01:36:47.4369169Z Takes in optional filter values and prints out formatted tables of the information. 2024-12-18T01:36:47.4369897Z 2024-12-18T01:36:47.4370607Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-12-18T01:36:47.4371765Z 1.) the first table handles all tensor level information 2024-12-18T01:36:47.4372671Z 2.) the second table handles and displays all channel based information 2024-12-18T01:36:47.4373314Z 2024-12-18T01:36:47.4373923Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:36:47.4375378Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:36:47.4376869Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:36:47.4377826Z 2024-12-18T01:36:47.4378035Z Tensor table columns: 2024-12-18T01:36:47.4378660Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:47.4379443Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:36:47.4379924Z 2024-12-18T01:36:47.4380119Z Per-Channel table columns: 2024-12-18T01:36:47.4380468Z 2024-12-18T01:36:47.4380876Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:47.4381744Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:36:47.4382205Z 2024-12-18T01:36:47.4382366Z Args: 2024-12-18T01:36:47.4383079Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:36:47.4383975Z contain this filter substring 2024-12-18T01:36:47.4384670Z Default = "", results in all the features being printed 2024-12-18T01:36:47.4385673Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:47.4386808Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:47.4387479Z 2024-12-18T01:36:47.4387654Z Example Use: 2024-12-18T01:36:47.4388106Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:47.4388851Z >>> mod_report_visualizer.generate_table_visualization( 2024-12-18T01:36:47.4389607Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:47.4390240Z ... module_fqn_filter = "block1" 2024-12-18T01:36:47.4390795Z ... ) 2024-12-18T01:36:47.4391352Z >>> # prints out neatly formatted table with per_channel_min info 2024-12-18T01:36:47.4392138Z >>> # for all modules in block 1 of the model 2024-12-18T01:36:47.4392587Z 2024-12-18T01:36:47.4393060Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.4393776Z 2024-12-18T01:36:47.4395514Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566. 2024-12-18T01:36:47.4397788Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.4398425Z 2024-12-18T01:36:47.4398675Z Takes in a feature and optional module_filter and plots of the desired data. 2024-12-18T01:36:47.4399030Z 2024-12-18T01:36:47.4399310Z For per channel features, it averages the value across the channels and plots a point 2024-12-18T01:36:47.4399958Z per module. The reason for this is that for models with hundreds of channels, it can 2024-12-18T01:36:47.4400593Z be hard to differentiate one channel line from another, and so the point of generating 2024-12-18T01:36:47.4401497Z a single average point per module is to give a sense of general trends that encourage 2024-12-18T01:36:47.4402099Z further deep dives. 2024-12-18T01:36:47.4402248Z 2024-12-18T01:36:47.4402350Z Note: 2024-12-18T01:36:47.4402737Z Only features in the report that have tensor value data are plottable by this class 2024-12-18T01:36:47.4403545Z When the tensor information is plotted, it will plot: 2024-12-18T01:36:47.4404243Z idx as the x val, feature value as the y_val 2024-12-18T01:36:47.4404981Z When the channel information is plotted, it will plot: 2024-12-18T01:36:47.4406070Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-12-18T01:36:47.4407211Z The reason for this is that we want to be able to compare values across the 2024-12-18T01:36:47.4408265Z channels for same layer, and it will be hard if values are staggered by idx 2024-12-18T01:36:47.4409243Z This means each module is represented by only 1 x value 2024-12-18T01:36:47.4409906Z Args: 2024-12-18T01:36:47.4410524Z feature_filter (str): Filters the features presented to only those that 2024-12-18T01:36:47.4411377Z contain this filter substring 2024-12-18T01:36:47.4412364Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:47.4413503Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:47.4414125Z 2024-12-18T01:36:47.4414252Z Example Use: 2024-12-18T01:36:47.4414530Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:47.4414955Z >>> mod_report_visualizer.generate_plot_visualization( 2024-12-18T01:36:47.4415364Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:47.4415719Z ... module_fqn_filter = "block1" 2024-12-18T01:36:47.4416018Z ... ) 2024-12-18T01:36:47.4416331Z >>> # outputs line plot of per_channel_min information for all 2024-12-18T01:36:47.4416816Z >>> # modules in block1 of model each channel gets it's own line, 2024-12-18T01:36:47.4417296Z >>> # and it's plotted across the in-order modules on the x-axis 2024-12-18T01:36:47.4417585Z 2024-12-18T01:36:47.4417851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.4418217Z 2024-12-18T01:36:47.4419132Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646. 2024-12-18T01:36:47.4420666Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.4421345Z 2024-12-18T01:36:47.4421852Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-12-18T01:36:47.4422582Z 2024-12-18T01:36:47.4422730Z Note: 2024-12-18T01:36:47.4423417Z Only features in the report that have tensor value data can be viewed as a histogram 2024-12-18T01:36:47.4424620Z If you want to plot a histogram from all the channel values of a specific feature for 2024-12-18T01:36:47.4425795Z a specific model, make sure to specify both the model and the feature properly 2024-12-18T01:36:47.4426951Z in the filters and you should be able to see a distribution of the channel data 2024-12-18T01:36:47.4427804Z 2024-12-18T01:36:47.4427958Z Args: 2024-12-18T01:36:47.4428658Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:36:47.4429561Z contain this filter substring 2024-12-18T01:36:47.4430263Z Default = "", results in all the features being printed 2024-12-18T01:36:47.4431281Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:47.4432451Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:47.4433452Z num_bins (int, optional): The number of bins to create the histogram with 2024-12-18T01:36:47.4434432Z Default = 10, the values will be split into 10 equal sized bins 2024-12-18T01:36:47.4435124Z 2024-12-18T01:36:47.4435286Z Example Use: 2024-12-18T01:36:47.4435790Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.4436637Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-12-18T01:36:47.4437605Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:47.4438230Z ... module_fqn_filter = "block1" 2024-12-18T01:36:47.4438775Z ... ) 2024-12-18T01:36:47.4439596Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-12-18T01:36:47.4440830Z information is gathered across all channels for all modules in block 1 for the 2024-12-18T01:36:47.4441941Z per_channel_min and is displayed in a histogram of equally sized bins 2024-12-18T01:36:47.4442598Z 2024-12-18T01:36:47.4443067Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.4443789Z 2024-12-18T01:36:47.7151740Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=660. 2024-12-18T01:36:47.7153972Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:47.7154677Z 2024-12-18T01:36:47.7155136Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-12-18T01:36:47.7156458Z The submesh created consists of the dimensions and the communicators indicated by 2024-12-18T01:36:47.7157354Z ``mesh_dim_names`` 2024-12-18T01:36:47.7157591Z 2024-12-18T01:36:47.7157763Z Args: 2024-12-18T01:36:47.7158378Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-12-18T01:36:47.7159328Z mesh dimension of the DeviceMesh to create the submesh for. 2024-12-18T01:36:47.7160033Z Returns: 2024-12-18T01:36:47.7160439Z A :class:`DeviceMesh` object 2024-12-18T01:36:47.7160782Z 2024-12-18T01:36:47.7161328Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-12-18T01:36:47.7162284Z In the first example: 2024-12-18T01:36:47.7163046Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-12-18T01:36:47.7164220Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-12-18T01:36:47.7165342Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-12-18T01:36:47.7166348Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-12-18T01:36:47.7167408Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-12-18T01:36:47.7168476Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-12-18T01:36:47.7169076Z 2024-12-18T01:36:47.7169249Z In the second example: 2024-12-18T01:36:47.7169997Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-12-18T01:36:47.7171191Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-12-18T01:36:47.7172341Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-12-18T01:36:47.7173168Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-12-18T01:36:47.7173542Z 2024-12-18T01:36:47.7173677Z Example:: 2024-12-18T01:36:47.7173907Z >>> # xdoctest: +SKIP("no rank") 2024-12-18T01:36:47.7174300Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-12-18T01:36:47.7174675Z >>> 2024-12-18T01:36:47.7175002Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-12-18T01:36:47.7175466Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-12-18T01:36:47.7175956Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:36:47.7176431Z >>> tp_mesh = mesh_2d["tp"] 2024-12-18T01:36:47.7176724Z >>> dp_mesh = mesh_2d["dp"] 2024-12-18T01:36:47.7177057Z >>> 2024-12-18T01:36:47.7177283Z >>> # Initialize a 3D mesh. 2024-12-18T01:36:47.7177742Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-12-18T01:36:47.7178432Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-12-18T01:36:47.7178959Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-12-18T01:36:47.7179290Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-12-18T01:36:47.7179565Z 2024-12-18T01:36:47.7180230Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-12-18T01:36:47.7180982Z 2024-12-18T01:36:47.7181235Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:36:47.7181716Z ^ 2024-12-18T01:36:47.7519815Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3063. 2024-12-18T01:36:47.7521738Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.7522487Z 2024-12-18T01:36:47.7522909Z Gathers picklable objects from the whole group in a single process. 2024-12-18T01:36:47.7523554Z 2024-12-18T01:36:47.7524003Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-12-18T01:36:47.7524939Z object must be picklable in order to be gathered. 2024-12-18T01:36:47.7525397Z 2024-12-18T01:36:47.7525554Z Args: 2024-12-18T01:36:47.7525987Z obj (Any): Input object. Must be picklable. 2024-12-18T01:36:47.7526796Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-12-18T01:36:47.7527736Z should be correctly sized as the size of the group for this 2024-12-18T01:36:47.7528697Z collective and will contain the output. Must be ``None`` on non-dst 2024-12-18T01:36:47.7529497Z ranks. (default is ``None``) 2024-12-18T01:36:47.7530501Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2024-12-18T01:36:47.7531693Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2024-12-18T01:36:47.7532700Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:36:47.7533717Z the default process group will be used. Default is ``None``. 2024-12-18T01:36:47.7534968Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2024-12-18T01:36:47.7535802Z 2024-12-18T01:36:47.7535963Z Returns: 2024-12-18T01:36:47.7536505Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-12-18T01:36:47.7537268Z output of the collective. 2024-12-18T01:36:47.7537623Z 2024-12-18T01:36:47.7538054Z .. note:: Note that this API differs slightly from the gather collective 2024-12-18T01:36:47.7539090Z since it does not provide an async_op handle and thus will be a blocking 2024-12-18T01:36:47.7540071Z call. 2024-12-18T01:36:47.7540291Z 2024-12-18T01:36:47.7540721Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-12-18T01:36:47.7541741Z of objects must be moved to the GPU device before communication takes 2024-12-18T01:36:47.7542609Z place. In this case, the device used is given by 2024-12-18T01:36:47.7543492Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-12-18T01:36:47.7544471Z ensure that this is set so that each rank has an individual GPU, via 2024-12-18T01:36:47.7545279Z ``torch.cuda.set_device()``. 2024-12-18T01:36:47.7545650Z 2024-12-18T01:36:47.7545834Z .. warning:: 2024-12-18T01:36:47.7546418Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-12-18T01:36:47.7547419Z known to be insecure. It is possible to construct malicious pickle data 2024-12-18T01:36:47.7548601Z which will execute arbitrary code during unpickling. Only call this 2024-12-18T01:36:47.7549450Z function with data you trust. 2024-12-18T01:36:47.7549852Z 2024-12-18T01:36:47.7550020Z .. warning:: 2024-12-18T01:36:47.7550651Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-12-18T01:36:47.7551653Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-12-18T01:36:47.7552726Z pickled. Please consider using :func:`gather` instead. 2024-12-18T01:36:47.7553282Z 2024-12-18T01:36:47.7553452Z Example:: 2024-12-18T01:36:47.7553923Z >>> # xdoctest: +SKIP("need process group init") 2024-12-18T01:36:47.7554713Z >>> # Note: Process group initialization omitted on each rank. 2024-12-18T01:36:47.7555476Z >>> import torch.distributed as dist 2024-12-18T01:36:47.7556140Z >>> # Assumes world_size of 3. 2024-12-18T01:36:47.7556816Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-12-18T01:36:47.7557576Z >>> output = [None for _ in gather_objects] 2024-12-18T01:36:47.7558193Z >>> dist.gather_object( 2024-12-18T01:36:47.7558822Z ... gather_objects[dist.get_rank()], 2024-12-18T01:36:47.7559453Z ... output if dist.get_rank() == 0 else None, 2024-12-18T01:36:47.7560068Z ... dst=0 2024-12-18T01:36:47.7560479Z ... ) 2024-12-18T01:36:47.7560841Z >>> # On rank 0 2024-12-18T01:36:47.7561262Z >>> output 2024-12-18T01:36:47.7561661Z ['foo', 12, {1: 2}] 2024-12-18T01:36:47.7561963Z 2024-12-18T01:36:47.7562445Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.7563152Z 2024-12-18T01:36:47.7724464Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2024-12-18T01:36:47.7725350Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.7725735Z 2024-12-18T01:36:47.7725872Z Module ``torch.distributed.launch``. 2024-12-18T01:36:47.7726092Z 2024-12-18T01:36:47.7726363Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-12-18T01:36:47.7726886Z training processes on each of the training nodes. 2024-12-18T01:36:47.7727147Z 2024-12-18T01:36:47.7727270Z .. warning:: 2024-12-18T01:36:47.7727400Z 2024-12-18T01:36:47.7727653Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-12-18T01:36:47.7728031Z 2024-12-18T01:36:47.7728273Z The utility can be used for single-node distributed training, in which one or 2024-12-18T01:36:47.7728864Z more processes per node will be spawned. The utility can be used for either 2024-12-18T01:36:47.7729468Z CPU training or GPU training. If the utility is used for GPU training, 2024-12-18T01:36:47.7730444Z each distributed process will be operating on a single GPU. This can achieve 2024-12-18T01:36:47.7731505Z well-improved single-node training performance. It can also be used in 2024-12-18T01:36:47.7732588Z multi-node distributed training, by spawning up multiple processes on each node 2024-12-18T01:36:47.7733265Z for well-improved multi-node distributed training performance as well. 2024-12-18T01:36:47.7733982Z This will especially be beneficial for systems with multiple Infiniband 2024-12-18T01:36:47.7734581Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-12-18T01:36:47.7735076Z aggregated communication bandwidth. 2024-12-18T01:36:47.7735288Z 2024-12-18T01:36:47.7735542Z In both cases of single-node distributed training or multi-node distributed 2024-12-18T01:36:47.7736110Z training, this utility will launch the given number of processes per node 2024-12-18T01:36:47.7736684Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-12-18T01:36:47.7737243Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-12-18T01:36:47.7737780Z and each process will be operating on a single GPU from *GPU 0 to 2024-12-18T01:36:47.7738303Z GPU (nproc_per_node - 1)*. 2024-12-18T01:36:47.7738598Z 2024-12-18T01:36:47.7738774Z **How to use this module:** 2024-12-18T01:36:47.7739092Z 2024-12-18T01:36:47.7739349Z 1. Single-Node multi-process distributed training 2024-12-18T01:36:47.7739833Z 2024-12-18T01:36:47.7739986Z :: 2024-12-18T01:36:47.7740198Z 2024-12-18T01:36:47.7740633Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:36:47.7741733Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-12-18T01:36:47.7742519Z arguments of your training script) 2024-12-18T01:36:47.7742933Z 2024-12-18T01:36:47.7743335Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-12-18T01:36:47.7743932Z 2024-12-18T01:36:47.7743939Z 2024-12-18T01:36:47.7744177Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-12-18T01:36:47.7744666Z 2024-12-18T01:36:47.7744887Z :: 2024-12-18T01:36:47.7745079Z 2024-12-18T01:36:47.7745516Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:36:47.7746580Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-12-18T01:36:47.7747382Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:36:47.7748226Z and all other arguments of your training script) 2024-12-18T01:36:47.7748686Z 2024-12-18T01:36:47.7748847Z Node 2: 2024-12-18T01:36:47.7749053Z 2024-12-18T01:36:47.7749221Z :: 2024-12-18T01:36:47.7749428Z 2024-12-18T01:36:47.7749862Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:36:47.7750805Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-12-18T01:36:47.7751679Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:36:47.7752558Z and all other arguments of your training script) 2024-12-18T01:36:47.7753044Z 2024-12-18T01:36:47.7753349Z 3. To look up what optional arguments this module offers: 2024-12-18T01:36:47.7753855Z 2024-12-18T01:36:47.7754021Z :: 2024-12-18T01:36:47.7754215Z 2024-12-18T01:36:47.7754472Z python -m torch.distributed.launch --help 2024-12-18T01:36:47.7754930Z 2024-12-18T01:36:47.7754935Z 2024-12-18T01:36:47.7755105Z **Important Notices:** 2024-12-18T01:36:47.7755416Z 2024-12-18T01:36:47.7755820Z 1. This utility and multi-process distributed (single-node or 2024-12-18T01:36:47.7756579Z multi-node) GPU training currently only achieves the best performance using 2024-12-18T01:36:47.7757196Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-12-18T01:36:47.7757663Z use for GPU training. 2024-12-18T01:36:47.7757827Z 2024-12-18T01:36:47.7758049Z 2. In your training program, you must parse the command-line argument: 2024-12-18T01:36:47.7758613Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-12-18T01:36:47.7772543Z If your training program uses GPUs, you should ensure that your code only 2024-12-18T01:36:47.7773697Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-12-18T01:36:47.7774283Z 2024-12-18T01:36:47.7774483Z Parsing the local_rank argument 2024-12-18T01:36:47.7774955Z 2024-12-18T01:36:47.7775131Z :: 2024-12-18T01:36:47.7775320Z 2024-12-18T01:36:47.7775499Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.7775965Z >>> import argparse 2024-12-18T01:36:47.7776489Z >>> parser = argparse.ArgumentParser() 2024-12-18T01:36:47.7777290Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-12-18T01:36:47.7778063Z >>> args = parser.parse_args() 2024-12-18T01:36:47.7778432Z 2024-12-18T01:36:47.7778664Z Set your device to local rank using either 2024-12-18T01:36:47.7779092Z 2024-12-18T01:36:47.7779260Z :: 2024-12-18T01:36:47.7779456Z 2024-12-18T01:36:47.7779820Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-12-18T01:36:47.7780435Z 2024-12-18T01:36:47.7780588Z or 2024-12-18T01:36:47.7780919Z 2024-12-18T01:36:47.7781072Z :: 2024-12-18T01:36:47.7781258Z 2024-12-18T01:36:47.7781512Z >>> with torch.cuda.device(args.local_rank): 2024-12-18T01:36:47.7782100Z >>> # your code to run 2024-12-18T01:36:47.7782577Z >>> ... 2024-12-18T01:36:47.7782817Z 2024-12-18T01:36:47.7782983Z .. versionchanged:: 2.0.0 2024-12-18T01:36:47.7783312Z 2024-12-18T01:36:47.7783771Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-12-18T01:36:47.7784965Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-12-18T01:36:47.7785889Z previously used underscored ``--local_rank``. 2024-12-18T01:36:47.7786311Z 2024-12-18T01:36:47.7786773Z For backward compatibility, it may be necessary for users to handle both 2024-12-18T01:36:47.7787940Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-12-18T01:36:47.7789065Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-12-18T01:36:47.7790171Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-12-18T01:36:47.7791381Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-12-18T01:36:47.7792326Z including ``"--local-rank"`` should be sufficient. 2024-12-18T01:36:47.7792813Z 2024-12-18T01:36:47.7793268Z 3. In your training program, you are supposed to call the following function 2024-12-18T01:36:47.7794291Z at the beginning to start the distributed backend. It is strongly recommended 2024-12-18T01:36:47.7795319Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-12-18T01:36:47.7796046Z but ``env://`` is the one that is officially supported by this module. 2024-12-18T01:36:47.7796364Z 2024-12-18T01:36:47.7796467Z :: 2024-12-18T01:36:47.7796580Z 2024-12-18T01:36:47.7796800Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-12-18T01:36:47.7797274Z >>> init_method='env://') 2024-12-18T01:36:47.7797522Z 2024-12-18T01:36:47.7797766Z 4. In your training program, you can either use regular distributed functions 2024-12-18T01:36:47.7798637Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-12-18T01:36:47.7799207Z training program uses GPUs for training and you would like to use 2024-12-18T01:36:47.7799731Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-12-18T01:36:47.7800147Z here is how to configure it. 2024-12-18T01:36:47.7800329Z 2024-12-18T01:36:47.7800437Z :: 2024-12-18T01:36:47.7800551Z 2024-12-18T01:36:47.7800752Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-12-18T01:36:47.7801406Z >>> device_ids=[args.local_rank], 2024-12-18T01:36:47.7801822Z >>> output_device=args.local_rank) 2024-12-18T01:36:47.7802094Z 2024-12-18T01:36:47.7802334Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-12-18T01:36:47.7803179Z that your code will be operating on. This is generally the local rank of the 2024-12-18T01:36:47.7804259Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-12-18T01:36:47.7805471Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-12-18T01:36:47.7806186Z utility 2024-12-18T01:36:47.7806399Z 2024-12-18T01:36:47.7806806Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-12-18T01:36:47.7807878Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-12-18T01:36:47.7808907Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-12-18T01:36:47.7809887Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-12-18T01:36:47.7810771Z will not pass ``--local-rank`` when you specify this flag. 2024-12-18T01:36:47.7811315Z 2024-12-18T01:36:47.7811488Z .. warning:: 2024-12-18T01:36:47.7811737Z 2024-12-18T01:36:47.7812212Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-12-18T01:36:47.7813128Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-12-18T01:36:47.7813876Z write to a networked filesystem. See 2024-12-18T01:36:47.7814683Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-12-18T01:36:47.7815588Z how things can go wrong if you don't do this correctly. 2024-12-18T01:36:47.7816102Z 2024-12-18T01:36:47.7816110Z 2024-12-18T01:36:47.7816118Z 2024-12-18T01:36:47.7816215Z 2024-12-18T01:36:47.7816678Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.7817378Z 2024-12-18T01:36:47.8391278Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-12-18T01:36:47.8393299Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.8394051Z 2024-12-18T01:36:47.8394502Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-12-18T01:36:47.8395601Z Needs to be called on all ranks in an SPMD fashion. 2024-12-18T01:36:47.8396174Z 2024-12-18T01:36:47.8396342Z Args: 2024-12-18T01:36:47.8397048Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-12-18T01:36:47.8398254Z of shards that represent the local shards on this rank. 2024-12-18T01:36:47.8399195Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-12-18T01:36:47.8400060Z shape of the overall sharded tensor. 2024-12-18T01:36:47.8400455Z 2024-12-18T01:36:47.8400621Z Keyword args: 2024-12-18T01:36:47.8401605Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:36:47.8402538Z the default process group will be used. 2024-12-18T01:36:47.8403231Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:36:47.8404142Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:36:47.8404852Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:36:47.8405278Z Default: ``False``. 2024-12-18T01:36:47.8405458Z 2024-12-18T01:36:47.8405564Z Returns: 2024-12-18T01:36:47.8405833Z A :class:`ShardedTensor` object handle on this rank 2024-12-18T01:36:47.8406104Z 2024-12-18T01:36:47.8406109Z 2024-12-18T01:36:47.8406204Z Examples: 2024-12-18T01:36:47.8406589Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-12-18T01:36:47.8407143Z each shard have a (5, 5) local tensor, we can do it like below: 2024-12-18T01:36:47.8407436Z 2024-12-18T01:36:47.8407544Z on rank 0: 2024-12-18T01:36:47.8407808Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:36:47.8408148Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:36:47.8408489Z >>> shard_offsets=[0, 0], 2024-12-18T01:36:47.8408794Z >>> shard_lengths=[5, 5], 2024-12-18T01:36:47.8409099Z >>> placement="rank:0/cuda:0" 2024-12-18T01:36:47.8409400Z >>> ) 2024-12-18T01:36:47.8409710Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:36:47.8410344Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:36:47.8410734Z 2024-12-18T01:36:47.8410867Z on rank 1: 2024-12-18T01:36:47.8411286Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:36:47.8411900Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:36:47.8412492Z >>> shard_offsets=[5, 0], 2024-12-18T01:36:47.8412975Z >>> shard_lengths=[5, 5], 2024-12-18T01:36:47.8413313Z >>> placement="rank:1/cuda:1" 2024-12-18T01:36:47.8413620Z >>> ) 2024-12-18T01:36:47.8413949Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:36:47.8414463Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:36:47.8414876Z 2024-12-18T01:36:47.8415126Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.8415500Z 2024-12-18T01:36:47.8527343Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-12-18T01:36:47.8529518Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.8530257Z 2024-12-18T01:36:47.8530900Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-12-18T01:36:47.8531816Z size and sharding spec on each rank. 2024-12-18T01:36:47.8532204Z 2024-12-18T01:36:47.8532372Z Args: 2024-12-18T01:36:47.8532989Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-12-18T01:36:47.8534130Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-12-18T01:36:47.8535124Z The specification describing how to shard the Tensor. 2024-12-18T01:36:47.8535970Z global_size (Sequence[int]): Size of the sharded tensor. 2024-12-18T01:36:47.8537097Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-12-18T01:36:47.8537907Z Default: None 2024-12-18T01:36:47.8538442Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:36:47.8539302Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:36:47.8540261Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:36:47.8541014Z Default: ``False``. 2024-12-18T01:36:47.8541334Z 2024-12-18T01:36:47.8541503Z Returns: 2024-12-18T01:36:47.8542171Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-12-18T01:36:47.8542983Z tensor stored in the current rank. 2024-12-18T01:36:47.8543413Z 2024-12-18T01:36:47.8543560Z Examples: 2024-12-18T01:36:47.8543925Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.8544322Z >>> # All tensors below are of torch.int64 type. 2024-12-18T01:36:47.8544682Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:36:47.8545092Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:36:47.8545594Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-12-18T01:36:47.8546003Z >>> local_tensor 2024-12-18T01:36:47.8546262Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-12-18T01:36:47.8546548Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-12-18T01:36:47.8546846Z >>> sharding_dim = 0 2024-12-18T01:36:47.8547142Z >>> sharding_spec = ChunkShardingSpec( 2024-12-18T01:36:47.8547472Z dim=sharding_dim, 2024-12-18T01:36:47.8547753Z placements=[ 2024-12-18T01:36:47.8548015Z "rank:0/cuda:0", 2024-12-18T01:36:47.8548297Z "rank:1/cuda:1", 2024-12-18T01:36:47.8548570Z ], 2024-12-18T01:36:47.8548795Z ) 2024-12-18T01:36:47.8549176Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-12-18T01:36:47.8549629Z >>> st 2024-12-18T01:36:47.8549874Z ShardedTensor( 2024-12-18T01:36:47.8550290Z ShardedTensorMetadata( 2024-12-18T01:36:47.8550928Z shards_metadata=[ 2024-12-18T01:36:47.8551736Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-12-18T01:36:47.8552901Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-12-18T01:36:47.8553762Z ], 2024-12-18T01:36:47.8554143Z size=torch.Size([2, 4]) 2024-12-18T01:36:47.8554635Z ) 2024-12-18T01:36:47.8555005Z >>> st.local_tensor() 2024-12-18T01:36:47.8555441Z tensor([1, 2, 3, 4]) # Rank 0 2024-12-18T01:36:47.8555933Z tensor([3, 4, 5, 6]) # Rank 1 2024-12-18T01:36:47.8556135Z 2024-12-18T01:36:47.8556408Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-12-18T01:36:47.8557033Z rank validations, and we only validate the local shard on the current rank. 2024-12-18T01:36:47.8557720Z We fully rely on the user to ensure local tensor is sharded based on the 2024-12-18T01:36:47.8558144Z sharding spec. 2024-12-18T01:36:47.8558321Z 2024-12-18T01:36:47.8558575Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.8558950Z 2024-12-18T01:36:47.8559693Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-12-18T01:36:47.8560706Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.8561359Z 2024-12-18T01:36:47.8561803Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-12-18T01:36:47.8562656Z single local shard. 2024-12-18T01:36:47.8562929Z 2024-12-18T01:36:47.8563342Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-12-18T01:36:47.8564224Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-12-18T01:36:47.8564779Z we swap local shards directly. 2024-12-18T01:36:47.8565229Z For more generic cases, we merge different shards across different ranks and split 2024-12-18T01:36:47.8565858Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-12-18T01:36:47.8566232Z 2024-12-18T01:36:47.8566323Z Args: 2024-12-18T01:36:47.8566737Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-12-18T01:36:47.8567306Z specification describing how the tensor is sharded. 2024-12-18T01:36:47.8567577Z 2024-12-18T01:36:47.8567680Z Returns: 2024-12-18T01:36:47.8567998Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-12-18T01:36:47.8568319Z 2024-12-18T01:36:47.8568409Z Examples: 2024-12-18T01:36:47.8568647Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.8568945Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:36:47.8569412Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:36:47.8570125Z >>> tensor = torch.stack([tensor, tensor]) 2024-12-18T01:36:47.8570685Z >>> tensor 2024-12-18T01:36:47.8571124Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-12-18T01:36:47.8571759Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-12-18T01:36:47.8572372Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-12-18T01:36:47.8572994Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-12-18T01:36:47.8573569Z >>> sharding_dim = 0 2024-12-18T01:36:47.8574066Z >>> spec = ChunkShardingSpec( 2024-12-18T01:36:47.8574627Z dim=sharding_dim, 2024-12-18T01:36:47.8575117Z placements=[ 2024-12-18T01:36:47.8575578Z "rank:0/cuda:0", 2024-12-18T01:36:47.8576029Z "rank:1/cuda:1", 2024-12-18T01:36:47.8576532Z "rank:2/cuda:2", 2024-12-18T01:36:47.8577041Z "rank:3/cuda:3", 2024-12-18T01:36:47.8577527Z ], 2024-12-18T01:36:47.8577904Z ) 2024-12-18T01:36:47.8578272Z >>> current_offsets = [0] * 2 2024-12-18T01:36:47.8578801Z >>> current_offsets[0] = rank * 2 2024-12-18T01:36:47.8579551Z >>> shard_metadata = ShardMetadata( 2024-12-18T01:36:47.8580217Z shard_offsets=copy.deepcopy(current_offsets), 2024-12-18T01:36:47.8580865Z shard_sizes=tensor.size(), 2024-12-18T01:36:47.8581454Z placement=spec.placements[rank], 2024-12-18T01:36:47.8582003Z ) 2024-12-18T01:36:47.8582399Z >>> local_shards = [ 2024-12-18T01:36:47.8582834Z Shard( 2024-12-18T01:36:47.8583202Z tensor=tensor, 2024-12-18T01:36:47.8583532Z metadata=shard_metadata, 2024-12-18T01:36:47.8583840Z ) 2024-12-18T01:36:47.8584065Z ] 2024-12-18T01:36:47.8584419Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-12-18T01:36:47.8584854Z >>> sharding_dim = 1 2024-12-18T01:36:47.8585237Z >>> resharding_spec = ChunkShardingSpec( 2024-12-18T01:36:47.8585579Z dim=sharding_dim, 2024-12-18T01:36:47.8585873Z placements=[ 2024-12-18T01:36:47.8586149Z "rank:0/cuda:0", 2024-12-18T01:36:47.8586424Z "rank:1/cuda:1", 2024-12-18T01:36:47.8586710Z "rank:2/cuda:2", 2024-12-18T01:36:47.8586997Z "rank:3/cuda:3", 2024-12-18T01:36:47.8587271Z ], 2024-12-18T01:36:47.8587494Z ) 2024-12-18T01:36:47.8587752Z >>> st.reshard(resharding_spec) 2024-12-18T01:36:47.8588078Z >>> tensor = st.local_shards()[0].tensor 2024-12-18T01:36:47.8588392Z >>> tensor 2024-12-18T01:36:47.8588676Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-12-18T01:36:47.8589160Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-12-18T01:36:47.8589830Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-12-18T01:36:47.8590511Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-12-18T01:36:47.8590985Z 2024-12-18T01:36:47.8591505Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.8592208Z 2024-12-18T01:36:47.8755513Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-12-18T01:36:47.8757329Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.8757735Z 2024-12-18T01:36:47.8757958Z Representation of a sharding plan, describes how to shard a module 2024-12-18T01:36:47.8758556Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-12-18T01:36:47.8759224Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-12-18T01:36:47.8759858Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-12-18T01:36:47.8760228Z 2024-12-18T01:36:47.8760319Z Args: 2024-12-18T01:36:47.8760711Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-12-18T01:36:47.8761278Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-12-18T01:36:47.8761828Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-12-18T01:36:47.8762463Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-12-18T01:36:47.8762951Z a parameter to a `ShardingSpec`. 2024-12-18T01:36:47.8763441Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-12-18T01:36:47.8764234Z to a `Sharder` object. 2024-12-18T01:36:47.8765181Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-12-18T01:36:47.8766487Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-12-18T01:36:47.8767645Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-12-18T01:36:47.8768452Z Default: `None` 2024-12-18T01:36:47.8769214Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-12-18T01:36:47.8770498Z a module's sharded output to be returned as a Tensor from its local shards to 2024-12-18T01:36:47.8771622Z ensure further processing in a data parallel fashion. ("" in list means the 2024-12-18T01:36:47.8772469Z root module). 2024-12-18T01:36:47.8772904Z Default: None 2024-12-18T01:36:47.8773344Z Example: 2024-12-18T01:36:47.8774093Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-12-18T01:36:47.8775399Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-12-18T01:36:47.8776172Z 2024-12-18T01:36:47.8776490Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-12-18T01:36:47.8777270Z >>> class MyModule(nn.Module): 2024-12-18T01:36:47.8777828Z >>> def __init__(self) -> None: 2024-12-18T01:36:47.8778401Z >>> super().__init__() 2024-12-18T01:36:47.8778963Z >>> self.fc1 = nn.Linear() 2024-12-18T01:36:47.8779507Z >>> self.gelu = nn.GELU() 2024-12-18T01:36:47.8780035Z >>> self.fc2 = nn.Linear() 2024-12-18T01:36:47.8780563Z >>> self.relu = nn.Linear() 2024-12-18T01:36:47.8781097Z >>> 2024-12-18T01:36:47.8781500Z >>> def forward(self, input): 2024-12-18T01:36:47.8782313Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-12-18T01:36:47.8782861Z 2024-12-18T01:36:47.8782869Z 2024-12-18T01:36:47.8783115Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-12-18T01:36:47.8783754Z >>> sharding_plan = ShardingPlan( 2024-12-18T01:36:47.8784316Z >>> plan={ 2024-12-18T01:36:47.8784747Z >>> "fc1.weight": spec1, 2024-12-18T01:36:47.8785303Z >>> "fc2.weight": spec2 2024-12-18T01:36:47.8785826Z >>> }, 2024-12-18T01:36:47.8786219Z >>> output_plan={ 2024-12-18T01:36:47.8786702Z >>> "fc2": output_spec 2024-12-18T01:36:47.8787304Z >>> }, 2024-12-18T01:36:47.8787748Z >>> return_local_tensor=["fc2"] 2024-12-18T01:36:47.8788299Z >>> ) 2024-12-18T01:36:47.8788522Z 2024-12-18T01:36:47.8788954Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.8789646Z 2024-12-18T01:36:47.9608552Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-12-18T01:36:47.9609673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.9610060Z 2024-12-18T01:36:47.9610174Z Run post-localSGD algorithm. 2024-12-18T01:36:47.9610379Z 2024-12-18T01:36:47.9610611Z This DDP communication hook is used for running post-localSGD algorithm, 2024-12-18T01:36:47.9611131Z by combining with a model averaging component (e.g., 2024-12-18T01:36:47.9611743Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-12-18T01:36:47.9612319Z that runs after the optimizer step. 2024-12-18T01:36:47.9612533Z 2024-12-18T01:36:47.9612621Z Args: 2024-12-18T01:36:47.9612970Z state (PostLocalSGDState): State information to run post-localSGD. 2024-12-18T01:36:47.9613584Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-12-18T01:36:47.9614383Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:36:47.9615154Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:36:47.9615927Z only exactly one tensor is stored in this bucket. 2024-12-18T01:36:47.9616409Z 2024-12-18T01:36:47.9616563Z Returns: 2024-12-18T01:36:47.9617222Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:36:47.9617871Z 2024-12-18T01:36:47.9618052Z Example:: 2024-12-18T01:36:47.9618462Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.9619413Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-12-18T01:36:47.9620314Z start_localSGD_iter=10) 2024-12-18T01:36:47.9621042Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:47.9622197Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-12-18T01:36:47.9623711Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-12-18T01:36:47.9624603Z 2024-12-18T01:36:47.9625068Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.9625761Z 2024-12-18T01:36:47.9667196Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=343. 2024-12-18T01:36:47.9669412Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.9670137Z 2024-12-18T01:36:47.9670337Z Implement PowerSGD algorithm. 2024-12-18T01:36:47.9670682Z 2024-12-18T01:36:47.9671101Z This DDP communication hook implements PowerSGD gradient compression 2024-12-18T01:36:47.9672165Z algorithm described in the `paper `_. 2024-12-18T01:36:47.9673350Z Once gradient tensors are aggregated across all workers, this hook applies 2024-12-18T01:36:47.9674075Z compression as follows: 2024-12-18T01:36:47.9674245Z 2024-12-18T01:36:47.9674691Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-12-18T01:36:47.9675228Z 2024-12-18T01:36:47.9675739Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-12-18T01:36:47.9676262Z 2024-12-18T01:36:47.9676753Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-12-18T01:36:47.9677266Z 2024-12-18T01:36:47.9677394Z 2. Handles uncompressed tensors: 2024-12-18T01:36:47.9677594Z 2024-12-18T01:36:47.9678091Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-12-18T01:36:47.9678709Z 2024-12-18T01:36:47.9679042Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-12-18T01:36:47.9679500Z 2024-12-18T01:36:47.9679729Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-12-18T01:36:47.9680251Z 2024-12-18T01:36:47.9680656Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-12-18T01:36:47.9681875Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-12-18T01:36:47.9682679Z 2024-12-18T01:36:47.9682951Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-12-18T01:36:47.9683423Z 2024-12-18T01:36:47.9683626Z 3.3. Allreduces Ps as a batch; 2024-12-18T01:36:47.9683986Z 2024-12-18T01:36:47.9684199Z 3.4. Orthogonalizes each P in Ps; 2024-12-18T01:36:47.9684573Z 2024-12-18T01:36:47.9684923Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-12-18T01:36:47.9685505Z 2024-12-18T01:36:47.9685693Z 3.6. Allreduces Qs as a batch; 2024-12-18T01:36:47.9686065Z 2024-12-18T01:36:47.9686617Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-12-18T01:36:47.9687402Z 2024-12-18T01:36:47.9688177Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-12-18T01:36:47.9689682Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-12-18T01:36:47.9691276Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-12-18T01:36:47.9692345Z 2024-12-18T01:36:47.9692617Z Args: 2024-12-18T01:36:47.9693662Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-12-18T01:36:47.9695291Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-12-18T01:36:47.9696407Z and ``min_compression_rate``. 2024-12-18T01:36:47.9697573Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:36:47.9699216Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:36:47.9700134Z only exactly one tensor is stored in this bucket. 2024-12-18T01:36:47.9700595Z 2024-12-18T01:36:47.9700745Z Returns: 2024-12-18T01:36:47.9701840Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:36:47.9702561Z 2024-12-18T01:36:47.9702744Z Example:: 2024-12-18T01:36:47.9703166Z >>> # xdoctest: +SKIP 2024-12-18T01:36:47.9703952Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-12-18T01:36:47.9704949Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-12-18T01:36:47.9705738Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-12-18T01:36:47.9706374Z 2024-12-18T01:36:47.9706838Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.9707512Z 2024-12-18T01:36:47.9730735Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=37. 2024-12-18T01:36:47.9732083Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.9732482Z 2024-12-18T01:36:47.9732673Z Averages parameters periodically after the warm-up stage. 2024-12-18T01:36:47.9732968Z 2024-12-18T01:36:47.9733365Z This can be used for running `post-local SGD `_, 2024-12-18T01:36:47.9733942Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-12-18T01:36:47.9734505Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-12-18T01:36:47.9734866Z 2024-12-18T01:36:47.9734955Z Args: 2024-12-18T01:36:47.9735263Z period (int): The number of steps per model averaging. 2024-12-18T01:36:47.9735812Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-12-18T01:36:47.9736317Z Otherwise, only DDP needs to be used. 2024-12-18T01:36:47.9736776Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-12-18T01:36:47.9737227Z model averaging is skipped. 2024-12-18T01:36:47.9737938Z process_group: The process group to be used for all-reduce. 2024-12-18T01:36:47.9738707Z If ``None``, the default process group, which 2024-12-18T01:36:47.9739520Z is created by :func:`torch.distributed.init_process_group`, 2024-12-18T01:36:47.9740303Z will be used. (default: ``None``) 2024-12-18T01:36:47.9740736Z 2024-12-18T01:36:47.9740912Z Example:: 2024-12-18T01:36:47.9741153Z 2024-12-18T01:36:47.9741375Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:47.9741984Z >>> import torch 2024-12-18T01:36:47.9742465Z >>> import torch.distributed as dist 2024-12-18T01:36:47.9743464Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-12-18T01:36:47.9744794Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:36:47.9745662Z >>> import torch.nn as nn 2024-12-18T01:36:47.9746162Z >>> 2024-12-18T01:36:47.9746695Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:36:47.9747441Z >>> torch.cuda.set_device(rank) 2024-12-18T01:36:47.9748074Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-12-18T01:36:47.9748962Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:36:47.9749718Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:36:47.9750321Z >>> ) 2024-12-18T01:36:47.9750783Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:36:47.9751821Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:36:47.9752933Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:47.9753610Z >>> 2024-12-18T01:36:47.9754267Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:36:47.9755269Z >>> # After 100 steps, run model averaging every 4 steps. 2024-12-18T01:36:47.9756403Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:36:47.9757763Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:36:47.9758660Z >>> for step in range(0, 200): 2024-12-18T01:36:47.9759224Z >>> optimizer.zero_grad() 2024-12-18T01:36:47.9759786Z >>> loss = loss_fn(output, labels) 2024-12-18T01:36:47.9760335Z >>> loss.backward() 2024-12-18T01:36:47.9760775Z >>> optimizer.step() 2024-12-18T01:36:47.9761448Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-12-18T01:36:47.9762426Z >>> # inter-node communication only occurs every 4 iterations after 2024-12-18T01:36:47.9763230Z >>> # the initial ``warmup_steps`` period. 2024-12-18T01:36:47.9763964Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:36:47.9764466Z 2024-12-18T01:36:47.9764942Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.9765591Z 2024-12-18T01:36:47.9767146Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-12-18T01:36:47.9768351Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:47.9768736Z 2024-12-18T01:36:47.9769075Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-12-18T01:36:47.9769531Z 2024-12-18T01:36:47.9769837Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-12-18T01:36:47.9770467Z by using different periods concurrently after the warm-up stage. 2024-12-18T01:36:47.9771187Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-12-18T01:36:47.9772042Z that supports `post-local SGD `_, which essentially only supports 2024-12-18T01:36:47.9772793Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-12-18T01:36:47.9773548Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-12-18T01:36:47.9774778Z Similarly, the process groups within this class do not have such an intra-machine process 2024-12-18T01:36:47.9776042Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-12-18T01:36:47.9776778Z 2024-12-18T01:36:47.9776932Z Args: 2024-12-18T01:36:47.9777630Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-12-18T01:36:47.9778715Z process group size, used for initializing process groups of 2024-12-18T01:36:47.9779708Z different sizes in a hierarchy to average parameters concurrently. 2024-12-18T01:36:47.9780698Z Particularly, at each iteration, there will be at most a single 2024-12-18T01:36:47.9781748Z process group that runs averaging -- the period of such group should 2024-12-18T01:36:47.9782780Z have the largest period which the current step can be divided by. 2024-12-18T01:36:47.9783817Z For example, if the dict has three keys: 2, 4, and 8, 2024-12-18T01:36:47.9784676Z then this means totally three process groups will be created to 2024-12-18T01:36:47.9785630Z average parameters every 2, 4, and 8 iterations, respectively. 2024-12-18T01:36:47.9786302Z At the 4th iteration, only the second process group will run 2024-12-18T01:36:47.9786795Z averaging, because the first process group should be a 2024-12-18T01:36:47.9787317Z subset of the second process group, and no need to execute the first 2024-12-18T01:36:47.9787781Z process group redundantly. 2024-12-18T01:36:47.9788298Z On the other hand, the third process group can only be triggered 2024-12-18T01:36:47.9788831Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-12-18T01:36:47.9789476Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-12-18T01:36:47.9790327Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-12-18T01:36:47.9791084Z If ``None``, the default process group, which is created 2024-12-18T01:36:47.9791903Z by :func:`torch.distributed.init_process_group`, will be used. 2024-12-18T01:36:47.9792683Z (default: ``None``) 2024-12-18T01:36:47.9793106Z 2024-12-18T01:36:47.9793291Z Example:: 2024-12-18T01:36:47.9793699Z >>> # xdoctest: +SKIP('undefined rank') 2024-12-18T01:36:47.9794349Z >>> from collections import OrderedDict 2024-12-18T01:36:47.9794923Z >>> import torch 2024-12-18T01:36:47.9795488Z >>> import torch.distributed as dist 2024-12-18T01:36:47.9796502Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:36:47.9797370Z >>> PostLocalSGDState, 2024-12-18T01:36:47.9798094Z >>> post_localSGD_hook, 2024-12-18T01:36:47.9798584Z >>> ) 2024-12-18T01:36:47.9799530Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-12-18T01:36:47.9800642Z >>> import torch.nn as nn 2024-12-18T01:36:47.9801127Z >>> 2024-12-18T01:36:47.9801928Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:36:47.9802672Z >>> torch.cuda.set_device(rank) 2024-12-18T01:36:47.9803293Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:36:47.9804037Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:36:47.9804785Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:36:47.9805347Z >>> ) 2024-12-18T01:36:47.9805844Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:36:47.9806688Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-12-18T01:36:47.9807210Z >>> subgroup, _ = dist.new_subgroups() 2024-12-18T01:36:47.9807749Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-12-18T01:36:47.9808350Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:47.9808725Z >>> 2024-12-18T01:36:47.9809138Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-12-18T01:36:47.9809665Z >>> # the 16 processes every 16 iterations. 2024-12-18T01:36:47.9810099Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-12-18T01:36:47.9810628Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-12-18T01:36:47.9811296Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:36:47.9812429Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:36:47.9813405Z >>> # After 100 steps, run model averaging at two levels. 2024-12-18T01:36:47.9814073Z >>> for step in range(0, 200): 2024-12-18T01:36:47.9814627Z >>> optimizer.zero_grad() 2024-12-18T01:36:47.9815176Z >>> loss = loss_fn(output, labels) 2024-12-18T01:36:47.9815750Z >>> loss.backward() 2024-12-18T01:36:47.9816253Z >>> optimizer.step() 2024-12-18T01:36:47.9816836Z >>> # Average parameters after ``optimizer.step()``. 2024-12-18T01:36:47.9817799Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-12-18T01:36:47.9818814Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:36:47.9819430Z 2024-12-18T01:36:47.9819590Z .. warning :: 2024-12-18T01:36:47.9820281Z The last group size in the dict must be the size of the provided ``process_group``, 2024-12-18T01:36:47.9821120Z which indicates model averaging at the highest level of the hierarchy. 2024-12-18T01:36:47.9821769Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-12-18T01:36:47.9822180Z 2024-12-18T01:36:47.9822293Z .. warning :: 2024-12-18T01:36:47.9822742Z `HierarchicalModelAverager` is experimental and subject to change. 2024-12-18T01:36:47.9823100Z 2024-12-18T01:36:47.9823348Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:47.9823719Z 2024-12-18T01:36:48.0055606Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-12-18T01:36:48.0057735Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0058466Z 2024-12-18T01:36:48.0059077Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-12-18T01:36:48.0060280Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-12-18T01:36:48.0060965Z 2024-12-18T01:36:48.0061274Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-12-18T01:36:48.0061781Z 2024-12-18T01:36:48.0061970Z .. warning:: 2024-12-18T01:36:48.0062516Z Current implementation only supports loading Tensors. 2024-12-18T01:36:48.0063060Z 2024-12-18T01:36:48.0063253Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0063762Z >>> sd = {"mode": model} 2024-12-18T01:36:48.0064223Z >>> dcp.load( 2024-12-18T01:36:48.0064591Z >>> sd, 2024-12-18T01:36:48.0064981Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:36:48.0065388Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:36:48.0065743Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:36:48.0066063Z >>> ) 2024-12-18T01:36:48.0066179Z 2024-12-18T01:36:48.0066446Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0066812Z 2024-12-18T01:36:48.0067515Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-12-18T01:36:48.0068540Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0068927Z 2024-12-18T01:36:48.0069282Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-12-18T01:36:48.0070078Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-12-18T01:36:48.0070894Z metadata file, like Torch Save files. 2024-12-18T01:36:48.0071268Z 2024-12-18T01:36:48.0071598Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-12-18T01:36:48.0072134Z 2024-12-18T01:36:48.0072319Z .. warning:: 2024-12-18T01:36:48.0072860Z Current implementation only supports loading Tensors. 2024-12-18T01:36:48.0073397Z 2024-12-18T01:36:48.0073731Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0074308Z >>> sd = {"mode": model} 2024-12-18T01:36:48.0074761Z >>> dcp.load( 2024-12-18T01:36:48.0075144Z >>> sd, 2024-12-18T01:36:48.0075727Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:36:48.0076434Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:36:48.0077076Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:36:48.0077644Z >>> ) 2024-12-18T01:36:48.0077847Z 2024-12-18T01:36:48.0078326Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0079007Z 2024-12-18T01:36:48.0141208Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-12-18T01:36:48.0142415Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0142788Z 2024-12-18T01:36:48.0143014Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-12-18T01:36:48.0143336Z 2024-12-18T01:36:48.0143496Z This is the current recommended way to checkpoint FSDP. 2024-12-18T01:36:48.0143874Z >>> # xdoctest: +SKIP 2024-12-18T01:36:48.0144194Z >>> import torch.distributed.checkpoint as dist_cp 2024-12-18T01:36:48.0144552Z >>> # Save 2024-12-18T01:36:48.0144854Z >>> model: torch.nn.Model 2024-12-18T01:36:48.0145140Z >>> optim_params = model.parameters() 2024-12-18T01:36:48.0145508Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-12-18T01:36:48.0145850Z >>> # Save 2024-12-18T01:36:48.0146197Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:36:48.0146671Z >>> state_dict = { 2024-12-18T01:36:48.0147192Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-12-18T01:36:48.0147846Z >>> "model": model.state_dict() 2024-12-18T01:36:48.0148374Z >>> } 2024-12-18T01:36:48.0148773Z >>> dist_cp.save_state_dict( 2024-12-18T01:36:48.0149405Z >>> state_dict=optim_state, 2024-12-18T01:36:48.0150089Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-12-18T01:36:48.0150878Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-12-18T01:36:48.0151488Z >>> ) 2024-12-18T01:36:48.0151834Z >>> 2024-12-18T01:36:48.0152194Z >>> # Load 2024-12-18T01:36:48.0152830Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:36:48.0153729Z >>> model_state_dict = model_tp.state_dict() 2024-12-18T01:36:48.0154343Z >>> checkpoint = { 2024-12-18T01:36:48.0154825Z >>> "model": model_state_dict 2024-12-18T01:36:48.0155365Z >>> } 2024-12-18T01:36:48.0155857Z >>> dist_cp.load_state_dict( 2024-12-18T01:36:48.0156407Z >>> state_dict=checkpoint, 2024-12-18T01:36:48.0157091Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-12-18T01:36:48.0157911Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-12-18T01:36:48.0158516Z >>> ) 2024-12-18T01:36:48.0159010Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-12-18T01:36:48.0159610Z >>> 2024-12-18T01:36:48.0160134Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-12-18T01:36:48.0160857Z >>> model_state_dict, 2024-12-18T01:36:48.0161413Z >>> optimizer_key="optimizer", 2024-12-18T01:36:48.0162145Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-12-18T01:36:48.0162833Z >>> ) 2024-12-18T01:36:48.0163196Z >>> 2024-12-18T01:36:48.0163665Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-12-18T01:36:48.0164367Z >>> model, optim, optim_state["optimizer"] 2024-12-18T01:36:48.0164940Z >>> ) 2024-12-18T01:36:48.0165305Z >>> 2024-12-18T01:36:48.0165728Z >>> optim.load_state_dict(flattened_osd) 2024-12-18T01:36:48.0166143Z 2024-12-18T01:36:48.0166629Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0167322Z 2024-12-18T01:36:48.0179831Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-12-18T01:36:48.0181845Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0182578Z 2024-12-18T01:36:48.0183167Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-12-18T01:36:48.0183951Z 2024-12-18T01:36:48.0184528Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-12-18T01:36:48.0185328Z 2024-12-18T01:36:48.0185859Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:36:48.0186813Z will be visible to the whole process. 2024-12-18T01:36:48.0187223Z 2024-12-18T01:36:48.0187747Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-12-18T01:36:48.0188588Z 2024-12-18T01:36:48.0188797Z 1) set_up_planner - called on all ranks. 2024-12-18T01:36:48.0189410Z Signals the start of a checkpoint save. 2024-12-18T01:36:48.0189795Z 2024-12-18T01:36:48.0190008Z 2) create_local_plan - called on all ranks. 2024-12-18T01:36:48.0190837Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-12-18T01:36:48.0191561Z 2024-12-18T01:36:48.0191886Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:36:48.0192883Z Takes the SavePlan from all ranks and make any global decision. 2024-12-18T01:36:48.0193481Z 2024-12-18T01:36:48.0193684Z 4) finish_plan - called on all ranks. 2024-12-18T01:36:48.0194480Z This gives each rank a chance to adjust to global planning decisions. 2024-12-18T01:36:48.0195102Z 2024-12-18T01:36:48.0195396Z 5) resolve_data - called multiple times on each rank 2024-12-18T01:36:48.0196342Z Lookups a value on the `state_dict` for the storage layer to write. 2024-12-18T01:36:48.0196948Z 2024-12-18T01:36:48.0197587Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-12-18T01:36:48.0198907Z most changes can be expressed by changes in a single method. 2024-12-18T01:36:48.0199469Z 2024-12-18T01:36:48.0199677Z There are 3 usual patterns of extension: 2024-12-18T01:36:48.0200074Z 2024-12-18T01:36:48.0200375Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-12-18T01:36:48.0200998Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-12-18T01:36:48.0201552Z 2024-12-18T01:36:48.0201688Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0202043Z >>> class RenamePlanner(DefaultSavePlanner): 2024-12-18T01:36:48.0202400Z >>> def set_up_planner( 2024-12-18T01:36:48.0202681Z >>> self, 2024-12-18T01:36:48.0202942Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:36:48.0203289Z >>> storage_meta: Optional[StorageMeta], 2024-12-18T01:36:48.0203629Z >>> is_coordinator: bool, 2024-12-18T01:36:48.0203926Z >>> ) -> None: 2024-12-18T01:36:48.0204192Z >>> # prefix all keys with `foo_`` 2024-12-18T01:36:48.0204706Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-12-18T01:36:48.0205102Z 2024-12-18T01:36:48.0205440Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-12-18T01:36:48.0205878Z 2024-12-18T01:36:48.0206071Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0206651Z >>> class FP16Planner(DefaultSavePlanner): 2024-12-18T01:36:48.0207226Z >>> def create_local_plan(self): 2024-12-18T01:36:48.0207789Z >>> plan = super().create_local_plan() 2024-12-18T01:36:48.0208371Z >>> for p in plan: 2024-12-18T01:36:48.0208867Z >>> if p.tensor_data is not None: 2024-12-18T01:36:48.0209552Z >>> p.tensor_data.properties.dtype = torch.float16 2024-12-18T01:36:48.0210221Z >>> return plan 2024-12-18T01:36:48.0210659Z >>> 2024-12-18T01:36:48.0211058Z >>> def resolve_data(self, write_item): 2024-12-18T01:36:48.0211837Z >>> item = super().resolve_data(write_item) 2024-12-18T01:36:48.0212749Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-12-18T01:36:48.0213475Z 2024-12-18T01:36:48.0214115Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-12-18T01:36:48.0214969Z 2024-12-18T01:36:48.0215174Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0215776Z >>> from itertools import zip_longest 2024-12-18T01:36:48.0216366Z >>> from dataclasses import replace 2024-12-18T01:36:48.0217067Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-12-18T01:36:48.0218076Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-12-18T01:36:48.0219049Z >>> # This sample doesn't handle ShardedTensors 2024-12-18T01:36:48.0219842Z >>> def create_global_plan(self, all_plans): 2024-12-18T01:36:48.0220557Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2024-12-18T01:36:48.0221228Z >>> items_per_rank = [ 2024-12-18T01:36:48.0221794Z >>> [item for item in items if item is not None] 2024-12-18T01:36:48.0222541Z >>> for items in zip(*zip_longest(*iters), strict=True) 2024-12-18T01:36:48.0223200Z >>> ] 2024-12-18T01:36:48.0223593Z >>> all_plans = [ 2024-12-18T01:36:48.0224232Z >>> replace(plan, items=items) 2024-12-18T01:36:48.0224989Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2024-12-18T01:36:48.0225732Z >>> ] 2024-12-18T01:36:48.0226222Z >>> return super().create_global_plan(all_plans) 2024-12-18T01:36:48.0226707Z 2024-12-18T01:36:48.0227203Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-12-18T01:36:48.0228446Z accomplished by having each rank contribute their data items in the local plan and 2024-12-18T01:36:48.0229399Z the global planner aggregate them: 2024-12-18T01:36:48.0229869Z 2024-12-18T01:36:48.0230081Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0230758Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-12-18T01:36:48.0231463Z >>> def create_local_plan(self) -> SavePlan: 2024-12-18T01:36:48.0232077Z >>> plan = super().create_local_plan() 2024-12-18T01:36:48.0232791Z >>> return replace(plan, planner_data="per-rank-data") 2024-12-18T01:36:48.0233422Z >>> 2024-12-18T01:36:48.0234190Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-12-18T01:36:48.0235274Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-12-18T01:36:48.0236251Z >>> merged_data = [p.planner_data for p in global_plan] 2024-12-18T01:36:48.0237067Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-12-18T01:36:48.0237804Z >>> return global_plan, metadata 2024-12-18T01:36:48.0238196Z 2024-12-18T01:36:48.0238656Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0239342Z 2024-12-18T01:36:48.0240510Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=272. 2024-12-18T01:36:48.0242332Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0243077Z 2024-12-18T01:36:48.0243538Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-12-18T01:36:48.0244293Z 2024-12-18T01:36:48.0244764Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-12-18T01:36:48.0245184Z 2024-12-18T01:36:48.0245461Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:36:48.0245974Z will be visible to the whole process. 2024-12-18T01:36:48.0246194Z 2024-12-18T01:36:48.0246479Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-12-18T01:36:48.0246866Z 2024-12-18T01:36:48.0247086Z 1) set_up_planner - called on all ranks. 2024-12-18T01:36:48.0247444Z Signals the start of loading a checkpoint. 2024-12-18T01:36:48.0247699Z 2024-12-18T01:36:48.0247822Z 2) create_local_plan - called on all ranks. 2024-12-18T01:36:48.0248336Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-12-18T01:36:48.0248742Z 2024-12-18T01:36:48.0248924Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:36:48.0249414Z Takes the LoadPlan from all ranks and make any global decision. 2024-12-18T01:36:48.0249716Z 2024-12-18T01:36:48.0249875Z 4) load_bytes - called multiple times on each rank 2024-12-18T01:36:48.0250286Z This is called once per non-tensor value in state_dict. 2024-12-18T01:36:48.0250570Z 2024-12-18T01:36:48.0250891Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-12-18T01:36:48.0251792Z They are called in pair for each Tensor value in state_dict. 2024-12-18T01:36:48.0252346Z 2024-12-18T01:36:48.0252888Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-12-18T01:36:48.0254000Z most changes can be expressed by changes in a single method. 2024-12-18T01:36:48.0254554Z 2024-12-18T01:36:48.0254799Z There are two usual patterns of extension: 2024-12-18T01:36:48.0255286Z 2024-12-18T01:36:48.0255722Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-12-18T01:36:48.0256850Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-12-18T01:36:48.0257950Z to keep a reference to the original state_dict as load happens in place so 2024-12-18T01:36:48.0258778Z we need to be able to perform it in place 2024-12-18T01:36:48.0259192Z 2024-12-18T01:36:48.0259401Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0260009Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-12-18T01:36:48.0260365Z >>> def set_up_planner( 2024-12-18T01:36:48.0260714Z >>> self, 2024-12-18T01:36:48.0260979Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:36:48.0261304Z >>> metadata: Metadata, 2024-12-18T01:36:48.0261603Z >>> is_coordinator: bool, 2024-12-18T01:36:48.0261889Z >>> ) -> None: 2024-12-18T01:36:48.0262165Z >>> self.original_state_dict = state_dict 2024-12-18T01:36:48.0262584Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-12-18T01:36:48.0262965Z >>> 2024-12-18T01:36:48.0263205Z >>> if self.flatten_sharded_tensors: 2024-12-18T01:36:48.0263577Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-12-18T01:36:48.0263937Z >>> 2024-12-18T01:36:48.0264168Z >>> if self.flatten_state_dict: 2024-12-18T01:36:48.0264576Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-12-18T01:36:48.0264970Z >>> 2024-12-18T01:36:48.0265189Z >>> self.state_dict = state_dict 2024-12-18T01:36:48.0265602Z >>> self.metadata = metadata 2024-12-18T01:36:48.0266158Z >>> self.is_coordinator = is_coordinator 2024-12-18T01:36:48.0266727Z >>> 2024-12-18T01:36:48.0267133Z >>> def load_bytes(self, read_item, value): 2024-12-18T01:36:48.0267735Z >>> # Remove the "foo_" prefix 2024-12-18T01:36:48.0268707Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-12-18T01:36:48.0269534Z 2024-12-18T01:36:48.0269542Z 2024-12-18T01:36:48.0270008Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-12-18T01:36:48.0270685Z 2024-12-18T01:36:48.0270892Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.0271551Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-12-18T01:36:48.0272230Z >>> def resolve_tensor(self, read_item): 2024-12-18T01:36:48.0272868Z >>> tensor = super().resolve_tensor(read_item) 2024-12-18T01:36:48.0273597Z >>> return torch.empty_like(tensor, device="cpu") 2024-12-18T01:36:48.0274224Z >>> 2024-12-18T01:36:48.0274659Z >>> def commit_tensor(self, read_item, tensor): 2024-12-18T01:36:48.0275492Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-12-18T01:36:48.0276085Z 2024-12-18T01:36:48.0276568Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0277260Z 2024-12-18T01:36:48.0426652Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-12-18T01:36:48.0427629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0428018Z 2024-12-18T01:36:48.0428164Z Load a distributed ``state_dict`` in SPMD style. 2024-12-18T01:36:48.0428428Z 2024-12-18T01:36:48.0428607Z Each rank will try to read the least amount of data necessary 2024-12-18T01:36:48.0429275Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-12-18T01:36:48.0429877Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-12-18T01:36:48.0430236Z 2024-12-18T01:36:48.0430503Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:36:48.0431122Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-12-18T01:36:48.0431713Z ``load_state_dict`` once the deserialization is complete. 2024-12-18T01:36:48.0432254Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2024-12-18T01:36:48.0432793Z it in the ``state_dict`` with the deserialized object. 2024-12-18T01:36:48.0433050Z 2024-12-18T01:36:48.0433172Z .. warning:: 2024-12-18T01:36:48.0433483Z All tensors in ``state_dict`` must be allocated on their 2024-12-18T01:36:48.0433921Z destination device *prior to* calling this function. 2024-12-18T01:36:48.0434204Z 2024-12-18T01:36:48.0434431Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-12-18T01:36:48.0434867Z on state_dict. 2024-12-18T01:36:48.0435069Z 2024-12-18T01:36:48.0435164Z .. warning:: 2024-12-18T01:36:48.0435691Z Users must call `load_state_dict` on the root module to ensure load 2024-12-18T01:36:48.0436205Z pos-processing and non-tensor data properly propagates. 2024-12-18T01:36:48.0436494Z 2024-12-18T01:36:48.0436587Z .. note: 2024-12-18T01:36:48.0436981Z If no process group is initialized, this function will assume the intent 2024-12-18T01:36:48.0437546Z is to load a checkpoint into the local process. This can be useful in the 2024-12-18T01:36:48.0438135Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-12-18T01:36:48.0438606Z or ShardedTensor) 2024-12-18T01:36:48.0438768Z 2024-12-18T01:36:48.0438875Z .. note: 2024-12-18T01:36:48.0439133Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:36:48.0439392Z 2024-12-18T01:36:48.0439484Z Args: 2024-12-18T01:36:48.0439769Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:36:48.0440191Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:36:48.0440662Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:36:48.0441196Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:36:48.0441677Z It can also be a key if the storage is a key-value store. 2024-12-18T01:36:48.0442066Z (Default: ``None``) 2024-12-18T01:36:48.0442385Z storage_reader (Optional[StorageReader]): 2024-12-18T01:36:48.0442829Z Instance of StorageWriter used to perform reads. If this is not 2024-12-18T01:36:48.0443348Z specified, DCP will automatically infer the reader based on the 2024-12-18T01:36:48.0443859Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:36:48.0444288Z be raised. (Default: ``None``) 2024-12-18T01:36:48.0444625Z planner (Optional[LoadPlanner]): 2024-12-18T01:36:48.0445047Z Instance of LoadPlanner. If this is not specificed, the default 2024-12-18T01:36:48.0445551Z planner will be used. (Default: ``None``) 2024-12-18T01:36:48.0445907Z process_group (Optional[ProcessGroup]): 2024-12-18T01:36:48.0446329Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:36:48.0446723Z (Default: ``None``) 2024-12-18T01:36:48.0446905Z 2024-12-18T01:36:48.0446993Z Returns: 2024-12-18T01:36:48.0447211Z None. 2024-12-18T01:36:48.0447330Z 2024-12-18T01:36:48.0447432Z Examples 2024-12-18T01:36:48.0447644Z >>> # xdoctest: +SKIP 2024-12-18T01:36:48.0447921Z >>> my_model = MyModule() 2024-12-18T01:36:48.0448245Z >>> optimizer = Adagrad(my_model.parameters()) 2024-12-18T01:36:48.0448621Z >>> model_state_dict = my_model.state_dict() 2024-12-18T01:36:48.0449163Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-12-18T01:36:48.0449609Z 2024-12-18T01:36:48.0449772Z >>> torch.distributed.checkpoint.load_state_dict( 2024-12-18T01:36:48.0450161Z >>> state_dict=model_state_dict, 2024-12-18T01:36:48.0450504Z >>> storage_reader=fs_storage_reader, 2024-12-18T01:36:48.0450822Z >>> ) 2024-12-18T01:36:48.0450944Z 2024-12-18T01:36:48.0451156Z >>> # module.load_state_dict() function might have customized steps 2024-12-18T01:36:48.0451585Z >>> # to flush the state_dict, must call it to 2024-12-18T01:36:48.0451964Z >>> # ensure correct behavior. 2024-12-18T01:36:48.0452302Z >>> my_model.load_state_dict(model_state_dict) 2024-12-18T01:36:48.0452537Z 2024-12-18T01:36:48.0452643Z .. note:: 2024-12-18T01:36:48.0452979Z load_state_dict uses collectives to coordinate reads across ranks. 2024-12-18T01:36:48.0453515Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:36:48.0454057Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:36:48.0454628Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:36:48.0455225Z and it is the user's responsibility to ensure that this is set so that each 2024-12-18T01:36:48.0455756Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:36:48.0456046Z 2024-12-18T01:36:48.0456306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0456665Z 2024-12-18T01:36:48.0459942Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-12-18T01:36:48.0460937Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0461309Z 2024-12-18T01:36:48.0461444Z Save a distributed model in SPMD style. 2024-12-18T01:36:48.0461665Z 2024-12-18T01:36:48.0461856Z This function is different from ``torch.save()`` as it handles 2024-12-18T01:36:48.0462415Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-12-18T01:36:48.0462795Z 2024-12-18T01:36:48.0463049Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:36:48.0463578Z save will call ``state_dict`` before serialization. 2024-12-18T01:36:48.0463859Z 2024-12-18T01:36:48.0464026Z .. warning:: 2024-12-18T01:36:48.0464579Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-12-18T01:36:48.0465093Z for saved state_dicts. 2024-12-18T01:36:48.0465447Z 2024-12-18T01:36:48.0465620Z .. warning:: 2024-12-18T01:36:48.0466019Z If using the `process_group` argument, make sure that only its ranks 2024-12-18T01:36:48.0466545Z call `save_state_dict` and that all data in state_dict belong to it. 2024-12-18T01:36:48.0466857Z 2024-12-18T01:36:48.0466962Z .. note:: 2024-12-18T01:36:48.0467347Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-12-18T01:36:48.0467969Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-12-18T01:36:48.0468456Z group needs to be passed in. 2024-12-18T01:36:48.0468782Z 2024-12-18T01:36:48.0468875Z .. note:: 2024-12-18T01:36:48.0469264Z If no process group is available, this function assumes the intention is to save the 2024-12-18T01:36:48.0469764Z state_dict in the local process. 2024-12-18T01:36:48.0469973Z 2024-12-18T01:36:48.0470074Z .. note: 2024-12-18T01:36:48.0470326Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:36:48.0470582Z 2024-12-18T01:36:48.0470586Z 2024-12-18T01:36:48.0470671Z Args: 2024-12-18T01:36:48.0470945Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:36:48.0471358Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:36:48.0471931Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:36:48.0472446Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:36:48.0472976Z It can also be a key if the storage is a key-value store. 2024-12-18T01:36:48.0473360Z (Default: ``None``) 2024-12-18T01:36:48.0473678Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:36:48.0474133Z Instance of StorageWriter used to perform writes. If this is not 2024-12-18T01:36:48.0474650Z specified, DCP will automatically infer the writer based on the 2024-12-18T01:36:48.0475207Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:36:48.0475691Z be raised. (Default: ``None``) 2024-12-18T01:36:48.0476029Z planner (Optional[SavePlanner]): 2024-12-18T01:36:48.0476450Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:36:48.0476897Z planner will be used. (Default: ``None``) 2024-12-18T01:36:48.0477254Z process_group (Optional[ProcessGroup]): 2024-12-18T01:36:48.0477676Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:36:48.0478073Z (Default: ``None``) 2024-12-18T01:36:48.0478247Z 2024-12-18T01:36:48.0478349Z Returns: 2024-12-18T01:36:48.0478669Z Metadata: Metadata object for the saved checkpoint. 2024-12-18T01:36:48.0478933Z 2024-12-18T01:36:48.0479024Z Example: 2024-12-18T01:36:48.0479255Z >>> # xdoctest: +SKIP 2024-12-18T01:36:48.0479534Z >>> my_model = MyModule() 2024-12-18T01:36:48.0479714Z 2024-12-18T01:36:48.0479838Z >>> state_dict = {"model": my_model} 2024-12-18T01:36:48.0480051Z 2024-12-18T01:36:48.0492143Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:36:48.0492808Z >>> torch.distributed.checkpoint.save( 2024-12-18T01:36:48.0493154Z >>> state_dict=state_dict, 2024-12-18T01:36:48.0493486Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:36:48.0493813Z >>> ) 2024-12-18T01:36:48.0493934Z 2024-12-18T01:36:48.0494046Z .. note:: 2024-12-18T01:36:48.0494388Z save_state_dict uses collectives to coordinate writes across ranks. 2024-12-18T01:36:48.0494929Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:36:48.0495495Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:36:48.0496067Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:36:48.0496620Z and it is the user's responsibility to ensure that this is set so that 2024-12-18T01:36:48.0497148Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:36:48.0497455Z 2024-12-18T01:36:48.0497717Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0498300Z 2024-12-18T01:36:48.0498949Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-12-18T01:36:48.0499929Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0500565Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-12-18T01:36:48.0501237Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-12-18T01:36:48.0501766Z 2024-12-18T01:36:48.0501868Z .. warning:: 2024-12-18T01:36:48.0502181Z This feature is experimental and subject to change. 2024-12-18T01:36:48.0502447Z 2024-12-18T01:36:48.0502551Z Args: 2024-12-18T01:36:48.0502843Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:36:48.0503251Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:36:48.0503720Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:36:48.0504254Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:36:48.0504748Z It can also be a key if the storage is a key-value store. 2024-12-18T01:36:48.0505134Z (Default: ``None``) 2024-12-18T01:36:48.0505495Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:36:48.0505958Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-12-18T01:36:48.0506529Z this is not specified, DCP will automatically infer the writer based on the 2024-12-18T01:36:48.0507096Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:36:48.0507533Z be raised. (Default: ``None``) 2024-12-18T01:36:48.0507923Z planner (Optional[SavePlanner]): 2024-12-18T01:36:48.0508347Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:36:48.0508804Z planner will be used. (Default: ``None``) 2024-12-18T01:36:48.0509183Z process_group (Optional[ProcessGroup]): 2024-12-18T01:36:48.0509619Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:36:48.0510025Z (Default: ``None``) 2024-12-18T01:36:48.0510207Z 2024-12-18T01:36:48.0510301Z Returns: 2024-12-18T01:36:48.0510654Z Future: A future holding the resultant Metadata object from `save`. 2024-12-18T01:36:48.0510990Z 2024-12-18T01:36:48.0511120Z Example: 2024-12-18T01:36:48.0511365Z >>> # xdoctest: +SKIP 2024-12-18T01:36:48.0511662Z >>> my_model = MyModule() 2024-12-18T01:36:48.0511852Z 2024-12-18T01:36:48.0511982Z >>> state_dict = {"model": my_model} 2024-12-18T01:36:48.0512199Z 2024-12-18T01:36:48.0512503Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:36:48.0513145Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-12-18T01:36:48.0513586Z >>> state_dict=state_dict, 2024-12-18T01:36:48.0513927Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:36:48.0514252Z >>> ) 2024-12-18T01:36:48.0514464Z >>> 2024-12-18T01:36:48.0514695Z >>> # ... do some work ... 2024-12-18T01:36:48.0514984Z >>> 2024-12-18T01:36:48.0515229Z >>> checkpoint_future.result() 2024-12-18T01:36:48.0515443Z 2024-12-18T01:36:48.0515542Z 2024-12-18T01:36:48.0515992Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0516370Z 2024-12-18T01:36:48.0587140Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-12-18T01:36:48.0588196Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.0588578Z 2024-12-18T01:36:48.0588780Z Initialize rendezvous event object and record its operations. 2024-12-18T01:36:48.0589101Z 2024-12-18T01:36:48.0589193Z Args: 2024-12-18T01:36:48.0589449Z run_id (str): The run id of the rendezvous. 2024-12-18T01:36:48.0589841Z message (str): The message describing the event. 2024-12-18T01:36:48.0590341Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-12-18T01:36:48.0590905Z name (str): Event name. (E.g. Current action being performed). 2024-12-18T01:36:48.0591327Z hostname (str): Hostname of the node. 2024-12-18T01:36:48.0591779Z pid (Optional[int]): The process id of the node. 2024-12-18T01:36:48.0592276Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-12-18T01:36:48.0592979Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-12-18T01:36:48.0593518Z rank (Optional[int]): The rank of the node, if known. 2024-12-18T01:36:48.0593881Z Returns: 2024-12-18T01:36:48.0594100Z None 2024-12-18T01:36:48.0594315Z Example: 2024-12-18T01:36:48.0594572Z >>> # See DynamicRendezvousHandler class 2024-12-18T01:36:48.0594893Z >>> def _record( 2024-12-18T01:36:48.0595141Z ... self, 2024-12-18T01:36:48.0595384Z ... message: str, 2024-12-18T01:36:48.0595765Z ... node_state: NodeState = NodeState.RUNNING, 2024-12-18T01:36:48.0596177Z ... rank: Optional[int] = None, 2024-12-18T01:36:48.0596478Z ... ) -> None: 2024-12-18T01:36:48.0596756Z ... construct_and_record_rdzv_event( 2024-12-18T01:36:48.0597154Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-12-18T01:36:48.0597561Z ... run_id=self._settings.run_id, 2024-12-18T01:36:48.0598064Z ... message=message, 2024-12-18T01:36:48.0598361Z ... node_state=node_state, 2024-12-18T01:36:48.0598767Z ... hostname=self._this_node.addr, 2024-12-18T01:36:48.0599117Z ... pid=self._this_node.pid, 2024-12-18T01:36:48.0599462Z ... local_id=self._this_node.local_id, 2024-12-18T01:36:48.0599803Z ... rank=rank, 2024-12-18T01:36:48.0600054Z ... ) 2024-12-18T01:36:48.0600202Z 2024-12-18T01:36:48.0600451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.0600830Z 2024-12-18T01:36:48.2491686Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=113. 2024-12-18T01:36:48.2492953Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.2493335Z 2024-12-18T01:36:48.2495315Z This configures FSDP-native mixed precision training. 2024-12-18T01:36:48.2495602Z 2024-12-18T01:36:48.2495716Z Attributes: 2024-12-18T01:36:48.2496122Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-12-18T01:36:48.2496687Z parameters during forward and backward and thus the dtype for 2024-12-18T01:36:48.2497212Z forward and backward computation. Outside forward and backward, the 2024-12-18T01:36:48.2497748Z *sharded* parameters are kept in full precision (e.g. for the 2024-12-18T01:36:48.2498422Z optimizer step), and for model checkpointing, the parameters are 2024-12-18T01:36:48.2498911Z always saved in full precision. (Default: ``None``) 2024-12-18T01:36:48.2499406Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:36:48.2499961Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-12-18T01:36:48.2500468Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-12-18T01:36:48.2500972Z the ``param_dtype`` value, still running gradient reduction in low 2024-12-18T01:36:48.2501508Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-12-18T01:36:48.2502039Z to force gradient reduction to run in full precision. (Default: 2024-12-18T01:36:48.2502446Z ``None``) 2024-12-18T01:36:48.2502798Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:36:48.2503326Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-12-18T01:36:48.2503840Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-12-18T01:36:48.2504361Z dtype thereafter. For model checkpointing, the buffers are saved 2024-12-18T01:36:48.2504872Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-12-18T01:36:48.2505264Z ``None``) 2024-12-18T01:36:48.2505710Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-12-18T01:36:48.2506247Z gradients to full precision after the backward pass in preparation 2024-12-18T01:36:48.2506771Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-12-18T01:36:48.2507298Z in the dtype used for gradient reduction, which can save memory if 2024-12-18T01:36:48.2507822Z using a custom optimizer that supports running in low precision. 2024-12-18T01:36:48.2508242Z (Default: ``False``) 2024-12-18T01:36:48.2508631Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-12-18T01:36:48.2509146Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-12-18T01:36:48.2509671Z that parameter and input dtypes match for forward computation, as 2024-12-18T01:36:48.2510258Z required by many ops. This may need to be set to ``True`` when only 2024-12-18T01:36:48.2510801Z applying mixed precision to some but not all FSDP modules, in which 2024-12-18T01:36:48.2511346Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-12-18T01:36:48.2511774Z (Default: ``False``) 2024-12-18T01:36:48.2512161Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-12-18T01:36:48.2512760Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-12-18T01:36:48.2513263Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-12-18T01:36:48.2513710Z this does not do anything. (Default: ``True``) 2024-12-18T01:36:48.2514177Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-12-18T01:36:48.2514679Z module classes to ignore for mixed precision when using an 2024-12-18T01:36:48.2515160Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-12-18T01:36:48.2515796Z applied to them separately with mixed precision disabled (meaning 2024-12-18T01:36:48.2516335Z that the final FSDP construction would deviate from the specified 2024-12-18T01:36:48.2516844Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-12-18T01:36:48.2517350Z not do anything. This API is experimental and subject to change. 2024-12-18T01:36:48.2517768Z (Default: ``(_BatchNorm,)``) 2024-12-18T01:36:48.2517986Z 2024-12-18T01:36:48.2518177Z .. note:: This API is experimental and subject to change. 2024-12-18T01:36:48.2518463Z 2024-12-18T01:36:48.2518684Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-12-18T01:36:48.2519028Z 2024-12-18T01:36:48.2519212Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-12-18T01:36:48.2519623Z precision, but buffers are not. 2024-12-18T01:36:48.2519831Z 2024-12-18T01:36:48.2520047Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-12-18T01:36:48.2520687Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-12-18T01:36:48.2521246Z Disabling FSDP's mixed precision for those norm modules only means that 2024-12-18T01:36:48.2521807Z the affine parameters are kept in ``float32``. However, this incurs 2024-12-18T01:36:48.2522370Z separate all-gathers and reduce-scatters for those norm modules, which 2024-12-18T01:36:48.2522939Z may be inefficient, so if the workload permits, the user should prefer 2024-12-18T01:36:48.2523419Z to still apply mixed precision to those modules. 2024-12-18T01:36:48.2523674Z 2024-12-18T01:36:48.2523879Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-12-18T01:36:48.2524403Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-12-18T01:36:48.2524946Z modules will have FSDP applied to them separately with mixed precision 2024-12-18T01:36:48.2525468Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-12-18T01:36:48.2525750Z 2024-12-18T01:36:48.2525973Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-12-18T01:36:48.2526542Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-12-18T01:36:48.2527034Z its ``cast_root_forward_inputs`` takes precedence over its 2024-12-18T01:36:48.2527507Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-12-18T01:36:48.2528021Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-12-18T01:36:48.2528574Z sufficient for the typical case where each FSDP instance has the same 2024-12-18T01:36:48.2529138Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-12-18T01:36:48.2529660Z ``param_dtype`` at the beginning of the model's forward pass. 2024-12-18T01:36:48.2529948Z 2024-12-18T01:36:48.2530153Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-12-18T01:36:48.2530737Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-12-18T01:36:48.2531294Z values to configure casting inputs or not before each instance's 2024-12-18T01:36:48.2531806Z forward. In such a case, since the casts happen before each FSDP 2024-12-18T01:36:48.2532342Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-12-18T01:36:48.2532901Z submodules run before its FSDP submodules to avoid the activation dtype 2024-12-18T01:36:48.2533488Z being changed due to a different ``MixedPrecision`` configuration. 2024-12-18T01:36:48.2533808Z 2024-12-18T01:36:48.2533904Z Example:: 2024-12-18T01:36:48.2534052Z 2024-12-18T01:36:48.2534186Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:48.2534608Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-12-18T01:36:48.2534995Z >>> model[1] = FSDP( 2024-12-18T01:36:48.2535274Z >>> model[1], 2024-12-18T01:36:48.2535736Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-12-18T01:36:48.2536279Z >>> ) 2024-12-18T01:36:48.2536518Z >>> model = FSDP( 2024-12-18T01:36:48.2536788Z >>> model, 2024-12-18T01:36:48.2537249Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-12-18T01:36:48.2537761Z >>> ) 2024-12-18T01:36:48.2537895Z 2024-12-18T01:36:48.2538107Z The above shows a working example. On the other hand, if ``model[1]`` 2024-12-18T01:36:48.2538638Z were replaced with ``model[0]``, meaning that the submodule using 2024-12-18T01:36:48.2539180Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-12-18T01:36:48.2539742Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-12-18T01:36:48.2540167Z ones. 2024-12-18T01:36:48.2540287Z 2024-12-18T01:36:48.2540291Z 2024-12-18T01:36:48.2540550Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.2540912Z 2024-12-18T01:36:48.2634358Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=649. 2024-12-18T01:36:48.2635561Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.2636250Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:36:48.2636624Z 2024-12-18T01:36:48.2636881Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-12-18T01:36:48.2637464Z The target module does not have to be a FSDP module. If the target 2024-12-18T01:36:48.2638012Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-12-18T01:36:48.2638490Z 2024-12-18T01:36:48.2638709Z .. note:: This API should be called for only the top-level (root) 2024-12-18T01:36:48.2639117Z module. 2024-12-18T01:36:48.2639258Z 2024-12-18T01:36:48.2639466Z .. note:: This API enables users to transparently use the conventional 2024-12-18T01:36:48.2640085Z ``state_dict`` API to take model checkpoints in cases where the 2024-12-18T01:36:48.2640605Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-12-18T01:36:48.2641142Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-12-18T01:36:48.2641700Z instances, while dispatching into `sharded_state_dict` implementation 2024-12-18T01:36:48.2642149Z for FSDP: 2024-12-18T01:36:48.2642301Z 2024-12-18T01:36:48.2642396Z Example:: 2024-12-18T01:36:48.2642555Z 2024-12-18T01:36:48.2642691Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:48.2643055Z >>> model = DDP(FSDP(...)) 2024-12-18T01:36:48.2643382Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:48.2643745Z >>> model, 2024-12-18T01:36:48.2644039Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:36:48.2644503Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-12-18T01:36:48.2645067Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-12-18T01:36:48.2645500Z >>> ) 2024-12-18T01:36:48.2645774Z >>> param_state_dict = model.state_dict() 2024-12-18T01:36:48.2646234Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:36:48.2646517Z 2024-12-18T01:36:48.2646607Z Args: 2024-12-18T01:36:48.2646872Z module (torch.nn.Module): Root module. 2024-12-18T01:36:48.2647346Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:36:48.2647942Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-12-18T01:36:48.2648420Z target ``state_dict_type``. 2024-12-18T01:36:48.2648881Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-12-18T01:36:48.2649411Z for the optimizer state dict. 2024-12-18T01:36:48.2649650Z 2024-12-18T01:36:48.2649740Z Returns: 2024-12-18T01:36:48.2650108Z A StateDictSettings that include the previous state_dict type and 2024-12-18T01:36:48.2650557Z configuration for the module. 2024-12-18T01:36:48.2650871Z 2024-12-18T01:36:48.2651231Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.2651607Z 2024-12-18T01:36:48.2652399Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=805. 2024-12-18T01:36:48.2653542Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.2654158Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:36:48.2654529Z 2024-12-18T01:36:48.2654840Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-12-18T01:36:48.2655401Z :meth:`set_state_dict_type` for the detail. 2024-12-18T01:36:48.2655638Z 2024-12-18T01:36:48.2655745Z Example:: 2024-12-18T01:36:48.2655890Z 2024-12-18T01:36:48.2656024Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:48.2656384Z >>> model = DDP(FSDP(...)) 2024-12-18T01:36:48.2656713Z >>> with FSDP.state_dict_type( 2024-12-18T01:36:48.2657027Z >>> model, 2024-12-18T01:36:48.2657334Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:36:48.2657656Z >>> ): 2024-12-18T01:36:48.2657925Z >>> checkpoint = model.state_dict() 2024-12-18T01:36:48.2658167Z 2024-12-18T01:36:48.2658258Z Args: 2024-12-18T01:36:48.2658521Z module (torch.nn.Module): Root module. 2024-12-18T01:36:48.2659000Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:36:48.2659613Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-12-18T01:36:48.2660119Z configuration for the target ``state_dict_type``. 2024-12-18T01:36:48.2660629Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-12-18T01:36:48.2661186Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-12-18T01:36:48.2661593Z 2024-12-18T01:36:48.2661971Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.2662329Z 2024-12-18T01:36:48.2692835Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1818. 2024-12-18T01:36:48.2694058Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.2694426Z 2024-12-18T01:36:48.2694665Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-12-18T01:36:48.2695028Z 2024-12-18T01:36:48.2695219Z The given state-dict can be transformed to one of three types: 2024-12-18T01:36:48.2696004Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-12-18T01:36:48.2696429Z 2024-12-18T01:36:48.2696802Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-12-18T01:36:48.2697670Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-12-18T01:36:48.2698252Z avoid OOM. 2024-12-18T01:36:48.2698379Z 2024-12-18T01:36:48.2698613Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-12-18T01:36:48.2699174Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-12-18T01:36:48.2699589Z memory. 2024-12-18T01:36:48.2699726Z 2024-12-18T01:36:48.2699945Z For local state_dict, no transformation will be performed. But a state 2024-12-18T01:36:48.2700587Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-12-18T01:36:48.2701061Z nature (this is not supported yet). 2024-12-18T01:36:48.2701269Z 2024-12-18T01:36:48.2701372Z Example:: 2024-12-18T01:36:48.2701514Z 2024-12-18T01:36:48.2701642Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:48.2702129Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:36:48.2702648Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:36:48.2703104Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:36:48.2703604Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:36:48.2704014Z >>> # Save a checkpoint 2024-12-18T01:36:48.2704302Z >>> model, optim = ... 2024-12-18T01:36:48.2704591Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:48.2704927Z >>> model, 2024-12-18T01:36:48.2705197Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:48.2705567Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2705949Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2706305Z >>> ) 2024-12-18T01:36:48.2706546Z >>> state_dict = model.state_dict() 2024-12-18T01:36:48.2706933Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:36:48.2707361Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:36:48.2707703Z >>> # Load a checkpoint 2024-12-18T01:36:48.2707982Z >>> model, optim = ... 2024-12-18T01:36:48.2708309Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:36:48.2708682Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:48.2708973Z >>> model, 2024-12-18T01:36:48.2709224Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:48.2709582Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2709980Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2710445Z >>> ) 2024-12-18T01:36:48.2710695Z >>> model.load_state_dict(state_dict) 2024-12-18T01:36:48.2711119Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:36:48.2711501Z >>> model, optim, optim_state_dict 2024-12-18T01:36:48.2711814Z >>> ) 2024-12-18T01:36:48.2712068Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:36:48.2712298Z 2024-12-18T01:36:48.2712403Z Args: 2024-12-18T01:36:48.2712713Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:36:48.2713227Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:36:48.2713677Z were passed into the optimizer ``optim``. 2024-12-18T01:36:48.2714110Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:36:48.2714499Z parameters. 2024-12-18T01:36:48.2714867Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-12-18T01:36:48.2715455Z transform. If the value is None, optim.state_dict() will be used. ( 2024-12-18T01:36:48.2715968Z Default: ``None``) 2024-12-18T01:36:48.2716393Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:36:48.2716937Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:36:48.2717341Z Default: ``None``) 2024-12-18T01:36:48.2717512Z 2024-12-18T01:36:48.2717605Z Returns: 2024-12-18T01:36:48.2717974Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-12-18T01:36:48.2718453Z ``model``. The sharding of the optimizer state is based on 2024-12-18T01:36:48.2718837Z ``state_dict_type``. 2024-12-18T01:36:48.2719004Z 2024-12-18T01:36:48.2719268Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.2719628Z 2024-12-18T01:36:48.2720458Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1916. 2024-12-18T01:36:48.2721652Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.2722033Z 2024-12-18T01:36:48.2722374Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-12-18T01:36:48.2722844Z 2024-12-18T01:36:48.2723013Z Given a ``optim_state_dict`` that is transformed through 2024-12-18T01:36:48.2723503Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-12-18T01:36:48.2724036Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-12-18T01:36:48.2724552Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-12-18T01:36:48.2724850Z 2024-12-18T01:36:48.2724992Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:48.2725457Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:36:48.2725974Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:36:48.2726429Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:36:48.2726927Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:36:48.2727346Z >>> # Save a checkpoint 2024-12-18T01:36:48.2727616Z >>> model, optim = ... 2024-12-18T01:36:48.2727899Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:48.2728189Z >>> model, 2024-12-18T01:36:48.2728456Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:48.2728813Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2729191Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2729537Z >>> ) 2024-12-18T01:36:48.2729775Z >>> state_dict = model.state_dict() 2024-12-18T01:36:48.2730114Z >>> original_osd = optim.state_dict() 2024-12-18T01:36:48.2730470Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-12-18T01:36:48.2730789Z >>> model, 2024-12-18T01:36:48.2731024Z >>> optim, 2024-12-18T01:36:48.2731283Z >>> optim_state_dict=original_osd 2024-12-18T01:36:48.2731628Z >>> ) 2024-12-18T01:36:48.2731905Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:36:48.2732251Z >>> # Load a checkpoint 2024-12-18T01:36:48.2732538Z >>> model, optim = ... 2024-12-18T01:36:48.2732865Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:36:48.2733239Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:48.2733531Z >>> model, 2024-12-18T01:36:48.2733784Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:48.2734141Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2734531Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:48.2734881Z >>> ) 2024-12-18T01:36:48.2735121Z >>> model.load_state_dict(state_dict) 2024-12-18T01:36:48.2735494Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:36:48.2735905Z >>> model, optim, optim_state_dict 2024-12-18T01:36:48.2736222Z >>> ) 2024-12-18T01:36:48.2736482Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:36:48.2736715Z 2024-12-18T01:36:48.2736816Z Args: 2024-12-18T01:36:48.2737125Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:36:48.2737640Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:36:48.2738099Z were passed into the optimizer ``optim``. 2024-12-18T01:36:48.2738566Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:36:48.2738966Z parameters. 2024-12-18T01:36:48.2739321Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-12-18T01:36:48.2739861Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-12-18T01:36:48.2740378Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-12-18T01:36:48.2740870Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-12-18T01:36:48.2741364Z load_directly (bool): If this is set to True, this API will also 2024-12-18T01:36:48.2741909Z call optim.load_state_dict(result) before returning the result. 2024-12-18T01:36:48.2742436Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-12-18T01:36:48.2742876Z (Default: ``False``) 2024-12-18T01:36:48.2743303Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:36:48.2743850Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:36:48.2744246Z Default: ``None``) 2024-12-18T01:36:48.2744528Z 2024-12-18T01:36:48.2744789Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.2745146Z 2024-12-18T01:36:48.3210683Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-12-18T01:36:48.3211681Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.3212063Z 2024-12-18T01:36:48.3212290Z RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:36:48.3212634Z 2024-12-18T01:36:48.3212828Z It creates a user-specified module on a specified remote node. 2024-12-18T01:36:48.3213363Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:36:48.3213820Z executed on the remote node. 2024-12-18T01:36:48.3214245Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:36:48.3214735Z gradients back to the corresponding remote module. 2024-12-18T01:36:48.3215359Z It can be shared across processors using `RPC framework `__, 2024-12-18T01:36:48.3216011Z without incurring any overheads of copying the actual module, 2024-12-18T01:36:48.3216521Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-12-18T01:36:48.3216948Z pointing to the remote module. 2024-12-18T01:36:48.3217139Z 2024-12-18T01:36:48.3217351Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-12-18T01:36:48.3217961Z the ``forward`` method of the module returned by the ``module_cls``. 2024-12-18T01:36:48.3218282Z 2024-12-18T01:36:48.3218583Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-12-18T01:36:48.3219242Z 2024-12-18T01:36:48.3219495Z Particularly, to create a hybrid model, typically the local modules should be 2024-12-18T01:36:48.3220287Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-12-18T01:36:48.3220864Z Hybrid Example: 2024-12-18T01:36:48.3221166Z >>> class HybridModel(nn.Module): 2024-12-18T01:36:48.3221512Z >>> def __init__(self) -> None: 2024-12-18T01:36:48.3221852Z >>> nn.Module.__init__(self) 2024-12-18T01:36:48.3222222Z >>> self.remote_embedding = RemoteModule(...) 2024-12-18T01:36:48.3222664Z >>> self.local_linear = nn.Linear(...) 2024-12-18T01:36:48.3222914Z 2024-12-18T01:36:48.3223118Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:36:48.3223690Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:36:48.3224270Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-12-18T01:36:48.3224767Z ``def forward(input: Tensor) -> Tensor:`` and 2024-12-18T01:36:48.3225166Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-12-18T01:36:48.3225446Z 2024-12-18T01:36:48.3225558Z .. note:: 2024-12-18T01:36:48.3225836Z If the remote module is placed on a cuda device, 2024-12-18T01:36:48.3226333Z any input CPU tensors will be automatically moved to the same cuda device, 2024-12-18T01:36:48.3227067Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-12-18T01:36:48.3227612Z 2024-12-18T01:36:48.3227769Z Args: 2024-12-18T01:36:48.3228487Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:36:48.3229620Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:36:48.3230278Z formats: 2024-12-18T01:36:48.3230432Z 2024-12-18T01:36:48.3230574Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:36:48.3230986Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:36:48.3231248Z 2024-12-18T01:36:48.3231505Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:36:48.3231974Z module_cls (nn.Module): For example, 2024-12-18T01:36:48.3232314Z >>> class MyModule(nn.Module): 2024-12-18T01:36:48.3232630Z >>> def forward(input): 2024-12-18T01:36:48.3232933Z >>> return input + 1 2024-12-18T01:36:48.3233225Z >>> 2024-12-18T01:36:48.3233445Z >>> module_cls = MyModule 2024-12-18T01:36:48.3233846Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:36:48.3234349Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:36:48.3234925Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:36:48.3235553Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:36:48.3236221Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:36:48.3236878Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:36:48.3237236Z 2024-12-18T01:36:48.3237324Z Returns: 2024-12-18T01:36:48.3237696Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:36:48.3238280Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:36:48.3238894Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:36:48.3239429Z on the user-provided module on the remote side. 2024-12-18T01:36:48.3239737Z 2024-12-18T01:36:48.3239836Z Example:: 2024-12-18T01:36:48.3240125Z Run the following code in two different processes: 2024-12-18T01:36:48.3240397Z 2024-12-18T01:36:48.3240514Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:48.3240833Z >>> # On worker 0: 2024-12-18T01:36:48.3241088Z >>> import torch 2024-12-18T01:36:48.3241361Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:48.3241707Z >>> from torch import nn, Tensor 2024-12-18T01:36:48.3242138Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:36:48.3242564Z >>> 2024-12-18T01:36:48.3242829Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:36:48.3243190Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:36:48.3243550Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:36:48.3243911Z >>> ) 2024-12-18T01:36:48.3244150Z >>> input = torch.randn(128, 20) 2024-12-18T01:36:48.3244521Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:36:48.3244882Z >>> ret = ret_fut.wait() 2024-12-18T01:36:48.3245172Z >>> rpc.shutdown() 2024-12-18T01:36:48.3245343Z 2024-12-18T01:36:48.3245438Z >>> # On worker 1: 2024-12-18T01:36:48.3245697Z >>> import torch 2024-12-18T01:36:48.3246016Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:48.3246336Z >>> 2024-12-18T01:36:48.3246604Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:36:48.3246956Z >>> rpc.shutdown() 2024-12-18T01:36:48.3247115Z 2024-12-18T01:36:48.3247377Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.3247737Z 2024-12-18T01:36:48.3248428Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-12-18T01:36:48.3249505Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.3249873Z 2024-12-18T01:36:48.3250201Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-12-18T01:36:48.3250631Z 2024-12-18T01:36:48.3250947Z This alternate initialization method can be particularly useful if we want to create multiple 2024-12-18T01:36:48.3251692Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-12-18T01:36:48.3252200Z 2024-12-18T01:36:48.3252473Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-12-18T01:36:48.3253045Z which is not supported. The recommended way is as follows: 2024-12-18T01:36:48.3253330Z 2024-12-18T01:36:48.3253460Z 1. the sender creates a RemoteModule; 2024-12-18T01:36:48.3253832Z 2. the sender sends its ``module_rref`` over RPC; 2024-12-18T01:36:48.3254406Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-12-18T01:36:48.3254858Z 2024-12-18T01:36:48.3254956Z Example:: 2024-12-18T01:36:48.3255244Z Run the following code in two different processes: 2024-12-18T01:36:48.3255509Z 2024-12-18T01:36:48.3255627Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:48.3255945Z >>> # On worker 0: 2024-12-18T01:36:48.3256201Z >>> import torch 2024-12-18T01:36:48.3256483Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:48.3256830Z >>> from torch import nn, Tensor 2024-12-18T01:36:48.3257263Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:36:48.3257687Z >>> 2024-12-18T01:36:48.3257954Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:36:48.3258307Z >>> remote_module = RemoteModule( 2024-12-18T01:36:48.3258650Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:36:48.3259039Z >>> ) 2024-12-18T01:36:48.3259259Z >>> 2024-12-18T01:36:48.3259373Z >>> remote_module1 = rpc.rpc_sync( 2024-12-18T01:36:48.3259485Z >>> "worker1/cpu", 2024-12-18T01:36:48.3259674Z >>> RemoteModule.init_from_module_rref, 2024-12-18T01:36:48.3259832Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-12-18T01:36:48.3259933Z >>> ) 2024-12-18T01:36:48.3260028Z >>> rpc.shutdown() 2024-12-18T01:36:48.3260033Z 2024-12-18T01:36:48.3260139Z >>> # On worker 1: 2024-12-18T01:36:48.3260233Z >>> import torch 2024-12-18T01:36:48.3260356Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:48.3260457Z >>> 2024-12-18T01:36:48.3260597Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:36:48.3260705Z >>> rpc.shutdown() 2024-12-18T01:36:48.3260710Z 2024-12-18T01:36:48.3260796Z Args: 2024-12-18T01:36:48.3261098Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:36:48.3261410Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:36:48.3261501Z formats: 2024-12-18T01:36:48.3261508Z 2024-12-18T01:36:48.3261666Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:36:48.3261815Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:36:48.3261820Z 2024-12-18T01:36:48.3262073Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:36:48.3262344Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-12-18T01:36:48.3262467Z the created remote module. 2024-12-18T01:36:48.3262737Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:36:48.3262981Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:36:48.3263200Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:36:48.3263432Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:36:48.3263436Z 2024-12-18T01:36:48.3263568Z Returns: 2024-12-18T01:36:48.3263810Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:36:48.3264051Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-12-18T01:36:48.3264314Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:36:48.3264469Z on the user-provided module on the remote side. 2024-12-18T01:36:48.3264474Z 2024-12-18T01:36:48.3264719Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.3264724Z 2024-12-18T01:36:48.3265356Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-12-18T01:36:48.3265613Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.3265619Z 2024-12-18T01:36:48.3265863Z A RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:36:48.3265869Z 2024-12-18T01:36:48.3266063Z It creates a user-specified module on a specified remote node. 2024-12-18T01:36:48.3266307Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:36:48.3266416Z executed on the remote node. 2024-12-18T01:36:48.3266663Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:36:48.3266888Z gradients back to the corresponding remote module. 2024-12-18T01:36:48.3266893Z 2024-12-18T01:36:48.3267110Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-12-18T01:36:48.3267334Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-12-18T01:36:48.3267578Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-12-18T01:36:48.3267789Z and ``forward`` are the same as the ``forward`` method of the module 2024-12-18T01:36:48.3267902Z returned by the ``module_cls``. 2024-12-18T01:36:48.3267908Z 2024-12-18T01:36:48.3268146Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:36:48.3268392Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:36:48.3268616Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-12-18T01:36:48.3268634Z 2024-12-18T01:36:48.3268764Z | ``def forward(input: Tensor) -> Tensor:`` 2024-12-18T01:36:48.3268930Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-12-18T01:36:48.3268935Z 2024-12-18T01:36:48.3269036Z Args: 2024-12-18T01:36:48.3269322Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:36:48.3269665Z The format should be "/", where the device field can be parsed as torch.device type. 2024-12-18T01:36:48.3269834Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-12-18T01:36:48.3270088Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:36:48.3270333Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-12-18T01:36:48.3270338Z 2024-12-18T01:36:48.3270448Z >>> class MyModule(nn.Module): 2024-12-18T01:36:48.3270562Z >>> def forward(input): 2024-12-18T01:36:48.3270702Z >>> return input + 1 2024-12-18T01:36:48.3270802Z >>> 2024-12-18T01:36:48.3270903Z >>> module_cls = MyModule 2024-12-18T01:36:48.3270908Z 2024-12-18T01:36:48.3271114Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:36:48.3271302Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:36:48.3271307Z 2024-12-18T01:36:48.3271395Z Returns: 2024-12-18T01:36:48.3271641Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:36:48.3271870Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:36:48.3272168Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:36:48.3272312Z on the user-provided module on the remote side. 2024-12-18T01:36:48.3272317Z 2024-12-18T01:36:48.3272425Z Example:: 2024-12-18T01:36:48.3272577Z Run the following code in two different processes: 2024-12-18T01:36:48.3272581Z 2024-12-18T01:36:48.3272697Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:48.3272804Z >>> # On worker 0: 2024-12-18T01:36:48.3272898Z >>> import torch 2024-12-18T01:36:48.3273036Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:48.3273142Z >>> from torch import nn, Tensor 2024-12-18T01:36:48.3273376Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:36:48.3273464Z >>> 2024-12-18T01:36:48.3273604Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:36:48.3273742Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:36:48.3273870Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:36:48.3273970Z >>> ) 2024-12-18T01:36:48.3274076Z >>> input = torch.randn(128, 20) 2024-12-18T01:36:48.3274229Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:36:48.3274342Z >>> ret = ret_fut.wait() 2024-12-18T01:36:48.3274441Z >>> rpc.shutdown() 2024-12-18T01:36:48.3274445Z 2024-12-18T01:36:48.3274556Z >>> # On worker 1: 2024-12-18T01:36:48.3274650Z >>> import torch 2024-12-18T01:36:48.3274792Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:48.3274881Z >>> 2024-12-18T01:36:48.3275021Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:36:48.3275130Z >>> rpc.shutdown() 2024-12-18T01:36:48.3275134Z 2024-12-18T01:36:48.3275324Z Furthermore, a more practical example that is combined with 2024-12-18T01:36:48.3275892Z `DistributedDataParallel `__ (DDP) 2024-12-18T01:36:48.3276221Z can be found in this `tutorial `__. 2024-12-18T01:36:48.3276258Z 2024-12-18T01:36:48.3276521Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.3276525Z 2024-12-18T01:36:48.3452010Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-12-18T01:36:48.3452289Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.3452294Z 2024-12-18T01:36:48.3452532Z DistributedOptimizer takes remote references to parameters scattered 2024-12-18T01:36:48.3452782Z across workers and applies the given optimizer locally for each parameter. 2024-12-18T01:36:48.3452786Z 2024-12-18T01:36:48.3453021Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-12-18T01:36:48.3453269Z to retrieve the gradients for specific parameters. 2024-12-18T01:36:48.3453289Z 2024-12-18T01:36:48.3453392Z Concurrent calls to 2024-12-18T01:36:48.3453606Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-12-18T01:36:48.3453765Z either from the same or different clients, will 2024-12-18T01:36:48.3453996Z be serialized on each worker -- as each worker's optimizer can only work 2024-12-18T01:36:48.3454260Z on one set of gradients at a time. However, there is no guarantee that 2024-12-18T01:36:48.3454503Z the full forward-backward-optimizer sequence will execute for one client 2024-12-18T01:36:48.3454718Z at a time. This means that the gradients being applied may not correspond 2024-12-18T01:36:48.3454970Z to the latest forward pass executed on a given worker. Also, there is no 2024-12-18T01:36:48.3455100Z guaranteed ordering across workers. 2024-12-18T01:36:48.3455104Z 2024-12-18T01:36:48.3455362Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-12-18T01:36:48.3455626Z by default, so that optimizer updates are not blocked by the Python Global 2024-12-18T01:36:48.3455881Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-12-18T01:36:48.3456118Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-12-18T01:36:48.3456380Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-12-18T01:36:48.3456493Z for your own custom optimizers. 2024-12-18T01:36:48.3456498Z 2024-12-18T01:36:48.3456600Z Args: 2024-12-18T01:36:48.3456800Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-12-18T01:36:48.3456911Z instantiate on each worker. 2024-12-18T01:36:48.3457135Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-12-18T01:36:48.3457233Z to optimize. 2024-12-18T01:36:48.3457458Z args: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:36:48.3457679Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:36:48.3457683Z 2024-12-18T01:36:48.3457803Z Example:: 2024-12-18T01:36:48.3457923Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:48.3458092Z >>> import torch.distributed.autograd as dist_autograd 2024-12-18T01:36:48.3458233Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:48.3458340Z >>> from torch import optim 2024-12-18T01:36:48.3458634Z >>> from torch.distributed.optim import DistributedOptimizer 2024-12-18T01:36:48.3458723Z >>> 2024-12-18T01:36:48.3458860Z >>> with dist_autograd.context() as context_id: 2024-12-18T01:36:48.3458978Z >>> # Forward pass. 2024-12-18T01:36:48.3459179Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-12-18T01:36:48.3459386Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-12-18T01:36:48.3459511Z >>> loss = rref1.to_here() + rref2.to_here() 2024-12-18T01:36:48.3459619Z >>> 2024-12-18T01:36:48.3459718Z >>> # Backward pass. 2024-12-18T01:36:48.3459873Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-12-18T01:36:48.3460009Z >>> 2024-12-18T01:36:48.3460103Z >>> # Optimizer. 2024-12-18T01:36:48.3460243Z >>> dist_optim = DistributedOptimizer( 2024-12-18T01:36:48.3460336Z >>> optim.SGD, 2024-12-18T01:36:48.3460435Z >>> [rref1, rref2], 2024-12-18T01:36:48.3460535Z >>> lr=0.05, 2024-12-18T01:36:48.3460625Z >>> ) 2024-12-18T01:36:48.3460750Z >>> dist_optim.step(context_id) 2024-12-18T01:36:48.3460755Z 2024-12-18T01:36:48.3460913Z __ https://github.com/pytorch/tutorials/pull/1465 2024-12-18T01:36:48.3460918Z 2024-12-18T01:36:48.3461181Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.3461185Z 2024-12-18T01:36:48.3470897Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-12-18T01:36:48.3471310Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.3471317Z 2024-12-18T01:36:48.3471697Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-12-18T01:36:48.3471860Z This optimizer runs local optimizer at every step. 2024-12-18T01:36:48.3472216Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-12-18T01:36:48.3472221Z 2024-12-18T01:36:48.3472307Z Args: 2024-12-18T01:36:48.3472427Z optim: The local optimizer. 2024-12-18T01:36:48.3472643Z averager: A model averager instance to run post-localSGD algorithm. 2024-12-18T01:36:48.3472647Z 2024-12-18T01:36:48.3472761Z Example:: 2024-12-18T01:36:48.3472766Z 2024-12-18T01:36:48.3472891Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:48.3472999Z >>> import torch 2024-12-18T01:36:48.3473120Z >>> import torch.distributed as dist 2024-12-18T01:36:48.3473437Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:36:48.3473558Z >>> import torch.nn as nn 2024-12-18T01:36:48.3473753Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-12-18T01:36:48.3474039Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:36:48.3474146Z >>> PostLocalSGDState, 2024-12-18T01:36:48.3474262Z >>> post_localSGD_hook, 2024-12-18T01:36:48.3474349Z >>> ) 2024-12-18T01:36:48.3474434Z >>> 2024-12-18T01:36:48.3474606Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:36:48.3474749Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:36:48.3474849Z >>> ) 2024-12-18T01:36:48.3474935Z >>> 2024-12-18T01:36:48.3475081Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:36:48.3475386Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:36:48.3475561Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:48.3475742Z >>> 2024-12-18T01:36:48.3475949Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-12-18T01:36:48.3476210Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-12-18T01:36:48.3476374Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:36:48.3476597Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-12-18T01:36:48.3476715Z >>> opt = PostLocalSGDOptimizer( 2024-12-18T01:36:48.3476818Z >>> optim=local_optim, 2024-12-18T01:36:48.3477077Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:36:48.3477164Z >>> ) 2024-12-18T01:36:48.3477265Z >>> 2024-12-18T01:36:48.3477488Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-12-18T01:36:48.3477873Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-12-18T01:36:48.3478258Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-12-18T01:36:48.3478400Z >>> for step in range(0, 200): 2024-12-18T01:36:48.3478511Z >>> opt.zero_grad() 2024-12-18T01:36:48.3478623Z >>> loss = loss_fn(output, labels) 2024-12-18T01:36:48.3478735Z >>> loss.backward() 2024-12-18T01:36:48.3478833Z >>> opt.step() 2024-12-18T01:36:48.3478838Z 2024-12-18T01:36:48.3479090Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.3479110Z 2024-12-18T01:36:48.3586098Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-12-18T01:36:48.3586664Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.3586873Z 2024-12-18T01:36:48.3587649Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-12-18T01:36:48.3587665Z 2024-12-18T01:36:48.3587913Z The sharing is done as described by ZeRO_. 2024-12-18T01:36:48.3587922Z 2024-12-18T01:36:48.3588192Z The local optimizer instance in each rank is only 2024-12-18T01:36:48.3588661Z responsible for updating approximately ``1 / world_size`` parameters and 2024-12-18T01:36:48.3589105Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-12-18T01:36:48.3589569Z parameters are updated locally, each rank will broadcast its parameters to 2024-12-18T01:36:48.3589926Z all other peers to keep all model replicas in the same state. 2024-12-18T01:36:48.3590288Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-12-18T01:36:48.3590793Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-12-18T01:36:48.3590976Z memory consumption. 2024-12-18T01:36:48.3590985Z 2024-12-18T01:36:48.3591554Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-12-18T01:36:48.3591994Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-12-18T01:36:48.3592449Z not divided among ranks. The partition is arbitrary and might not match the 2024-12-18T01:36:48.3592689Z the parameter registration or usage order. 2024-12-18T01:36:48.3592699Z 2024-12-18T01:36:48.3592863Z Arguments: 2024-12-18T01:36:48.3593224Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-12-18T01:36:48.3593565Z or :class:`dict` s giving all parameters, which will be sharded 2024-12-18T01:36:48.3593749Z across ranks. 2024-12-18T01:36:48.3593758Z 2024-12-18T01:36:48.3593921Z Keyword Args: 2024-12-18T01:36:48.3594347Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-12-18T01:36:48.3594513Z optimizer. 2024-12-18T01:36:48.3594910Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-12-18T01:36:48.3595304Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-12-18T01:36:48.3595578Z :meth:`torch.distributed.init_process_group`). 2024-12-18T01:36:48.3596098Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-12-18T01:36:48.3596493Z packed into buckets to speed up communication, and ``param.data`` 2024-12-18T01:36:48.3596873Z fields point to bucket views at different offsets; if ``False``, 2024-12-18T01:36:48.3597245Z each individual parameter is communicated separately, and each 2024-12-18T01:36:48.3597517Z ``params.data`` stays intact (default: ``False``). 2024-12-18T01:36:48.3598076Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-12-18T01:36:48.3598443Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-12-18T01:36:48.3598843Z synchronization; this requires (1) either a functional optimizer 2024-12-18T01:36:48.3599179Z for the ``optimizer_class`` argument or one with a functional 2024-12-18T01:36:48.3599525Z equivalent and (2) registering a DDP communication hook 2024-12-18T01:36:48.3600008Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-12-18T01:36:48.3600307Z parameters are packed into buckets matching those in 2024-12-18T01:36:48.3600606Z :class:`DistributedDataParallel`, meaning that the 2024-12-18T01:36:48.3600887Z ``parameters_as_bucket_view`` argument is ignored. 2024-12-18T01:36:48.3601474Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-12-18T01:36:48.3601650Z (per normal). 2024-12-18T01:36:48.3601822Z (default: ``False``) 2024-12-18T01:36:48.3602227Z **defaults: any trailing arguments, which are forwarded to the local 2024-12-18T01:36:48.3602383Z optimizer. 2024-12-18T01:36:48.3602392Z 2024-12-18T01:36:48.3602583Z Example:: 2024-12-18T01:36:48.3602693Z 2024-12-18T01:36:48.3602871Z >>> # xdoctest: +SKIP 2024-12-18T01:36:48.3603067Z >>> import torch.nn as nn 2024-12-18T01:36:48.3603447Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-12-18T01:36:48.3603828Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-12-18T01:36:48.3604260Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-12-18T01:36:48.3604463Z >>> ddp = DDP(model, device_ids=[rank]) 2024-12-18T01:36:48.3604765Z >>> opt = ZeroRedundancyOptimizer( 2024-12-18T01:36:48.3604951Z >>> ddp.parameters(), 2024-12-18T01:36:48.3605179Z >>> optimizer_class=torch.optim.Adam, 2024-12-18T01:36:48.3605327Z >>> lr=0.01 2024-12-18T01:36:48.3605478Z >>> ) 2024-12-18T01:36:48.3605677Z >>> ddp(inputs).sum().backward() 2024-12-18T01:36:48.3605840Z >>> opt.step() 2024-12-18T01:36:48.3605849Z 2024-12-18T01:36:48.3606027Z .. warning:: 2024-12-18T01:36:48.3606421Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-12-18T01:36:48.3606699Z passed-in parameters are the same dense type. 2024-12-18T01:36:48.3606771Z 2024-12-18T01:36:48.3606942Z .. warning:: 2024-12-18T01:36:48.3607332Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-12-18T01:36:48.3607722Z the way that overlapping :class:`DistributedDataParallel` with 2024-12-18T01:36:48.3608155Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-12-18T01:36:48.3608569Z two or three training iterations do not perform parameter updates in 2024-12-18T01:36:48.3608916Z the optimizer step, depending on if ``static_graph=False`` or 2024-12-18T01:36:48.3609269Z ``static_graph=True``, respectively. This is because it needs 2024-12-18T01:36:48.3609614Z information about the gradient bucketing strategy used by 2024-12-18T01:36:48.3610035Z :class:`DistributedDataParallel`, which is not finalized until the 2024-12-18T01:36:48.3610425Z second forward pass if ``static_graph=False`` or until the third 2024-12-18T01:36:48.3610821Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-12-18T01:36:48.3611032Z is to prepend dummy inputs. 2024-12-18T01:36:48.3611041Z 2024-12-18T01:36:48.3611525Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-12-18T01:36:48.3611534Z 2024-12-18T01:36:48.3611781Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-12-18T01:36:48.3611790Z 2024-12-18T01:36:48.3611801Z 2024-12-18T01:36:48.3612270Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.3612279Z 2024-12-18T01:36:48.3818813Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-12-18T01:36:48.3819328Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.3819339Z 2024-12-18T01:36:48.3819798Z Custom reducer class that can be used to specify a custom operation that 2024-12-18T01:36:48.3820102Z reduces losses of multiple microbatches into one value. 2024-12-18T01:36:48.3820279Z 2024-12-18T01:36:48.3820431Z Example: 2024-12-18T01:36:48.3820614Z >>> # xdoctest: +SKIP 2024-12-18T01:36:48.3820794Z >>> sum_reducer = _CustomReducer( 2024-12-18T01:36:48.3820967Z >>> torch.tensor(0.0), 2024-12-18T01:36:48.3821135Z >>> lambda a, b: a + b 2024-12-18T01:36:48.3821295Z >>> ) 2024-12-18T01:36:48.3821304Z 2024-12-18T01:36:48.3821774Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.3821784Z 2024-12-18T01:36:48.4303858Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2024-12-18T01:36:48.4304828Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.4305201Z 2024-12-18T01:36:48.4305444Z A decorator for a function indicating that the return value of the function 2024-12-18T01:36:48.4306166Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-12-18T01:36:48.4306744Z function can run asynchronously on the RPC callee. More specifically, the 2024-12-18T01:36:48.4307339Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-12-18T01:36:48.4307931Z function and installs subsequent processing steps as a callback to that 2024-12-18T01:36:48.4308601Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-12-18T01:36:48.4309560Z from the :class:`~torch.futures.Future` when completed and send the 2024-12-18T01:36:48.4310453Z value back as the RPC response. That also means the returned 2024-12-18T01:36:48.4311415Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-12-18T01:36:48.4312463Z sent through RPC. This decorator is useful when the wrapped function's 2024-12-18T01:36:48.4313463Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-12-18T01:36:48.4314441Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-12-18T01:36:48.4315214Z 2024-12-18T01:36:48.4315733Z .. note:: To enable asynchronous execution, applications must pass the 2024-12-18T01:36:48.4316766Z function object returned by this decorator to RPC APIs. If RPC detected 2024-12-18T01:36:48.4317779Z attributes installed by this decorator, it knows that this function 2024-12-18T01:36:48.4318757Z returns a ``Future`` object and will handle that accordingly. 2024-12-18T01:36:48.4319692Z However, this does not mean this decorator has to be outmost one when 2024-12-18T01:36:48.4320725Z defining a function. For example, when combined with ``@staticmethod`` 2024-12-18T01:36:48.4321777Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-12-18T01:36:48.4322812Z inner decorator to allow the target function be recognized as a static 2024-12-18T01:36:48.4323852Z or class function. This target function can still execute asynchronously 2024-12-18T01:36:48.4324932Z because, when accessed, the static or class method preserves attributes 2024-12-18T01:36:48.4325821Z installed by ``@rpc.functions.async_execution``. 2024-12-18T01:36:48.4326291Z 2024-12-18T01:36:48.4326299Z 2024-12-18T01:36:48.4326465Z Example:: 2024-12-18T01:36:48.4327033Z The returned :class:`~torch.futures.Future` object can come from 2024-12-18T01:36:48.4327866Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-12-18T01:36:48.4328739Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-12-18T01:36:48.4329697Z constructor. The example below shows directly using the 2024-12-18T01:36:48.4330471Z :class:`~torch.futures.Future` returned by 2024-12-18T01:36:48.4331192Z :meth:`~torch.futures.Future.then`. 2024-12-18T01:36:48.4331609Z 2024-12-18T01:36:48.4331823Z >>> from torch.distributed import rpc 2024-12-18T01:36:48.4332404Z >>> 2024-12-18T01:36:48.4332810Z >>> # omitting setup and shutdown RPC 2024-12-18T01:36:48.4333359Z >>> 2024-12-18T01:36:48.4333718Z >>> # On all workers 2024-12-18T01:36:48.4334209Z >>> @rpc.functions.async_execution 2024-12-18T01:36:48.4334969Z >>> def async_add_chained(to, x, y, z): 2024-12-18T01:36:48.4335720Z >>> # This function runs on "worker1" and returns immediately when 2024-12-18T01:36:48.4336647Z >>> # the callback is installed through the `then(cb)` API. In the 2024-12-18T01:36:48.4337561Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-12-18T01:36:48.4338425Z >>> # When the return value of that `rpc_async` arrives at 2024-12-18T01:36:48.4339279Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-12-18T01:36:48.4340201Z >>> # and set the value for the previously returned `Future`, which 2024-12-18T01:36:48.4341125Z >>> # will then trigger RPC to send the result back to "worker0". 2024-12-18T01:36:48.4342096Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:48.4342787Z >>> lambda fut: fut.wait() + z 2024-12-18T01:36:48.4343299Z >>> ) 2024-12-18T01:36:48.4343658Z >>> 2024-12-18T01:36:48.4343882Z >>> # On worker0 2024-12-18T01:36:48.4344144Z >>> # xdoctest: +SKIP 2024-12-18T01:36:48.4344424Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:48.4344686Z >>> "worker1", 2024-12-18T01:36:48.4344951Z >>> async_add_chained, 2024-12-18T01:36:48.4345333Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-12-18T01:36:48.4345661Z >>> ) 2024-12-18T01:36:48.4345917Z >>> print(ret) # prints tensor([3., 3.]) 2024-12-18T01:36:48.4346142Z 2024-12-18T01:36:48.4346371Z When combined with TorchScript decorators, this decorator must be the 2024-12-18T01:36:48.4346814Z outmost one. 2024-12-18T01:36:48.4346967Z 2024-12-18T01:36:48.4347074Z >>> from torch import Tensor 2024-12-18T01:36:48.4347394Z >>> from torch.futures import Future 2024-12-18T01:36:48.4347746Z >>> from torch.distributed import rpc 2024-12-18T01:36:48.4348054Z >>> 2024-12-18T01:36:48.4348332Z >>> # omitting setup and shutdown RPC 2024-12-18T01:36:48.4348650Z >>> 2024-12-18T01:36:48.4348865Z >>> # On all workers 2024-12-18T01:36:48.4349186Z >>> @torch.jit.script 2024-12-18T01:36:48.4349691Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-12-18T01:36:48.4350289Z >>> return x + y 2024-12-18T01:36:48.4350731Z >>> 2024-12-18T01:36:48.4351142Z >>> @rpc.functions.async_execution 2024-12-18T01:36:48.4351723Z >>> @torch.jit.script 2024-12-18T01:36:48.4352352Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-12-18T01:36:48.4353163Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-12-18T01:36:48.4353786Z >>> 2024-12-18T01:36:48.4354149Z >>> # On worker0 2024-12-18T01:36:48.4354596Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:48.4355079Z >>> "worker1", 2024-12-18T01:36:48.4355516Z >>> async_add, 2024-12-18T01:36:48.4356093Z >>> args=("worker2", torch.ones(2), 1) 2024-12-18T01:36:48.4356671Z >>> ) 2024-12-18T01:36:48.4357117Z >>> print(ret) # prints tensor([2., 2.]) 2024-12-18T01:36:48.4357516Z 2024-12-18T01:36:48.4357934Z When combined with static or class method, this decorator must be the 2024-12-18T01:36:48.4358691Z inner one. 2024-12-18T01:36:48.4358918Z 2024-12-18T01:36:48.4359131Z >>> from torch.distributed import rpc 2024-12-18T01:36:48.4359712Z >>> 2024-12-18T01:36:48.4360126Z >>> # omitting setup and shutdown RPC 2024-12-18T01:36:48.4360678Z >>> 2024-12-18T01:36:48.4361066Z >>> # On all workers 2024-12-18T01:36:48.4361555Z >>> class AsyncExecutionClass: 2024-12-18T01:36:48.4362082Z >>> 2024-12-18T01:36:48.4362460Z >>> @staticmethod 2024-12-18T01:36:48.4363003Z >>> @rpc.functions.async_execution 2024-12-18T01:36:48.4363634Z >>> def static_async_add(to, x, y, z): 2024-12-18T01:36:48.4364357Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:48.4365095Z >>> lambda fut: fut.wait() + z 2024-12-18T01:36:48.4365806Z >>> ) 2024-12-18T01:36:48.4366215Z >>> 2024-12-18T01:36:48.4366597Z >>> @classmethod 2024-12-18T01:36:48.4367097Z >>> @rpc.functions.async_execution 2024-12-18T01:36:48.4367733Z >>> def class_async_add(cls, to, x, y, z): 2024-12-18T01:36:48.4368369Z >>> ret_fut = torch.futures.Future() 2024-12-18T01:36:48.4369048Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:48.4369780Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-12-18T01:36:48.4370412Z >>> ) 2024-12-18T01:36:48.4370838Z >>> return ret_fut 2024-12-18T01:36:48.4371287Z >>> 2024-12-18T01:36:48.4371701Z >>> @rpc.functions.async_execution 2024-12-18T01:36:48.4372343Z >>> def bound_async_add(self, to, x, y, z): 2024-12-18T01:36:48.4373175Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:48.4373902Z >>> lambda fut: fut.wait() + z 2024-12-18T01:36:48.4374404Z >>> ) 2024-12-18T01:36:48.4374755Z >>> 2024-12-18T01:36:48.4375088Z >>> # On worker0 2024-12-18T01:36:48.4375508Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:48.4375972Z >>> "worker1", 2024-12-18T01:36:48.4376485Z >>> AsyncExecutionClass.static_async_add, 2024-12-18T01:36:48.4377168Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:36:48.4377505Z >>> ) 2024-12-18T01:36:48.4377744Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:48.4378063Z >>> 2024-12-18T01:36:48.4378294Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:48.4378569Z >>> "worker1", 2024-12-18T01:36:48.4378865Z >>> AsyncExecutionClass.class_async_add, 2024-12-18T01:36:48.4379220Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:36:48.4379545Z >>> ) 2024-12-18T01:36:48.4379789Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:48.4380012Z 2024-12-18T01:36:48.4380224Z This decorator also works with RRef helpers, i.e., . 2024-12-18T01:36:48.4380650Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-12-18T01:36:48.4381054Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-12-18T01:36:48.4381472Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-12-18T01:36:48.4381734Z 2024-12-18T01:36:48.4381857Z >>> from torch.distributed import rpc 2024-12-18T01:36:48.4382178Z >>> 2024-12-18T01:36:48.4382438Z >>> # reuse the AsyncExecutionClass class above 2024-12-18T01:36:48.4382950Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:36:48.4383771Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-12-18T01:36:48.4384417Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:48.4384740Z >>> 2024-12-18T01:36:48.4385021Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:36:48.4385508Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-12-18T01:36:48.4385982Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:48.4386302Z >>> 2024-12-18T01:36:48.4386577Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:36:48.4387074Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-12-18T01:36:48.4387546Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:48.4387770Z 2024-12-18T01:36:48.4388018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.4388389Z 2024-12-18T01:36:48.4389106Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108. 2024-12-18T01:36:48.4390151Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.4390533Z 2024-12-18T01:36:48.4390738Z Set device mapping between each RPC caller and callee pair. This 2024-12-18T01:36:48.4391241Z function can be called multiple times to incrementally add 2024-12-18T01:36:48.4391745Z device placement configurations. 2024-12-18T01:36:48.4392080Z 2024-12-18T01:36:48.4392223Z Args: 2024-12-18T01:36:48.4392568Z to (str): Callee name. 2024-12-18T01:36:48.4393221Z device_map (Dict of int, str, or torch.device): Device placement 2024-12-18T01:36:48.4394126Z mappings from this worker to the callee. This map must be 2024-12-18T01:36:48.4394810Z invertible. 2024-12-18T01:36:48.4395074Z 2024-12-18T01:36:48.4395244Z Example: 2024-12-18T01:36:48.4395739Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:48.4396303Z >>> # both workers 2024-12-18T01:36:48.4396756Z >>> def add(x, y): 2024-12-18T01:36:48.4397283Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-12-18T01:36:48.4398119Z >>> return x + y, (x + y).to(2) 2024-12-18T01:36:48.4398769Z >>> 2024-12-18T01:36:48.4399128Z >>> # on worker 0 2024-12-18T01:36:48.4399619Z >>> options = TensorPipeRpcBackendOptions( 2024-12-18T01:36:48.4400274Z >>> num_worker_threads=8, 2024-12-18T01:36:48.4400796Z >>> device_maps={"worker1": {0: 1}} 2024-12-18T01:36:48.4401698Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-12-18T01:36:48.4402180Z >>> ) 2024-12-18T01:36:48.4402448Z >>> options.set_device_map("worker1", {1: 2}) 2024-12-18T01:36:48.4402930Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-12-18T01:36:48.4403274Z >>> 2024-12-18T01:36:48.4403493Z >>> rpc.init_rpc( 2024-12-18T01:36:48.4403756Z >>> "worker0", 2024-12-18T01:36:48.4404014Z >>> rank=0, 2024-12-18T01:36:48.4404267Z >>> world_size=2, 2024-12-18T01:36:48.4404582Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-12-18T01:36:48.4426677Z >>> rpc_backend_options=options 2024-12-18T01:36:48.4427318Z >>> ) 2024-12-18T01:36:48.4427698Z >>> 2024-12-18T01:36:48.4428086Z >>> x = torch.ones(2) 2024-12-18T01:36:48.4428830Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-12-18T01:36:48.4429700Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-12-18T01:36:48.4430601Z >>> # sending the return value back, it will follow the invert of 2024-12-18T01:36:48.4431489Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-12-18T01:36:48.4432215Z >>> # cuda:1 on worker0 2024-12-18T01:36:48.4432777Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-12-18T01:36:48.4433495Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-12-18T01:36:48.4433965Z 2024-12-18T01:36:48.4434445Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.4435146Z 2024-12-18T01:36:48.5586216Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=32. 2024-12-18T01:36:48.5587371Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.5587826Z 2024-12-18T01:36:48.5588117Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2024-12-18T01:36:48.5588840Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2024-12-18T01:36:48.5589555Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2024-12-18T01:36:48.5590177Z :class:`DTensor` according to the ``out_placements``. 2024-12-18T01:36:48.5590456Z 2024-12-18T01:36:48.5590545Z Args: 2024-12-18T01:36:48.5591009Z func (Callable): the function to be applied on each local shard of 2024-12-18T01:36:48.5591733Z :class:`DTensor` s. 2024-12-18T01:36:48.5592434Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-12-18T01:36:48.5593537Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2024-12-18T01:36:48.5594665Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-12-18T01:36:48.5595871Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-12-18T01:36:48.5597276Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-12-18T01:36:48.5598389Z mapping to the flattened ``output``. 2024-12-18T01:36:48.5599146Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-12-18T01:36:48.5600254Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2024-12-18T01:36:48.5601206Z should be `None`. 2024-12-18T01:36:48.5602213Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-12-18T01:36:48.5603312Z in. In this case, even if `out_placements` is not `None`, the result function 2024-12-18T01:36:48.5604387Z should ignore the desired placements because the function is not running with 2024-12-18T01:36:48.5605388Z :class:`DTensor` s. 2024-12-18T01:36:48.5606031Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-12-18T01:36:48.5607086Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2024-12-18T01:36:48.5608219Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2024-12-18T01:36:48.5609434Z placements of each :class:`DTensor` argument is the same as the required 2024-12-18T01:36:48.5610423Z placements or not. If the placements are not the same and 2024-12-18T01:36:48.5611432Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-12-18T01:36:48.5612431Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2024-12-18T01:36:48.5613543Z the required sharding placements before passing its local tensor to ``func``. 2024-12-18T01:36:48.5614466Z The only exception is when required placements are not ``None`` and the 2024-12-18T01:36:48.5615175Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-12-18T01:36:48.5615753Z will be skipped and the argument will be directly passed to ``func``. 2024-12-18T01:36:48.5616311Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-12-18T01:36:48.5616755Z Default: None 2024-12-18T01:36:48.5617059Z device_mesh (:class:`DeviceMesh`, optional): 2024-12-18T01:36:48.5617526Z the device mesh that all the :class:`DTensor` s are placed on. If not 2024-12-18T01:36:48.5618092Z specified, this will be inferred from the input :class:`DTensor` s' device 2024-12-18T01:36:48.5618676Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2024-12-18T01:36:48.5619141Z device mesh. Default: None. 2024-12-18T01:36:48.5619477Z redistribute_inputs (bool, optional): 2024-12-18T01:36:48.5619964Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2024-12-18T01:36:48.5620584Z their placements are different from the required input placements. If this 2024-12-18T01:36:48.5621424Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-12-18T01:36:48.5622271Z an exception will be raised. Default: False. 2024-12-18T01:36:48.5622706Z 2024-12-18T01:36:48.5622854Z Returns: 2024-12-18T01:36:48.5623494Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-12-18T01:36:48.5624607Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-12-18T01:36:48.5625277Z 2024-12-18T01:36:48.5625449Z Raises: 2024-12-18T01:36:48.5626095Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2024-12-18T01:36:48.5627223Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-12-18T01:36:48.5628084Z argument passed in. 2024-12-18T01:36:48.5628423Z 2024-12-18T01:36:48.5628875Z AssertionError: For any non-DTensor output, we require its corresponding 2024-12-18T01:36:48.5630185Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-12-18T01:36:48.5631070Z if this is not the case. 2024-12-18T01:36:48.5631418Z 2024-12-18T01:36:48.5631904Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-12-18T01:36:48.5632913Z a redistribution according to ``in_placements``. 2024-12-18T01:36:48.5633422Z 2024-12-18T01:36:48.5633583Z Example: 2024-12-18T01:36:48.5634017Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:48.5634685Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-12-18T01:36:48.5635373Z >>> partial_sum_tensor = torch.mm(W, X) 2024-12-18T01:36:48.5636328Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-12-18T01:36:48.5637287Z >>> return reduced_tensor 2024-12-18T01:36:48.5637795Z >>> 2024-12-18T01:36:48.5638236Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-12-18T01:36:48.5638844Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-12-18T01:36:48.5639447Z >>> Y = torch.mm(W, X) 2024-12-18T01:36:48.5640095Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-12-18T01:36:48.5641005Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-12-18T01:36:48.5641712Z >>> 2024-12-18T01:36:48.5642501Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-12-18T01:36:48.5643446Z >>> local_mm_allreduce_forward = local_map( 2024-12-18T01:36:48.5644069Z >>> mm_allreduce_forward, 2024-12-18T01:36:48.5644631Z >>> out_placements=[Replicate()], 2024-12-18T01:36:48.5645245Z >>> in_placements=[col_wise, row_wise], 2024-12-18T01:36:48.5645855Z >>> device_mesh=device_mesh, 2024-12-18T01:36:48.5646370Z >>> ) 2024-12-18T01:36:48.5646731Z >>> 2024-12-18T01:36:48.5647510Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-12-18T01:36:48.5648708Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-12-18T01:36:48.5650039Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-12-18T01:36:48.5650892Z 2024-12-18T01:36:48.5651308Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:36:48.5651887Z 2024-12-18T01:36:48.5652356Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.5653070Z 2024-12-18T01:36:48.5654504Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2024-12-18T01:36:48.5656575Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.5657318Z 2024-12-18T01:36:48.5657838Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2024-12-18T01:36:48.5659039Z strategies for an operator when the tensor inputs and outputs are DTensor. 2024-12-18T01:36:48.5660158Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-12-18T01:36:48.5661257Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-12-18T01:36:48.5662444Z when users would like to overwrite default sharding strategies of existing operators. 2024-12-18T01:36:48.5663165Z 2024-12-18T01:36:48.5663322Z Args: 2024-12-18T01:36:48.5663781Z op (Union[OpOverload, List[OpOverload]]): 2024-12-18T01:36:48.5664579Z An op or a list of ops to register the customized sharding function. 2024-12-18T01:36:48.5665117Z 2024-12-18T01:36:48.5665269Z Returns: 2024-12-18T01:36:48.5665982Z A function decorator which can be used to wrap a function that defines the sharding 2024-12-18T01:36:48.5667250Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-12-18T01:36:48.5668515Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-12-18T01:36:48.5669946Z already implemented the operator. The customized sharding function takes the same inputs 2024-12-18T01:36:48.5671195Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-12-18T01:36:48.5672329Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-12-18T01:36:48.5673570Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-12-18T01:36:48.5674520Z corresponding intput placements. 2024-12-18T01:36:48.5674922Z 2024-12-18T01:36:48.5675082Z Example: 2024-12-18T01:36:48.5675502Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:48.5676232Z >>> @register_sharding(aten._softmax.default) 2024-12-18T01:36:48.5677067Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-12-18T01:36:48.5677825Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-12-18T01:36:48.5678490Z >>> acceptable_shardings = [] 2024-12-18T01:36:48.5679035Z >>> 2024-12-18T01:36:48.5679575Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-12-18T01:36:48.5680381Z >>> acceptable_shardings.append(all_replicate) 2024-12-18T01:36:48.5681008Z >>> 2024-12-18T01:36:48.5681483Z >>> for sharding_dim in range(x.ndim): 2024-12-18T01:36:48.5682121Z >>> if sharding_dim != softmax_dim: 2024-12-18T01:36:48.5682724Z >>> all_sharded = ( 2024-12-18T01:36:48.5683284Z >>> [Shard(sharding_dim)], 2024-12-18T01:36:48.5683878Z >>> [Shard(sharding_dim), None, None], 2024-12-18T01:36:48.5684442Z >>> ) 2024-12-18T01:36:48.5684915Z >>> acceptable_shardings.append(all_sharded) 2024-12-18T01:36:48.5685277Z >>> 2024-12-18T01:36:48.5685521Z >>> return acceptable_shardings 2024-12-18T01:36:48.5685735Z 2024-12-18T01:36:48.5686032Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:36:48.5686337Z 2024-12-18T01:36:48.5686603Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.5686963Z 2024-12-18T01:36:48.5903474Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-12-18T01:36:48.5905406Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.5906131Z 2024-12-18T01:36:48.5906816Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:36:48.5908375Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-12-18T01:36:48.5909225Z 2024-12-18T01:36:48.5909389Z Keyword Args: 2024-12-18T01:36:48.5909971Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:36:48.5911085Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-12-18T01:36:48.5912525Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-12-18T01:36:48.5913584Z as a placeholder. default: None. 2024-12-18T01:36:48.5914430Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:36:48.5915887Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:36:48.5917545Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-12-18T01:36:48.5918753Z input_kwarg_layouts (Dict[str, Placement]): 2024-12-18T01:36:48.5919911Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-12-18T01:36:48.5921031Z default: None 2024-12-18T01:36:48.5921778Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-12-18T01:36:48.5922910Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:36:48.5924098Z have the desired DTensor layouts. default: None. 2024-12-18T01:36:48.5924760Z use_local_output (bool, optional): 2024-12-18T01:36:48.5925816Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-12-18T01:36:48.5926856Z Returns: 2024-12-18T01:36:48.5927659Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-12-18T01:36:48.5928486Z 2024-12-18T01:36:48.5928663Z Example:: 2024-12-18T01:36:48.5929066Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:36:48.5930190Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-12-18T01:36:48.5931345Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:36:48.5932066Z >>> ... 2024-12-18T01:36:48.5932821Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:36:48.5933807Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:36:48.5934402Z >>> 2024-12-18T01:36:48.5935400Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-12-18T01:36:48.5936495Z >>> # and then redistributed to Replicated DTensor. 2024-12-18T01:36:48.5937127Z >>> parallelize_module( 2024-12-18T01:36:48.5937667Z >>> block, # this can be a submodule or module 2024-12-18T01:36:48.5938256Z >>> tp_mesh, 2024-12-18T01:36:48.5938689Z >>> parallelize_plan={ 2024-12-18T01:36:48.5939229Z >>> "attn": PrepareModuleInput( 2024-12-18T01:36:48.5939881Z >>> input_layouts=(Shard(0), None, None, ...), 2024-12-18T01:36:48.5940765Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-12-18T01:36:48.5941468Z >>> ), 2024-12-18T01:36:48.5941878Z >>> } 2024-12-18T01:36:48.5942252Z >>> ) 2024-12-18T01:36:48.5942469Z 2024-12-18T01:36:48.5942942Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.5943643Z 2024-12-18T01:36:48.5944963Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-12-18T01:36:48.5946855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.5947583Z 2024-12-18T01:36:48.5948296Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:36:48.5949859Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-12-18T01:36:48.5950700Z 2024-12-18T01:36:48.5950882Z Keyword Args: 2024-12-18T01:36:48.5951427Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:36:48.5952526Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-12-18T01:36:48.5954035Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-12-18T01:36:48.5955199Z ``None`` need to be specified as a placeholder. 2024-12-18T01:36:48.5956089Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:36:48.5957366Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-12-18T01:36:48.5958473Z have the desired DTensor layouts. 2024-12-18T01:36:48.5959058Z use_local_output (bool, optional): 2024-12-18T01:36:48.5960104Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-12-18T01:36:48.5961155Z Returns: 2024-12-18T01:36:48.5962032Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-12-18T01:36:48.5962782Z 2024-12-18T01:36:48.5962967Z Example:: 2024-12-18T01:36:48.5963379Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:36:48.5964345Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-12-18T01:36:48.5965527Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:36:48.5966255Z >>> ... 2024-12-18T01:36:48.5967049Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:36:48.5968093Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:36:48.5968682Z >>> 2024-12-18T01:36:48.5969674Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-12-18T01:36:48.5970928Z >>> # and then redistributed to Sharded DTensor. 2024-12-18T01:36:48.5971574Z >>> parallelize_module( 2024-12-18T01:36:48.5972138Z >>> block, # this can be a submodule or module 2024-12-18T01:36:48.5972722Z >>> tp_mesh, 2024-12-18T01:36:48.5973221Z >>> parallelize_plan = PrepareModuleOutput( 2024-12-18T01:36:48.5973892Z >>> output_layouts=Replicate(), 2024-12-18T01:36:48.5974598Z >>> desired_output_layouts=Shard(0) 2024-12-18T01:36:48.5975197Z >>> ) 2024-12-18T01:36:48.5975577Z >>> ) 2024-12-18T01:36:48.5975794Z 2024-12-18T01:36:48.5976261Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.5976923Z 2024-12-18T01:36:48.6532573Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-12-18T01:36:48.6534549Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.6535267Z 2024-12-18T01:36:48.6535851Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-12-18T01:36:48.6536966Z distribution where all component are from different parameterizations of 2024-12-18T01:36:48.6538032Z the same distribution type. It is parameterized by a `Categorical` 2024-12-18T01:36:48.6539026Z "selecting distribution" (over `k` component) and a component 2024-12-18T01:36:48.6539998Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-12-18T01:36:48.6540871Z (equal to `[k]`) which indexes each (batch of) component. 2024-12-18T01:36:48.6541374Z 2024-12-18T01:36:48.6541540Z Examples:: 2024-12-18T01:36:48.6541748Z 2024-12-18T01:36:48.6541970Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:48.6542760Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-12-18T01:36:48.6543575Z >>> # weighted normal distributions 2024-12-18T01:36:48.6544183Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:36:48.6544879Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-12-18T01:36:48.6545569Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:36:48.6545980Z 2024-12-18T01:36:48.6546372Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-12-18T01:36:48.6547152Z >>> # weighted bivariate normal distributions 2024-12-18T01:36:48.6547767Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:36:48.6548398Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:36:48.6549020Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-12-18T01:36:48.6549694Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:36:48.6550101Z 2024-12-18T01:36:48.6550430Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-12-18T01:36:48.6551333Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-12-18T01:36:48.6552131Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-12-18T01:36:48.6552755Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:36:48.6553383Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-12-18T01:36:48.6554191Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:36:48.6554585Z 2024-12-18T01:36:48.6554751Z Args: 2024-12-18T01:36:48.6555300Z mixture_distribution: `torch.distributions.Categorical`-like 2024-12-18T01:36:48.6556322Z instance. Manages the probability of selecting component. 2024-12-18T01:36:48.6557201Z The number of categories must match the rightmost batch 2024-12-18T01:36:48.6558077Z dimension of the `component_distribution`. Must have either 2024-12-18T01:36:48.6558889Z scalar `batch_shape` or `batch_shape` matching 2024-12-18T01:36:48.6559570Z `component_distribution.batch_shape[:-1]` 2024-12-18T01:36:48.6560426Z component_distribution: `torch.distributions.Distribution`-like 2024-12-18T01:36:48.6561399Z instance. Right-most batch dimension indexes component. 2024-12-18T01:36:48.6562114Z 2024-12-18T01:36:48.6562588Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.6563265Z 2024-12-18T01:36:48.8244407Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-12-18T01:36:48.8245460Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.8245836Z 2024-12-18T01:36:48.8246243Z Creates a RelaxedBernoulli distribution, parametrized by 2024-12-18T01:36:48.8246742Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-12-18T01:36:48.8247277Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-12-18T01:36:48.8247789Z so the values are in (0, 1), and has reparametrizable samples. 2024-12-18T01:36:48.8248093Z 2024-12-18T01:36:48.8248230Z Example:: 2024-12-18T01:36:48.8248370Z 2024-12-18T01:36:48.8248521Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:48.8248966Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-12-18T01:36:48.8249695Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-12-18T01:36:48.8250273Z >>> m.sample() 2024-12-18T01:36:48.8250729Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-12-18T01:36:48.8251158Z 2024-12-18T01:36:48.8251314Z Args: 2024-12-18T01:36:48.8251759Z temperature (Tensor): relaxation temperature 2024-12-18T01:36:48.8252552Z probs (Number, Tensor): the probability of sampling `1` 2024-12-18T01:36:48.8253346Z logits (Number, Tensor): the log-odds of sampling `1` 2024-12-18T01:36:48.8253829Z 2024-12-18T01:36:48.8254286Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.8255003Z 2024-12-18T01:36:48.8267437Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-12-18T01:36:48.8269523Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:48.8270241Z 2024-12-18T01:36:48.8270647Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-12-18T01:36:48.8271636Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-12-18T01:36:48.8272633Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-12-18T01:36:48.8273620Z its samples are on simplex, and are reparametrizable. 2024-12-18T01:36:48.8274145Z 2024-12-18T01:36:48.8274326Z Example:: 2024-12-18T01:36:48.8274560Z 2024-12-18T01:36:48.8274807Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:48.8275539Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-12-18T01:36:48.8276310Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-12-18T01:36:48.8276927Z >>> m.sample() 2024-12-18T01:36:48.8277371Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-12-18T01:36:48.8277804Z 2024-12-18T01:36:48.8277954Z Args: 2024-12-18T01:36:48.8278418Z temperature (Tensor): relaxation temperature 2024-12-18T01:36:48.8279083Z probs (Tensor): event probabilities 2024-12-18T01:36:48.8279992Z logits (Tensor): unnormalized log probability for each event 2024-12-18T01:36:48.8280508Z 2024-12-18T01:36:48.8280977Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:48.8281645Z 2024-12-18T01:36:49.1811984Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-12-18T01:36:49.1813994Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.1815057Z Return a new dict with new, potentially nested, key value pair 2024-12-18T01:36:49.1815609Z 2024-12-18T01:36:49.1815772Z >>> purchase = { 2024-12-18T01:36:49.1816187Z ... "name": "Alice", 2024-12-18T01:36:49.1817094Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:36:49.1817912Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:36:49.1818489Z ... } 2024-12-18T01:36:49.1819080Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2024-12-18T01:36:49.1819846Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:36:49.1820423Z 'name': 'Alice', 2024-12-18T01:36:49.1820951Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-12-18T01:36:49.1821704Z 2024-12-18T01:36:49.1822376Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.1823051Z 2024-12-18T01:36:49.1824215Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2024-12-18T01:36:49.1826106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.1826801Z Update value in a (potentially) nested dictionary 2024-12-18T01:36:49.1827078Z 2024-12-18T01:36:49.1827170Z inputs: 2024-12-18T01:36:49.1827547Z d - dictionary on which to operate 2024-12-18T01:36:49.1828000Z keys - list or tuple giving the location of the value to be changed in d 2024-12-18T01:36:49.1828452Z func - function to operate on that value 2024-12-18T01:36:49.1828694Z 2024-12-18T01:36:49.1828883Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-12-18T01:36:49.1829424Z original dictionary with v replaced by func(v), but does not mutate the 2024-12-18T01:36:49.1829872Z original dictionary. 2024-12-18T01:36:49.1830040Z 2024-12-18T01:36:49.1830260Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-12-18T01:36:49.1830801Z specified by the keys, with the innermost value set to func(default). 2024-12-18T01:36:49.1831125Z 2024-12-18T01:36:49.1831226Z >>> inc = lambda x: x + 1 2024-12-18T01:36:49.1831523Z >>> update_in({"a": 0}, ["a"], inc) 2024-12-18T01:36:49.1831825Z {'a': 1} 2024-12-18T01:36:49.1831952Z 2024-12-18T01:36:49.1832063Z >>> transaction = { 2024-12-18T01:36:49.1832338Z ... "name": "Alice", 2024-12-18T01:36:49.1832922Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:36:49.1833661Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:36:49.1834216Z ... } 2024-12-18T01:36:49.1834821Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2024-12-18T01:36:49.1835596Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:36:49.1836240Z 'name': 'Alice', 2024-12-18T01:36:49.1836809Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-12-18T01:36:49.1837317Z 2024-12-18T01:36:49.1837524Z >>> # updating a value when k0 is not in d 2024-12-18T01:36:49.1838167Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-12-18T01:36:49.1838771Z {1: {2: {3: 'bar'}}} 2024-12-18T01:36:49.1839233Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2024-12-18T01:36:49.1839756Z {1: 'foo', 2: {3: {4: 1}}} 2024-12-18T01:36:49.1840231Z 2024-12-18T01:36:49.1841091Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.1841737Z 2024-12-18T01:36:49.1842925Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2024-12-18T01:36:49.1844828Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.1845819Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-12-18T01:36:49.1846320Z 2024-12-18T01:36:49.1846615Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-12-18T01:36:49.1847508Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-12-18T01:36:49.1848117Z 2024-12-18T01:36:49.1848491Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-12-18T01:36:49.1849433Z structures such as dictionaries and lists. 2024-12-18T01:36:49.1849888Z 2024-12-18T01:36:49.1850085Z >>> transaction = { 2024-12-18T01:36:49.1850556Z ... "name": "Alice", 2024-12-18T01:36:49.1851188Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:36:49.1851951Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:36:49.1852528Z ... } 2024-12-18T01:36:49.1853098Z >>> get_in(["purchase", "items", 0], transaction) 2024-12-18T01:36:49.1853709Z 'Apple' 2024-12-18T01:36:49.1854116Z >>> get_in(["name"], transaction) 2024-12-18T01:36:49.1854632Z 'Alice' 2024-12-18T01:36:49.1855076Z >>> get_in(["purchase", "total"], transaction) 2024-12-18T01:36:49.1855798Z >>> get_in(["purchase", "items", "apple"], transaction) 2024-12-18T01:36:49.1856516Z >>> get_in(["purchase", "items", 10], transaction) 2024-12-18T01:36:49.1857187Z >>> get_in(["purchase", "total"], transaction, 0) 2024-12-18T01:36:49.1857805Z 0 2024-12-18T01:36:49.1858199Z >>> get_in(["y"], {}, no_default=True) 2024-12-18T01:36:49.1858872Z Traceback (most recent call last): 2024-12-18T01:36:49.1859436Z ... 2024-12-18T01:36:49.1859794Z KeyError: 'y' 2024-12-18T01:36:49.1860063Z 2024-12-18T01:36:49.1860208Z See Also: 2024-12-18T01:36:49.1860602Z itertoolz.get 2024-12-18T01:36:49.1861043Z operator.getitem 2024-12-18T01:36:49.1861500Z 2024-12-18T01:36:49.1862132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.1862826Z 2024-12-18T01:36:49.1864094Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2024-12-18T01:36:49.1865946Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.1866861Z Group a collection by a key function 2024-12-18T01:36:49.1867218Z 2024-12-18T01:36:49.1867407Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2024-12-18T01:36:49.1867829Z >>> groupby(len, names) # doctest: +SKIP 2024-12-18T01:36:49.1868238Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-12-18T01:36:49.1868511Z 2024-12-18T01:36:49.1868621Z >>> iseven = lambda x: x % 2 == 0 2024-12-18T01:36:49.1869003Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-12-18T01:36:49.1869405Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-12-18T01:36:49.1869621Z 2024-12-18T01:36:49.1869776Z Non-callable keys imply grouping on a member. 2024-12-18T01:36:49.1870026Z 2024-12-18T01:36:49.1870130Z >>> groupby( 2024-12-18T01:36:49.1870358Z ... "gender", 2024-12-18T01:36:49.1870606Z ... [ 2024-12-18T01:36:49.1870858Z ... {"name": "Alice", "gender": "F"}, 2024-12-18T01:36:49.1871210Z ... {"name": "Bob", "gender": "M"}, 2024-12-18T01:36:49.1871566Z ... {"name": "Charlie", "gender": "M"}, 2024-12-18T01:36:49.1871881Z ... ], 2024-12-18T01:36:49.1872142Z ... ) # doctest:+SKIP 2024-12-18T01:36:49.1872727Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-12-18T01:36:49.1873296Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-12-18T01:36:49.1873888Z {'gender': 'M', 'name': 'Charlie'}]} 2024-12-18T01:36:49.1874292Z 2024-12-18T01:36:49.1874551Z Not to be confused with ``itertools.groupby`` 2024-12-18T01:36:49.1874986Z 2024-12-18T01:36:49.1875141Z See Also: 2024-12-18T01:36:49.1875537Z countby 2024-12-18T01:36:49.1876006Z 2024-12-18T01:36:49.1876665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.1877346Z 2024-12-18T01:36:49.5879045Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-12-18T01:36:49.5880776Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.5882086Z Applies Batch Normalization over a N-Dimensional input. 2024-12-18T01:36:49.5882570Z 2024-12-18T01:36:49.5883126Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-12-18T01:36:49.5884343Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-12-18T01:36:49.5885317Z Internal Covariate Shift `__ . 2024-12-18T01:36:49.5885909Z 2024-12-18T01:36:49.5886257Z .. math:: 2024-12-18T01:36:49.5886492Z 2024-12-18T01:36:49.5886891Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-12-18T01:36:49.5887462Z 2024-12-18T01:36:49.5887890Z The mean and standard-deviation are calculated per-dimension over all 2024-12-18T01:36:49.5888900Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-12-18T01:36:49.5890016Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-12-18T01:36:49.5891030Z By default, the elements of :math:`\gamma` are sampled from 2024-12-18T01:36:49.5892050Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-12-18T01:36:49.5893114Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-12-18T01:36:49.5894049Z `torch.var(input, unbiased=False)`. 2024-12-18T01:36:49.5894461Z 2024-12-18T01:36:49.5894903Z Also by default, during training this layer keeps running estimates of its 2024-12-18T01:36:49.5895963Z computed mean and variance, which are then used for normalization during 2024-12-18T01:36:49.5897050Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-12-18T01:36:49.5898135Z of 0.1. 2024-12-18T01:36:49.5898354Z 2024-12-18T01:36:49.5898780Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-12-18T01:36:49.5899790Z keep running estimates, and batch statistics are instead used during 2024-12-18T01:36:49.5900578Z evaluation time as well. 2024-12-18T01:36:49.5900916Z 2024-12-18T01:36:49.5901089Z .. note:: 2024-12-18T01:36:49.5902036Z This :attr:`momentum` argument is different from one used in optimizer 2024-12-18T01:36:49.5903016Z classes and the conventional notion of momentum. Mathematically, the 2024-12-18T01:36:49.5903859Z update rule for running statistics here is 2024-12-18T01:36:49.5904764Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-12-18T01:36:49.5905848Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-12-18T01:36:49.5906609Z new observed value. 2024-12-18T01:36:49.5906926Z 2024-12-18T01:36:49.5907484Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-12-18T01:36:49.5908728Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-12-18T01:36:49.5909737Z Normalization or Spatio-temporal Batch Normalization. 2024-12-18T01:36:49.5910266Z 2024-12-18T01:36:49.5910539Z Currently :class:`SyncBatchNorm` only supports 2024-12-18T01:36:49.5911707Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-12-18T01:36:49.5912874Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-12-18T01:36:49.5913875Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-12-18T01:36:49.5914658Z Network with DDP. 2024-12-18T01:36:49.5914948Z 2024-12-18T01:36:49.5915113Z Args: 2024-12-18T01:36:49.5915781Z num_features: :math:`C` from an expected input of size 2024-12-18T01:36:49.5916456Z :math:`(N, C, +)` 2024-12-18T01:36:49.5917139Z eps: a value added to the denominator for numerical stability. 2024-12-18T01:36:49.5917887Z Default: ``1e-5`` 2024-12-18T01:36:49.5918578Z momentum: the value used for the running_mean and running_var 2024-12-18T01:36:49.5919587Z computation. Can be set to ``None`` for cumulative moving average 2024-12-18T01:36:49.5920408Z (i.e. simple average). Default: 0.1 2024-12-18T01:36:49.5921188Z affine: a boolean value that when set to ``True``, this module has 2024-12-18T01:36:49.5922056Z learnable affine parameters. Default: ``True`` 2024-12-18T01:36:49.5922937Z track_running_stats: a boolean value that when set to ``True``, this 2024-12-18T01:36:49.5924101Z module tracks the running mean and variance, and when set to ``False``, 2024-12-18T01:36:49.5925155Z this module does not track such statistics, and initializes statistics 2024-12-18T01:36:49.5926162Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-12-18T01:36:49.5927176Z When these buffers are ``None``, this module always uses batch statistics. 2024-12-18T01:36:49.5928098Z in both training and eval modes. Default: ``True`` 2024-12-18T01:36:49.5929059Z process_group: synchronization of stats happen within each process group 2024-12-18T01:36:49.5930250Z individually. Default behavior is synchronization across the whole 2024-12-18T01:36:49.5931057Z world 2024-12-18T01:36:49.5931302Z 2024-12-18T01:36:49.5931454Z Shape: 2024-12-18T01:36:49.5931869Z - Input: :math:`(N, C, +)` 2024-12-18T01:36:49.5932488Z - Output: :math:`(N, C, +)` (same shape as input) 2024-12-18T01:36:49.5932960Z 2024-12-18T01:36:49.5933155Z .. note:: 2024-12-18T01:36:49.5933828Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-12-18T01:36:49.5934885Z synchronization is disabled when ``model.eval()`` is set or if 2024-12-18T01:36:49.5935705Z ``self.training`` is otherwise ``False``. 2024-12-18T01:36:49.5936042Z 2024-12-18T01:36:49.5936176Z Examples:: 2024-12-18T01:36:49.5936374Z 2024-12-18T01:36:49.5936503Z >>> # xdoctest: +SKIP 2024-12-18T01:36:49.5936902Z >>> # With Learnable Parameters 2024-12-18T01:36:49.5937466Z >>> m = nn.SyncBatchNorm(100) 2024-12-18T01:36:49.5938056Z >>> # creating process group (optional) 2024-12-18T01:36:49.5938730Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:36:49.5939366Z >>> ranks = list(range(8)) 2024-12-18T01:36:49.5939905Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:36:49.5940547Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:36:49.5941296Z >>> # process group created, even if that rank is not 2024-12-18T01:36:49.5941970Z >>> # part of the group. 2024-12-18T01:36:49.5942756Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:36:49.5943811Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:36:49.5944597Z >>> # Without Learnable Parameters 2024-12-18T01:36:49.5945403Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-12-18T01:36:49.5946189Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-12-18T01:36:49.5946785Z >>> output = m(input) 2024-12-18T01:36:49.5947219Z 2024-12-18T01:36:49.5947412Z >>> # network is nn.BatchNorm layer 2024-12-18T01:36:49.5948309Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-12-18T01:36:49.5949332Z >>> # only single gpu per process is currently supported 2024-12-18T01:36:49.5950247Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:36:49.5951071Z >>> sync_bn_network, 2024-12-18T01:36:49.5951703Z >>> device_ids=[args.local_rank], 2024-12-18T01:36:49.5952382Z >>> output_device=args.local_rank) 2024-12-18T01:36:49.5952982Z 2024-12-18T01:36:49.5953635Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.5954407Z 2024-12-18T01:36:49.5955720Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-12-18T01:36:49.5957621Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.5958870Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-12-18T01:36:49.5959673Z 2024-12-18T01:36:49.5959939Z Args: 2024-12-18T01:36:49.5960604Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-12-18T01:36:49.5961670Z process_group (optional): process group to scope synchronization, 2024-12-18T01:36:49.5962464Z default is the whole world 2024-12-18T01:36:49.5962863Z 2024-12-18T01:36:49.5963017Z Returns: 2024-12-18T01:36:49.5963720Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-12-18T01:36:49.5964804Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-12-18T01:36:49.5965846Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-12-18T01:36:49.5966615Z instead. 2024-12-18T01:36:49.5966875Z 2024-12-18T01:36:49.5967047Z Example:: 2024-12-18T01:36:49.5967297Z 2024-12-18T01:36:49.5967506Z >>> # Network with nn.BatchNorm layer 2024-12-18T01:36:49.5968161Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:49.5968822Z >>> module = torch.nn.Sequential( 2024-12-18T01:36:49.5969461Z >>> torch.nn.Linear(20, 100), 2024-12-18T01:36:49.5970102Z >>> torch.nn.BatchNorm1d(100), 2024-12-18T01:36:49.5970639Z >>> ).cuda() 2024-12-18T01:36:49.5971166Z >>> # creating process group (optional) 2024-12-18T01:36:49.5971817Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:36:49.5972440Z >>> ranks = list(range(8)) 2024-12-18T01:36:49.5972967Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:36:49.5973542Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:36:49.5974239Z >>> # process group created, even if that rank is not 2024-12-18T01:36:49.5974881Z >>> # part of the group. 2024-12-18T01:36:49.5975437Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:49.5976272Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:36:49.5977265Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:36:49.5978444Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-12-18T01:36:49.5979237Z 2024-12-18T01:36:49.5979390Z 2024-12-18T01:36:49.5980058Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.5980665Z 2024-12-18T01:36:49.6164880Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60. 2024-12-18T01:36:49.6166879Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.6167589Z 2024-12-18T01:36:49.6168179Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-12-18T01:36:49.6168961Z 2024-12-18T01:36:49.6169441Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-12-18T01:36:49.6170577Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-12-18T01:36:49.6171237Z 2024-12-18T01:36:49.6171826Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-12-18T01:36:49.6173114Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-12-18T01:36:49.6174285Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-12-18T01:36:49.6174803Z 2024-12-18T01:36:49.6174969Z Shape: 2024-12-18T01:36:49.6175601Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-12-18T01:36:49.6176698Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-12-18T01:36:49.6177803Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-12-18T01:36:49.6178754Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-12-18T01:36:49.6179218Z 2024-12-18T01:36:49.6179372Z Args: 2024-12-18T01:36:49.6179857Z dim (Union[int, str]): Dimension to be unflattened 2024-12-18T01:36:49.6180987Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-12-18T01:36:49.6181803Z 2024-12-18T01:36:49.6181951Z Examples: 2024-12-18T01:36:49.6182337Z >>> input = torch.randn(2, 50) 2024-12-18T01:36:49.6182888Z >>> # With tuple of ints 2024-12-18T01:36:49.6183399Z >>> m = nn.Sequential( 2024-12-18T01:36:49.6183883Z >>> nn.Linear(50, 50), 2024-12-18T01:36:49.6184512Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-12-18T01:36:49.6185043Z >>> ) 2024-12-18T01:36:49.6185421Z >>> output = m(input) 2024-12-18T01:36:49.6185879Z >>> output.size() 2024-12-18T01:36:49.6186337Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:36:49.6186809Z >>> # With torch.Size 2024-12-18T01:36:49.6187291Z >>> m = nn.Sequential( 2024-12-18T01:36:49.6187780Z >>> nn.Linear(50, 50), 2024-12-18T01:36:49.6188351Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-12-18T01:36:49.6188956Z >>> ) 2024-12-18T01:36:49.6189344Z >>> output = m(input) 2024-12-18T01:36:49.6189825Z >>> output.size() 2024-12-18T01:36:49.6190281Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:36:49.6190833Z >>> # With namedshape (tuple of tuples) 2024-12-18T01:36:49.6191521Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-12-18T01:36:49.6192408Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-12-18T01:36:49.6193214Z >>> output = unflatten(input) 2024-12-18T01:36:49.6193762Z >>> output.size() 2024-12-18T01:36:49.6194228Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:36:49.6194549Z 2024-12-18T01:36:49.6195047Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.6195857Z 2024-12-18T01:36:49.6517362Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1698. 2024-12-18T01:36:49.6519369Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.6520510Z Creates a criterion that measures the triplet loss given input 2024-12-18T01:36:49.6521464Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-12-18T01:36:49.6522435Z positive, and negative examples, respectively), and a nonnegative, 2024-12-18T01:36:49.6523550Z real-valued function ("distance function") used to compute the relationship 2024-12-18T01:36:49.6524656Z between the anchor and positive example ("positive distance") and the 2024-12-18T01:36:49.6525830Z anchor and negative example ("negative distance"). 2024-12-18T01:36:49.6526332Z 2024-12-18T01:36:49.6526671Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-12-18T01:36:49.6527433Z can be described as: 2024-12-18T01:36:49.6527762Z 2024-12-18T01:36:49.6527965Z .. math:: 2024-12-18T01:36:49.6528422Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-12-18T01:36:49.6529151Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-12-18T01:36:49.6529645Z 2024-12-18T01:36:49.6530107Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-12-18T01:36:49.6531341Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-12-18T01:36:49.6532711Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-12-18T01:36:49.6533867Z between the positive and negative distances that is required for the loss to 2024-12-18T01:36:49.6535000Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-12-18T01:36:49.6535864Z that the distance function can handle. 2024-12-18T01:36:49.6536286Z 2024-12-18T01:36:49.6536502Z If :attr:`reduction` is not ``'none'`` 2024-12-18T01:36:49.6537167Z (default ``'mean'``), then: 2024-12-18T01:36:49.6537532Z 2024-12-18T01:36:49.6537697Z .. math:: 2024-12-18T01:36:49.6538094Z \ell(x, y) = 2024-12-18T01:36:49.6538528Z \begin{cases} 2024-12-18T01:36:49.6539175Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-12-18T01:36:49.6540114Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-12-18T01:36:49.6540850Z \end{cases} 2024-12-18T01:36:49.6541125Z 2024-12-18T01:36:49.6541560Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-12-18T01:36:49.6542772Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-12-18T01:36:49.6543463Z 2024-12-18T01:36:49.6543635Z Args: 2024-12-18T01:36:49.6544367Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-12-18T01:36:49.6545450Z quantifies the closeness of two tensors. If not specified, 2024-12-18T01:36:49.6546320Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-12-18T01:36:49.6547295Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-12-18T01:36:49.6548493Z between the positive and negative distances required for the loss to be 0. Larger 2024-12-18T01:36:49.6549750Z margins penalize cases where the negative examples are not distant enough from the 2024-12-18T01:36:49.6550811Z anchors, relative to the positives. Default: :math:`1`. 2024-12-18T01:36:49.6551809Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-12-18T01:36:49.6552962Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-12-18T01:36:49.6554119Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-12-18T01:36:49.6555286Z negative example than the anchor is, swaps the positive example and the anchor in 2024-12-18T01:36:49.6556377Z the loss computation. Default: ``False``. 2024-12-18T01:36:49.6557364Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-12-18T01:36:49.6558440Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-12-18T01:36:49.6559324Z ``'mean'``: the sum of the output will be divided by the number of 2024-12-18T01:36:49.6560302Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-12-18T01:36:49.6560992Z 2024-12-18T01:36:49.6561001Z 2024-12-18T01:36:49.6561165Z Shape: 2024-12-18T01:36:49.6561981Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-12-18T01:36:49.6562894Z as supported by the distance function. 2024-12-18T01:36:49.6563784Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-12-18T01:36:49.6564649Z otherwise. 2024-12-18T01:36:49.6564941Z 2024-12-18T01:36:49.6565131Z Examples:: 2024-12-18T01:36:49.6565375Z 2024-12-18T01:36:49.6565580Z >>> # Initialize embeddings 2024-12-18T01:36:49.6566135Z >>> embedding = nn.Embedding(1000, 128) 2024-12-18T01:36:49.6566720Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:36:49.6567388Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:36:49.6568073Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:36:49.6568772Z >>> anchor = embedding(anchor_ids) 2024-12-18T01:36:49.6569355Z >>> positive = embedding(positive_ids) 2024-12-18T01:36:49.6569928Z >>> negative = embedding(negative_ids) 2024-12-18T01:36:49.6570494Z >>> 2024-12-18T01:36:49.6570885Z >>> # Built-in Distance Function 2024-12-18T01:36:49.6571451Z >>> triplet_loss = \ 2024-12-18T01:36:49.6572287Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-12-18T01:36:49.6573427Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:36:49.6574094Z >>> output.backward() 2024-12-18T01:36:49.6574542Z >>> 2024-12-18T01:36:49.6574938Z >>> # Custom Distance Function 2024-12-18T01:36:49.6575487Z >>> def l_infinity(x1, x2): 2024-12-18T01:36:49.6576066Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-12-18T01:36:49.6576646Z >>> 2024-12-18T01:36:49.6577183Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-12-18T01:36:49.6577911Z >>> triplet_loss = ( 2024-12-18T01:36:49.6578715Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-12-18T01:36:49.6579844Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:36:49.6580456Z >>> output.backward() 2024-12-18T01:36:49.6580928Z >>> 2024-12-18T01:36:49.6581339Z >>> # Custom Distance Function (Lambda) 2024-12-18T01:36:49.6581910Z >>> triplet_loss = ( 2024-12-18T01:36:49.6582446Z >>> nn.TripletMarginWithDistanceLoss( 2024-12-18T01:36:49.6583287Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-12-18T01:36:49.6584180Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:36:49.6584795Z >>> output.backward() 2024-12-18T01:36:49.6585080Z 2024-12-18T01:36:49.6585258Z Reference: 2024-12-18T01:36:49.6586062Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-12-18T01:36:49.6587198Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2024-12-18T01:36:49.6587972Z 2024-12-18T01:36:49.6588628Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-12-18T01:36:49.6589344Z 2024-12-18T01:36:49.7126251Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395. 2024-12-18T01:36:49.7128039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.7129078Z Computes a partial inverse of :class:`MaxPool2d`. 2024-12-18T01:36:49.7129579Z 2024-12-18T01:36:49.7130084Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-12-18T01:36:49.7130767Z 2024-12-18T01:36:49.7131191Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-12-18T01:36:49.7132273Z including the indices of the maximal values and computes a partial inverse 2024-12-18T01:36:49.7133189Z in which all non-maximal values are set to zero. 2024-12-18T01:36:49.7133693Z 2024-12-18T01:36:49.7133843Z Note: 2024-12-18T01:36:49.7134670Z This operation may behave nondeterministically when the input indices has repeat values. 2024-12-18T01:36:49.7136525Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-12-18T01:36:49.7137456Z 2024-12-18T01:36:49.7137936Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-12-18T01:36:49.7138889Z sizes. Hence, the inversion process can get ambiguous. 2024-12-18T01:36:49.7139729Z To accommodate this, you can provide the needed output size 2024-12-18T01:36:49.7140711Z as an additional argument :attr:`output_size` in the forward call. 2024-12-18T01:36:49.7141527Z See the Inputs and Example below. 2024-12-18T01:36:49.7141941Z 2024-12-18T01:36:49.7142115Z Args: 2024-12-18T01:36:49.7142667Z kernel_size (int or tuple): Size of the max pooling window. 2024-12-18T01:36:49.7143678Z stride (int or tuple): Stride of the max pooling window. 2024-12-18T01:36:49.7144480Z It is set to :attr:`kernel_size` by default. 2024-12-18T01:36:49.7145288Z padding (int or tuple): Padding that was added to the input 2024-12-18T01:36:49.7145870Z 2024-12-18T01:36:49.7146028Z Inputs: 2024-12-18T01:36:49.7146465Z - `input`: the input Tensor to invert 2024-12-18T01:36:49.7147372Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-12-18T01:36:49.7148258Z - `output_size` (optional): the targeted output size 2024-12-18T01:36:49.7148787Z 2024-12-18T01:36:49.7148930Z Shape: 2024-12-18T01:36:49.7149482Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-12-18T01:36:49.7150389Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-12-18T01:36:49.7151003Z 2024-12-18T01:36:49.7151203Z .. math:: 2024-12-18T01:36:49.7151950Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-12-18T01:36:49.7152764Z 2024-12-18T01:36:49.7152932Z .. math:: 2024-12-18T01:36:49.7153646Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-12-18T01:36:49.7154345Z 2024-12-18T01:36:49.7154640Z or as given by :attr:`output_size` in the call operator 2024-12-18T01:36:49.7155153Z 2024-12-18T01:36:49.7155329Z Example:: 2024-12-18T01:36:49.7155574Z 2024-12-18T01:36:49.7155982Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-12-18T01:36:49.7156730Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-12-18T01:36:49.7157389Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-12-18T01:36:49.7158049Z [ 5., 6., 7., 8.], 2024-12-18T01:36:49.7158655Z [ 9., 10., 11., 12.], 2024-12-18T01:36:49.7159269Z [13., 14., 15., 16.]]]]) 2024-12-18T01:36:49.7159896Z >>> output, indices = pool(input) 2024-12-18T01:36:49.7160501Z >>> unpool(output, indices) 2024-12-18T01:36:49.7161056Z tensor([[[[ 0., 0., 0., 0.], 2024-12-18T01:36:49.7161608Z [ 0., 6., 0., 8.], 2024-12-18T01:36:49.7162150Z [ 0., 0., 0., 0.], 2024-12-18T01:36:49.7162687Z [ 0., 14., 0., 16.]]]]) 2024-12-18T01:36:49.7163486Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-12-18T01:36:49.7164354Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-12-18T01:36:49.7165025Z [ 6., 7., 8., 9., 10.], 2024-12-18T01:36:49.7165611Z [11., 12., 13., 14., 15.], 2024-12-18T01:36:49.7166211Z [16., 17., 18., 19., 20.]]]]) 2024-12-18T01:36:49.7166836Z >>> output, indices = pool(input) 2024-12-18T01:36:49.7167560Z >>> # This call will not work without specifying output_size 2024-12-18T01:36:49.7168507Z >>> unpool(output, indices, output_size=input.size()) 2024-12-18T01:36:49.7169213Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-12-18T01:36:49.7169801Z [ 0., 7., 0., 9., 0.], 2024-12-18T01:36:49.7170364Z [ 0., 0., 0., 0., 0.], 2024-12-18T01:36:49.7170912Z [ 0., 17., 0., 19., 0.]]]]) 2024-12-18T01:36:49.7171317Z 2024-12-18T01:36:49.7171327Z 2024-12-18T01:36:49.7171484Z 2024-12-18T01:36:49.7172176Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.7172849Z 2024-12-18T01:36:49.7428463Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270. 2024-12-18T01:36:49.7430230Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.7431823Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-12-18T01:36:49.7432653Z 2024-12-18T01:36:49.7433318Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-12-18T01:36:49.7434272Z and with 2D inputs, this class 2024-12-18T01:36:49.7434628Z 2024-12-18T01:36:49.7435332Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-12-18T01:36:49.7436804Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-12-18T01:36:49.7438130Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-12-18T01:36:49.7438910Z 2024-12-18T01:36:49.7439578Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-12-18T01:36:49.7440631Z operations. 2024-12-18T01:36:49.7440895Z 2024-12-18T01:36:49.7441481Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-12-18T01:36:49.7442576Z pass. This scales the output of the Embedding before performing a weighted 2024-12-18T01:36:49.7443667Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-12-18T01:36:49.7444763Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-12-18T01:36:49.7445576Z :attr:`per_sample_weights`. 2024-12-18T01:36:49.7445923Z 2024-12-18T01:36:49.7446076Z Args: 2024-12-18T01:36:49.7446608Z num_embeddings (int): size of the dictionary of embeddings 2024-12-18T01:36:49.7447451Z embedding_dim (int): the size of each embedding vector 2024-12-18T01:36:49.7448541Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-12-18T01:36:49.7449599Z is renormalized to have norm :attr:`max_norm`. 2024-12-18T01:36:49.7450728Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-12-18T01:36:49.7452198Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-12-18T01:36:49.7453326Z the words in the mini-batch. Default ``False``. 2024-12-18T01:36:49.7454173Z Note: this option is not supported when ``mode="max"``. 2024-12-18T01:36:49.7455171Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-12-18T01:36:49.7456242Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-12-18T01:36:49.7457262Z into consideration. ``"mean"`` computes the average of the values 2024-12-18T01:36:49.7458221Z in the bag, ``"max"`` computes the max value over each bag. 2024-12-18T01:36:49.7458967Z Default: ``"mean"`` 2024-12-18T01:36:49.7460130Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-12-18T01:36:49.7461387Z Notes for more details regarding sparse gradients. Note: this option is not 2024-12-18T01:36:49.7462308Z supported when ``mode="max"``. 2024-12-18T01:36:49.7463341Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-12-18T01:36:49.7464690Z is equivalent to the size of `indices`. This matches the CSR format. 2024-12-18T01:36:49.7465934Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-12-18T01:36:49.7467262Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-12-18T01:36:49.7468545Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-12-18T01:36:49.7469720Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-12-18T01:36:49.7470881Z zeros, but can be updated to another value to be used as the padding vector. 2024-12-18T01:36:49.7472087Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-12-18T01:36:49.7472976Z reduction. 2024-12-18T01:36:49.7473386Z 2024-12-18T01:36:49.7473547Z Attributes: 2024-12-18T01:36:49.7474368Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-12-18T01:36:49.7475439Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-12-18T01:36:49.7476027Z 2024-12-18T01:36:49.7476218Z Examples:: 2024-12-18T01:36:49.7476470Z 2024-12-18T01:36:49.7476861Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-12-18T01:36:49.7477675Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-12-18T01:36:49.7478394Z >>> # a batch of 2 samples of 4 indices each 2024-12-18T01:36:49.7479156Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:36:49.7479805Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:36:49.7480461Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:49.7481115Z >>> embedding_sum(input, offsets) 2024-12-18T01:36:49.7481719Z tensor([[-0.8861, -5.4350, -0.0523], 2024-12-18T01:36:49.7482285Z [ 1.1306, -2.5798, -1.0044]]) 2024-12-18T01:36:49.7482663Z 2024-12-18T01:36:49.7482870Z >>> # Example with padding_idx 2024-12-18T01:36:49.7483619Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-12-18T01:36:49.7484530Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:36:49.7485318Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:36:49.7485986Z >>> embedding_sum(input, offsets) 2024-12-18T01:36:49.7486557Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-12-18T01:36:49.7487120Z [-0.7082, 3.2145, -2.6251]]) 2024-12-18T01:36:49.7487495Z 2024-12-18T01:36:49.7487815Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-12-18T01:36:49.7488609Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-12-18T01:36:49.7489367Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-12-18T01:36:49.7490035Z embedding.weight, 2024-12-18T01:36:49.7490618Z padding_idx=embedding.padding_idx, 2024-12-18T01:36:49.7491209Z mode='sum') 2024-12-18T01:36:49.7491667Z 2024-12-18T01:36:49.7492302Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.7492992Z 2024-12-18T01:36:49.7841099Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-12-18T01:36:49.7843382Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.7844114Z 2024-12-18T01:36:49.7844576Z Context manager for training with uneven inputs across processes in DDP. 2024-12-18T01:36:49.7845249Z 2024-12-18T01:36:49.7845689Z This context manager will keep track of already-joined DDP processes, 2024-12-18T01:36:49.7846730Z and "shadow" the forward and backward passes by inserting collective 2024-12-18T01:36:49.7847772Z communication operations to match with the ones created by non-joined 2024-12-18T01:36:49.7848875Z DDP processes. This will ensure each collective call has a corresponding 2024-12-18T01:36:49.7850079Z call by already-joined DDP processes, preventing hangs or errors that 2024-12-18T01:36:49.7851040Z would otherwise happen when training with uneven inputs across 2024-12-18T01:36:49.7852026Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-12-18T01:36:49.7853076Z specified to be ``True``, all trainers will throw an error once one rank 2024-12-18T01:36:49.7854059Z runs out of inputs, allowing these errors to be caught and handled 2024-12-18T01:36:49.7873415Z according to application logic. 2024-12-18T01:36:49.7873883Z 2024-12-18T01:36:49.7874319Z Once all DDP processes have joined, the context manager will broadcast 2024-12-18T01:36:49.7875371Z the model corresponding to the last joined process to all processes to 2024-12-18T01:36:49.7876352Z ensure the model is the same across all processes 2024-12-18T01:36:49.7877013Z (which is guaranteed by DDP). 2024-12-18T01:36:49.7877366Z 2024-12-18T01:36:49.7877698Z To use this to enable training with uneven inputs across processes, 2024-12-18T01:36:49.7878688Z simply wrap this context manager around your training loop. No further 2024-12-18T01:36:49.7879756Z modifications to the model or data loading is required. 2024-12-18T01:36:49.7880313Z 2024-12-18T01:36:49.7880520Z .. warning:: 2024-12-18T01:36:49.7881110Z If the model or training loop this context manager is wrapped around 2024-12-18T01:36:49.7882063Z has additional distributed collective operations, such as 2024-12-18T01:36:49.7882986Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-12-18T01:36:49.7883918Z ``throw_on_early_termination`` must be enabled. This is because this 2024-12-18T01:36:49.7884908Z context manager is not aware of non-DDP collective communication. 2024-12-18T01:36:49.7885734Z This flag will cause all ranks to throw when any one rank 2024-12-18T01:36:49.7886563Z exhausts inputs, allowing these errors to be caught and recovered 2024-12-18T01:36:49.7887312Z from across all ranks. 2024-12-18T01:36:49.7887652Z 2024-12-18T01:36:49.7887801Z Args: 2024-12-18T01:36:49.7888341Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-12-18T01:36:49.7889295Z gradients by the initial ``world_size`` DDP training was launched 2024-12-18T01:36:49.7890210Z with. If ``False``, will compute the effective world size 2024-12-18T01:36:49.7891084Z (number of ranks that have not depleted their inputs yet) and 2024-12-18T01:36:49.7891912Z divide gradients by that during allreduce. Set 2024-12-18T01:36:49.7892730Z ``divide_by_initial_world_size=True`` to ensure every input 2024-12-18T01:36:49.7893666Z sample including the uneven inputs have equal weight in terms of 2024-12-18T01:36:49.7894592Z how much they contribute to the global gradient. This is 2024-12-18T01:36:49.7895421Z achieved by always dividing the gradient by the initial 2024-12-18T01:36:49.7896293Z ``world_size`` even when we encounter uneven inputs. If you set 2024-12-18T01:36:49.7897162Z this to ``False``, we divide the gradient by the remaining 2024-12-18T01:36:49.7898281Z number of nodes. This ensures parity with training on a smaller 2024-12-18T01:36:49.7899334Z ``world_size`` although it also means the uneven inputs would 2024-12-18T01:36:49.7900108Z contribute more towards the global gradient. Typically, you 2024-12-18T01:36:49.7900614Z would want to set this to ``True`` for cases where the last few 2024-12-18T01:36:49.7901131Z inputs of your training job are uneven. In extreme cases, where 2024-12-18T01:36:49.7901894Z there is a large discrepancy in the number of inputs, setting 2024-12-18T01:36:49.7902349Z this to ``False`` might provide better results. 2024-12-18T01:36:49.7902820Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-12-18T01:36:49.7903313Z in ``enable=False`` to disable in cases where you know that 2024-12-18T01:36:49.7903797Z inputs are even across participating processes. Default is 2024-12-18T01:36:49.7904297Z ``True``. 2024-12-18T01:36:49.7904637Z throw_on_early_termination (bool): Whether to throw an error 2024-12-18T01:36:49.7905123Z or continue training when at least one rank has exhausted 2024-12-18T01:36:49.7905591Z inputs. If ``True``, will throw upon the first rank reaching end 2024-12-18T01:36:49.7906075Z of data. If ``False``, will continue training with a smaller 2024-12-18T01:36:49.7906858Z effective world size until all ranks are joined. Note that if 2024-12-18T01:36:49.7907607Z this flag is specified, then the flag 2024-12-18T01:36:49.7908339Z ``divide_by_initial_world_size`` would be ignored. Default 2024-12-18T01:36:49.7909014Z is ``False``. 2024-12-18T01:36:49.7909290Z 2024-12-18T01:36:49.7909298Z 2024-12-18T01:36:49.7909478Z Example:: 2024-12-18T01:36:49.7909716Z 2024-12-18T01:36:49.7909919Z >>> # xdoctest: +SKIP("Distributed") 2024-12-18T01:36:49.7910500Z >>> import torch 2024-12-18T01:36:49.7910970Z >>> import torch.distributed as dist 2024-12-18T01:36:49.7911495Z >>> import os 2024-12-18T01:36:49.7912058Z >>> import torch.multiprocessing as mp 2024-12-18T01:36:49.7912677Z >>> import torch.nn as nn 2024-12-18T01:36:49.7913190Z >>> # On each spawned worker 2024-12-18T01:36:49.7913715Z >>> def worker(rank): 2024-12-18T01:36:49.7914340Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-12-18T01:36:49.7915044Z >>> torch.cuda.set_device(rank) 2024-12-18T01:36:49.7915754Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:36:49.7916520Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:36:49.7917290Z >>> model, device_ids=[rank], output_device=rank 2024-12-18T01:36:49.7917909Z >>> ) 2024-12-18T01:36:49.7918351Z >>> # Rank 1 gets one more input than rank 0. 2024-12-18T01:36:49.7919081Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-12-18T01:36:49.7919785Z >>> with model.join(): 2024-12-18T01:36:49.7920270Z >>> for _ in range(5): 2024-12-18T01:36:49.7920825Z >>> for inp in inputs: 2024-12-18T01:36:49.7921368Z >>> loss = model(inp).sum() 2024-12-18T01:36:49.7921876Z >>> loss.backward() 2024-12-18T01:36:49.7922546Z >>> # Without the join() API, the below synchronization will hang 2024-12-18T01:36:49.7923370Z >>> # blocking for rank 1's allreduce to complete. 2024-12-18T01:36:49.7924046Z >>> torch.cuda.synchronize(device=rank) 2024-12-18T01:36:49.7924490Z 2024-12-18T01:36:49.7924933Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.7925605Z 2024-12-18T01:36:49.7926596Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-12-18T01:36:49.7927680Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.7928051Z 2024-12-18T01:36:49.7928354Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-12-18T01:36:49.7928870Z 2024-12-18T01:36:49.7929075Z Registers an optimizer with DDP such that the optimization for a 2024-12-18T01:36:49.7929602Z parameter will run immediately when that parameter's gradient is 2024-12-18T01:36:49.7930134Z finished with reduction, instead of waiting for all parameters' 2024-12-18T01:36:49.7930672Z gradients to finish reduction. This can result in a training speedup 2024-12-18T01:36:49.7931227Z depending on your workload since the optimizer can run while gradient 2024-12-18T01:36:49.7931772Z reduction for other parameters are still ongoing. In addition, this has 2024-12-18T01:36:49.7932507Z the potential to reduce peak memory consumption during training, as it 2024-12-18T01:36:49.7933416Z only needs to load the per-parameter optimizer states of a single 2024-12-18T01:36:49.7934451Z parameter at a time, instead of loading all per-parameter optimizer 2024-12-18T01:36:49.7935215Z states at once. 2024-12-18T01:36:49.7935461Z 2024-12-18T01:36:49.7935624Z Args: 2024-12-18T01:36:49.7936160Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-12-18T01:36:49.7936905Z as a fused optimizer. 2024-12-18T01:36:49.7937476Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-12-18T01:36:49.7938450Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-12-18T01:36:49.7939462Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-12-18T01:36:49.7940444Z Optimizers. If this is omitted, all DDP model parameters will be 2024-12-18T01:36:49.7941191Z optimized. 2024-12-18T01:36:49.7941752Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-12-18T01:36:49.7942331Z 2024-12-18T01:36:49.7942535Z .. warning :: 2024-12-18T01:36:49.7943169Z _register_fused_optim should only be called once on a DDP instance, 2024-12-18T01:36:49.7944222Z and registering multiple fused optimizers for the same DDP model 2024-12-18T01:36:49.7945070Z is not currently supported. Please ping 2024-12-18T01:36:49.7945937Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:36:49.7946762Z for your use case. 2024-12-18T01:36:49.7947046Z 2024-12-18T01:36:49.7947228Z .. warning :: 2024-12-18T01:36:49.7947830Z _register_fused_optim and register_comm_hook currently do not 2024-12-18T01:36:49.7948752Z compose together, meaning that custom DDP communication hooks are 2024-12-18T01:36:49.7949656Z not supported with overlapped optimizers. Please ping 2024-12-18T01:36:49.7950615Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:36:49.7951379Z for your use case. 2024-12-18T01:36:49.7951668Z 2024-12-18T01:36:49.7951837Z .. warning :: 2024-12-18T01:36:49.7952412Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-12-18T01:36:49.7952964Z with overlapped optimizer. Please ping 2024-12-18T01:36:49.7953440Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:36:49.7953884Z for your use case. 2024-12-18T01:36:49.7954041Z 2024-12-18T01:36:49.7954151Z Example:: 2024-12-18T01:36:49.7954275Z 2024-12-18T01:36:49.7954417Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-12-18T01:36:49.7954943Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-12-18T01:36:49.7955569Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-12-18T01:36:49.7956092Z >>> lr = 1e-2 2024-12-18T01:36:49.7956337Z >>> betas = (0.9, 0.99) 2024-12-18T01:36:49.7956614Z >>> eps = 1e-6 2024-12-18T01:36:49.7956977Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-12-18T01:36:49.7957441Z >>> # Example with subset of parameters 2024-12-18T01:36:49.7957811Z >>> params_to_opt = [list(net.parameters())[0]] 2024-12-18T01:36:49.7958172Z >>> net._register_fused_optim( 2024-12-18T01:36:49.7958614Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-12-18T01:36:49.7959127Z ... ) 2024-12-18T01:36:49.7959267Z 2024-12-18T01:36:49.7959515Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.7959890Z 2024-12-18T01:36:49.8412470Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6. 2024-12-18T01:36:49.8414409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.8415537Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-12-18T01:36:49.8416176Z 2024-12-18T01:36:49.8416728Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:36:49.8418120Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:36:49.8419265Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:36:49.8420272Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:36:49.8420702Z 2024-12-18T01:36:49.8420829Z .. note:: 2024-12-18T01:36:49.8421203Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-12-18T01:36:49.8421898Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-12-18T01:36:49.8422453Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:36:49.8423009Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:36:49.8423560Z One place we are confident in is that NHWC(channels_last) conversion for 2024-12-18T01:36:49.8424121Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-12-18T01:36:49.8424668Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:36:49.8425036Z 2024-12-18T01:36:49.8425282Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:36:49.8425738Z channels_last. This ensures that; 2024-12-18T01:36:49.8426174Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:36:49.8426939Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:36:49.8427976Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:36:49.8428794Z from memory_format conversion. 2024-12-18T01:36:49.8429167Z 2024-12-18T01:36:49.8429576Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:36:49.8430617Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:36:49.8431689Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:36:49.8432757Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:36:49.8433436Z 2024-12-18T01:36:49.8433831Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:36:49.8434854Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:36:49.8435899Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:36:49.8436974Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:36:49.8438006Z another convolution layer. There's no point in propagating that 2024-12-18T01:36:49.8438991Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:36:49.8439805Z ``memory_format``. 2024-12-18T01:36:49.8440115Z 2024-12-18T01:36:49.8440540Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:36:49.8441607Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:36:49.8442470Z immediately before a convolution. 2024-12-18T01:36:49.8443089Z 2024-12-18T01:36:49.8443241Z Args: 2024-12-18T01:36:49.8443833Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-12-18T01:36:49.8444626Z ``nn.Module`` 2024-12-18T01:36:49.8445113Z memory_format: user specified ``memory_format``, 2024-12-18T01:36:49.8445833Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:36:49.8446374Z 2024-12-18T01:36:49.8446540Z Returns: 2024-12-18T01:36:49.8447023Z The original module with updated ``nn.Conv2d`` 2024-12-18T01:36:49.8447488Z 2024-12-18T01:36:49.8447640Z Example: 2024-12-18T01:36:49.8448112Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:49.8448826Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:36:49.8449807Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:36:49.8450624Z >>> model = nn.Sequential( 2024-12-18T01:36:49.8451196Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-12-18T01:36:49.8451783Z >>> # This is identical to: 2024-12-18T01:36:49.8452575Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:36:49.8453770Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:36:49.8454657Z >>> out = model(input) 2024-12-18T01:36:49.8455108Z 2024-12-18T01:36:49.8455764Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.8456466Z 2024-12-18T01:36:49.8457682Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81. 2024-12-18T01:36:49.8459526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.8460637Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-12-18T01:36:49.8461774Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:36:49.8462966Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:36:49.8464127Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:36:49.8465244Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:36:49.8466019Z 2024-12-18T01:36:49.8466212Z .. note:: 2024-12-18T01:36:49.8466903Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-12-18T01:36:49.8467985Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-12-18T01:36:49.8469018Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:36:49.8470049Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:36:49.8471071Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-12-18T01:36:49.8472106Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-12-18T01:36:49.8473151Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:36:49.8473755Z 2024-12-18T01:36:49.8474199Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:36:49.8475086Z channels_last_3d. This ensures that; 2024-12-18T01:36:49.8476009Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:36:49.8477064Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:36:49.8478184Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:36:49.8479070Z from memory_format conversion. 2024-12-18T01:36:49.8479400Z 2024-12-18T01:36:49.8479820Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:36:49.8480963Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:36:49.8482061Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:36:49.8483096Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:36:49.8483752Z 2024-12-18T01:36:49.8484142Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:36:49.8485150Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:36:49.8486172Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:36:49.8487219Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:36:49.8488374Z another convolution layer. There's no point in propagating that 2024-12-18T01:36:49.8489392Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:36:49.8490206Z ``memory_format``. 2024-12-18T01:36:49.8490535Z 2024-12-18T01:36:49.8490926Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:36:49.8491978Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:36:49.8492923Z immediately before a convolution. 2024-12-18T01:36:49.8493341Z 2024-12-18T01:36:49.8493514Z Args: 2024-12-18T01:36:49.8494078Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-12-18T01:36:49.8494871Z ``nn.Module`` 2024-12-18T01:36:49.8495514Z memory_format: user specified ``memory_format``, 2024-12-18T01:36:49.8496319Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:36:49.8496863Z 2024-12-18T01:36:49.8497041Z Returns: 2024-12-18T01:36:49.8497510Z The original module with updated ``nn.Conv3d`` 2024-12-18T01:36:49.8498240Z 2024-12-18T01:36:49.8498535Z Example: 2024-12-18T01:36:49.8499025Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:49.8499785Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:36:49.8500719Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:36:49.8501896Z >>> model = nn.Sequential( 2024-12-18T01:36:49.8502466Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-12-18T01:36:49.8503069Z >>> # This is identical to: 2024-12-18T01:36:49.8503864Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:36:49.8505041Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:36:49.8505973Z >>> out = model(input) 2024-12-18T01:36:49.8506453Z 2024-12-18T01:36:49.8507098Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.8507797Z 2024-12-18T01:36:49.8690463Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=936. 2024-12-18T01:36:49.8692226Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.8693390Z Prune tensor by removing random channels along the specified dimension. 2024-12-18T01:36:49.8694002Z 2024-12-18T01:36:49.8694411Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:36:49.8695438Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:36:49.8696327Z along the specified ``dim`` selected at random. 2024-12-18T01:36:49.8697154Z Modifies module in place (and also return the modified module) 2024-12-18T01:36:49.8698087Z by: 2024-12-18T01:36:49.8698305Z 2024-12-18T01:36:49.8698710Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:49.8699746Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:49.8700986Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:49.8702312Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:49.8703105Z ``name+'_orig'``. 2024-12-18T01:36:49.8703424Z 2024-12-18T01:36:49.8703572Z Args: 2024-12-18T01:36:49.8704125Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:36:49.8704903Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:36:49.8705544Z will act. 2024-12-18T01:36:49.8706160Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:36:49.8707020Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:36:49.8707928Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:36:49.8708902Z absolute number of parameters to prune. 2024-12-18T01:36:49.8709696Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:36:49.8710280Z 2024-12-18T01:36:49.8710436Z Returns: 2024-12-18T01:36:49.8711077Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:36:49.8711667Z 2024-12-18T01:36:49.8711814Z Examples: 2024-12-18T01:36:49.8712342Z >>> # xdoctest: +SKIP 2024-12-18T01:36:49.8712897Z >>> m = prune.random_structured( 2024-12-18T01:36:49.8713525Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-12-18T01:36:49.8714138Z ... ) 2024-12-18T01:36:49.8714688Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-12-18T01:36:49.8715427Z >>> print(columns_pruned) 2024-12-18T01:36:49.8716036Z 3 2024-12-18T01:36:49.8716389Z 2024-12-18T01:36:49.8717026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.8717709Z 2024-12-18T01:36:49.8718870Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=977. 2024-12-18T01:36:49.8720536Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.8721789Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-12-18T01:36:49.8722613Z 2024-12-18T01:36:49.8723056Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:36:49.8724069Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:36:49.8725024Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-12-18T01:36:49.8725916Z Modifies module in place (and also return the modified module) 2024-12-18T01:36:49.8726652Z by: 2024-12-18T01:36:49.8726871Z 2024-12-18T01:36:49.8727255Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:49.8728278Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:49.8729310Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:49.8730307Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:49.8731106Z ``name+'_orig'``. 2024-12-18T01:36:49.8731406Z 2024-12-18T01:36:49.8731561Z Args: 2024-12-18T01:36:49.8732122Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:36:49.8733014Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:36:49.8733750Z will act. 2024-12-18T01:36:49.8734369Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:36:49.8735241Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:36:49.8736159Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:36:49.8737016Z absolute number of parameters to prune. 2024-12-18T01:36:49.8737824Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-12-18T01:36:49.8738814Z entries for argument ``p`` in :func:`torch.norm`. 2024-12-18T01:36:49.8739696Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:36:49.8740729Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-12-18T01:36:49.8741729Z shape as module parameter) used to compute mask for pruning. 2024-12-18T01:36:49.8742576Z The values in this tensor indicate the importance of the corresponding 2024-12-18T01:36:49.8743057Z elements in the parameter being pruned. 2024-12-18T01:36:49.8743533Z If unspecified or None, the module parameter will be used in its place. 2024-12-18T01:36:49.8743876Z 2024-12-18T01:36:49.8743982Z Returns: 2024-12-18T01:36:49.8744410Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:36:49.8744757Z 2024-12-18T01:36:49.8744849Z Examples: 2024-12-18T01:36:49.8745125Z >>> from torch.nn.utils import prune 2024-12-18T01:36:49.8745468Z >>> m = prune.ln_structured( 2024-12-18T01:36:49.8745867Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-12-18T01:36:49.8746246Z ... ) 2024-12-18T01:36:49.8746470Z 2024-12-18T01:36:49.8746885Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.8747257Z 2024-12-18T01:36:49.8747783Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1024. 2024-12-18T01:36:49.8748679Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.8749045Z 2024-12-18T01:36:49.8749472Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-12-18T01:36:49.8750363Z 2024-12-18T01:36:49.8750573Z Modifies modules in place by: 2024-12-18T01:36:49.8750997Z 2024-12-18T01:36:49.8751348Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:49.8752305Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:49.8753303Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:49.8754267Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:49.8755028Z ``name+'_orig'``. 2024-12-18T01:36:49.8755299Z 2024-12-18T01:36:49.8755439Z Args: 2024-12-18T01:36:49.8756104Z parameters (Iterable of (module, name) tuples): parameters of 2024-12-18T01:36:49.8757033Z the model to prune in a global fashion, i.e. by aggregating all 2024-12-18T01:36:49.8757990Z weights prior to deciding which ones to prune. module must be of 2024-12-18T01:36:49.8758862Z type :class:`nn.Module`, and name must be a string. 2024-12-18T01:36:49.8759668Z pruning_method (function): a valid pruning function from this module, 2024-12-18T01:36:49.8760615Z or a custom one implemented by the user that satisfies the 2024-12-18T01:36:49.8761570Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-12-18T01:36:49.8762612Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-12-18T01:36:49.8763675Z the corresponding parameter's importance scores tensor. The tensor 2024-12-18T01:36:49.8764697Z should be the same shape as the parameter, and is used for computing 2024-12-18T01:36:49.8765480Z mask for pruning. 2024-12-18T01:36:49.8766155Z If unspecified or None, the parameter will be used in place of its 2024-12-18T01:36:49.8766923Z importance scores. 2024-12-18T01:36:49.8767448Z kwargs: other keyword arguments such as: 2024-12-18T01:36:49.8768231Z amount (int or float): quantity of parameters to prune across the 2024-12-18T01:36:49.8769009Z specified parameters. 2024-12-18T01:36:49.8769602Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:36:49.8770582Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:36:49.8771388Z absolute number of parameters to prune. 2024-12-18T01:36:49.8771825Z 2024-12-18T01:36:49.8771983Z Raises: 2024-12-18T01:36:49.8772456Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-12-18T01:36:49.8772935Z 2024-12-18T01:36:49.8773079Z Note: 2024-12-18T01:36:49.8773668Z Since global structured pruning doesn't make much sense unless the 2024-12-18T01:36:49.8774632Z norm is normalized by the size of the parameter, we now limit the 2024-12-18T01:36:49.8775470Z scope of global pruning to unstructured methods. 2024-12-18T01:36:49.8775929Z 2024-12-18T01:36:49.8776091Z Examples: 2024-12-18T01:36:49.8776510Z >>> from torch.nn.utils import prune 2024-12-18T01:36:49.8777173Z >>> from collections import OrderedDict 2024-12-18T01:36:49.8777781Z >>> net = nn.Sequential(OrderedDict([ 2024-12-18T01:36:49.8778355Z ... ('first', nn.Linear(10, 4)), 2024-12-18T01:36:49.8778917Z ... ('second', nn.Linear(4, 1)), 2024-12-18T01:36:49.8779434Z ... ])) 2024-12-18T01:36:49.8779829Z >>> parameters_to_prune = ( 2024-12-18T01:36:49.8780372Z ... (net.first, 'weight'), 2024-12-18T01:36:49.8780886Z ... (net.second, 'weight'), 2024-12-18T01:36:49.8781395Z ... ) 2024-12-18T01:36:49.8781887Z >>> prune.global_unstructured( 2024-12-18T01:36:49.8782453Z ... parameters_to_prune, 2024-12-18T01:36:49.8782970Z ... pruning_method=prune.L1Unstructured, 2024-12-18T01:36:49.8783573Z ... amount=10, 2024-12-18T01:36:49.8784010Z ... ) 2024-12-18T01:36:49.8784582Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-12-18T01:36:49.8785292Z tensor(10) 2024-12-18T01:36:49.8785544Z 2024-12-18T01:36:49.8785558Z 2024-12-18T01:36:49.8786027Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.8786723Z 2024-12-18T01:36:49.8787802Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1143. 2024-12-18T01:36:49.8789467Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:49.8790824Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-12-18T01:36:49.8791784Z 2024-12-18T01:36:49.8792198Z Modifies module in place (and also return the modified module) by: 2024-12-18T01:36:49.8792802Z 2024-12-18T01:36:49.8793191Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:49.8794196Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:49.8795209Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:49.8796332Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:49.8797122Z ``name+'_orig'``. 2024-12-18T01:36:49.8797423Z 2024-12-18T01:36:49.8797587Z Args: 2024-12-18T01:36:49.8798301Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:36:49.8799201Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:36:49.8799947Z will act. 2024-12-18T01:36:49.8800575Z mask (Tensor): binary mask to be applied to the parameter. 2024-12-18T01:36:49.8801119Z 2024-12-18T01:36:49.8801295Z Returns: 2024-12-18T01:36:49.8802227Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:36:49.8802865Z 2024-12-18T01:36:49.8803025Z Examples: 2024-12-18T01:36:49.8803492Z >>> from torch.nn.utils import prune 2024-12-18T01:36:49.8804124Z >>> m = prune.custom_from_mask( 2024-12-18T01:36:49.8804839Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-12-18T01:36:49.8805547Z ... ) 2024-12-18T01:36:49.8805954Z >>> print(m.bias_mask) 2024-12-18T01:36:49.8806602Z tensor([0., 1., 0.]) 2024-12-18T01:36:49.8806937Z 2024-12-18T01:36:49.8807085Z 2024-12-18T01:36:49.8807758Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:49.8808463Z 2024-12-18T01:36:50.0028848Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=116. 2024-12-18T01:36:50.0030521Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:50.0031861Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-12-18T01:36:50.0032750Z 2024-12-18T01:36:50.0033156Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-12-18T01:36:50.0034222Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-12-18T01:36:50.0035538Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-12-18T01:36:50.0036452Z (UAI 2018). 2024-12-18T01:36:50.0036715Z 2024-12-18T01:36:50.0037179Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-12-18T01:36:50.0038221Z but using exponential weights instead of equal weights across iterations. 2024-12-18T01:36:50.0038884Z 2024-12-18T01:36:50.0039450Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-12-18T01:36:50.0040486Z on the device :attr:`device` and allows to compute running averages of the 2024-12-18T01:36:50.0041326Z parameters of the :attr:`model`. 2024-12-18T01:36:50.0041728Z 2024-12-18T01:36:50.0041886Z Args: 2024-12-18T01:36:50.0042335Z model (torch.nn.Module): model to use with SWA/EMA 2024-12-18T01:36:50.0043152Z device (torch.device, optional): if provided, the averaged model will be 2024-12-18T01:36:50.0043979Z stored on the :attr:`device` 2024-12-18T01:36:50.0044721Z avg_fn (function, optional): the averaging function used to update 2024-12-18T01:36:50.0045737Z parameters; the function must take in the current value of the 2024-12-18T01:36:50.0046291Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-12-18T01:36:50.0046834Z parameter, and the number of models already averaged; if None, 2024-12-18T01:36:50.0047323Z an equally weighted average is used (default: None) 2024-12-18T01:36:50.0047833Z multi_avg_fn (function, optional): the averaging function used to update 2024-12-18T01:36:50.0048401Z parameters inplace; the function must take in the current values of the 2024-12-18T01:36:50.0049030Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-12-18T01:36:50.0049638Z parameters as a list, and the number of models already averaged; if None, 2024-12-18T01:36:50.0050155Z an equally weighted average is used (default: None) 2024-12-18T01:36:50.0050637Z use_buffers (bool): if ``True``, it will compute running averages for 2024-12-18T01:36:50.0051187Z both the parameters and the buffers of the model. (default: ``False``) 2024-12-18T01:36:50.0051630Z 2024-12-18T01:36:50.0051771Z Example: 2024-12-18T01:36:50.0052211Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:50.0052873Z >>> loader, optimizer, model, loss_fn = ... 2024-12-18T01:36:50.0053614Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-12-18T01:36:50.0054539Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-12-18T01:36:50.0055296Z >>> T_max=300) 2024-12-18T01:36:50.0055892Z >>> swa_start = 160 2024-12-18T01:36:50.0056469Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-12-18T01:36:50.0057130Z >>> for i in range(300): 2024-12-18T01:36:50.0057700Z >>> for input, target in loader: 2024-12-18T01:36:50.0058290Z >>> optimizer.zero_grad() 2024-12-18T01:36:50.0059070Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:36:50.0059686Z >>> optimizer.step() 2024-12-18T01:36:50.0060237Z >>> if i > swa_start: 2024-12-18T01:36:50.0060780Z >>> swa_model.update_parameters(model) 2024-12-18T01:36:50.0061173Z >>> swa_scheduler.step() 2024-12-18T01:36:50.0061488Z >>> else: 2024-12-18T01:36:50.0061755Z >>> scheduler.step() 2024-12-18T01:36:50.0062054Z >>> 2024-12-18T01:36:50.0062342Z >>> # Update bn statistics for the swa_model at the end 2024-12-18T01:36:50.0062769Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-12-18T01:36:50.0063057Z 2024-12-18T01:36:50.0063351Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-12-18T01:36:50.0064039Z If no averaging function is provided, the default is to compute 2024-12-18T01:36:50.0064506Z equally-weighted average of the weights (SWA). 2024-12-18T01:36:50.0064769Z 2024-12-18T01:36:50.0064875Z Example: 2024-12-18T01:36:50.0065149Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:50.0065595Z >>> # Compute exponential moving averages of the weights and buffers 2024-12-18T01:36:50.0066181Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-12-18T01:36:50.0067048Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-12-18T01:36:50.0067670Z 2024-12-18T01:36:50.0067860Z .. note:: 2024-12-18T01:36:50.0068492Z When using SWA/EMA with models containing Batch Normalization you may 2024-12-18T01:36:50.0069478Z need to update the activation statistics for Batch Normalization. 2024-12-18T01:36:50.0070495Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-12-18T01:36:50.0071633Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-12-18T01:36:50.0072726Z statistics in a post-training step by passing data through the model. The 2024-12-18T01:36:50.0073830Z second does it during the parameter update phase by averaging all buffers. 2024-12-18T01:36:50.0074968Z Empirical evidence has shown that updating the statistics in normalization 2024-12-18T01:36:50.0076211Z layers increases accuracy, but you may wish to empirically test which 2024-12-18T01:36:50.0077142Z approach yields the best results in your problem. 2024-12-18T01:36:50.0077616Z 2024-12-18T01:36:50.0077763Z .. note:: 2024-12-18T01:36:50.0078418Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-12-18T01:36:50.0079127Z 2024-12-18T01:36:50.0079274Z .. note:: 2024-12-18T01:36:50.0079851Z When :meth:`update_parameters` is called for the first time (i.e. 2024-12-18T01:36:50.0080765Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-12-18T01:36:50.0081684Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-12-18T01:36:50.0082643Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-12-18T01:36:50.0083414Z to update the parameters. 2024-12-18T01:36:50.0083762Z 2024-12-18T01:36:50.0084181Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:36:50.0085026Z https://arxiv.org/abs/1803.05407 2024-12-18T01:36:50.0085883Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-12-18T01:36:50.0086706Z Average: 2024-12-18T01:36:50.0087171Z https://arxiv.org/abs/1806.05594 2024-12-18T01:36:50.0087952Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-12-18T01:36:50.0088753Z https://arxiv.org/abs/1904.11943 2024-12-18T01:36:50.0089594Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-12-18T01:36:50.0090406Z Generalizes Well: 2024-12-18T01:36:50.0090940Z https://arxiv.org/abs/2001.02312 2024-12-18T01:36:50.0091663Z .. _Polyak averaging: 2024-12-18T01:36:50.0092287Z https://paperswithcode.com/method/polyak-averaging 2024-12-18T01:36:50.0092962Z 2024-12-18T01:36:50.0093583Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:50.0094228Z 2024-12-18T01:36:50.0094784Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=368. 2024-12-18T01:36:50.0095627Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:50.0096218Z Anneals the learning rate in each parameter group to a fixed value. 2024-12-18T01:36:50.0096555Z 2024-12-18T01:36:50.0096784Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-12-18T01:36:50.0097424Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-12-18T01:36:50.0097746Z 2024-12-18T01:36:50.0098101Z Args: 2024-12-18T01:36:50.0098406Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-12-18T01:36:50.0098909Z swa_lrs (float or list): the learning rate value for all param groups 2024-12-18T01:36:50.0099367Z together or separately for each group. 2024-12-18T01:36:50.0099919Z annealing_epochs (int): number of epochs in the annealing phase 2024-12-18T01:36:50.0100340Z (default: 10) 2024-12-18T01:36:50.0100716Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-12-18T01:36:50.0101258Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-12-18T01:36:50.0101929Z (default: "cos") 2024-12-18T01:36:50.0102295Z last_epoch (int): the index of the last epoch (default: -1) 2024-12-18T01:36:50.0102586Z 2024-12-18T01:36:50.0102784Z The :class:`SWALR` scheduler can be used together with other 2024-12-18T01:36:50.0103351Z schedulers to switch to a constant learning rate late in the training 2024-12-18T01:36:50.0103777Z as in the example below. 2024-12-18T01:36:50.0103969Z 2024-12-18T01:36:50.0104060Z Example: 2024-12-18T01:36:50.0104339Z >>> # xdoctest: +SKIP("Undefined variables") 2024-12-18T01:36:50.0104900Z >>> loader, optimizer, model = ... 2024-12-18T01:36:50.0105479Z >>> lr_lambda = lambda epoch: 0.9 2024-12-18T01:36:50.0106250Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-12-18T01:36:50.0107057Z >>> lr_lambda=lr_lambda) 2024-12-18T01:36:50.0107739Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-12-18T01:36:50.0108589Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-12-18T01:36:50.0109299Z >>> swa_start = 160 2024-12-18T01:36:50.0109772Z >>> for i in range(300): 2024-12-18T01:36:50.0110288Z >>> for input, target in loader: 2024-12-18T01:36:50.0110899Z >>> optimizer.zero_grad() 2024-12-18T01:36:50.0111539Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:36:50.0112174Z >>> optimizer.step() 2024-12-18T01:36:50.0112698Z >>> if i > swa_start: 2024-12-18T01:36:50.0113220Z >>> swa_scheduler.step() 2024-12-18T01:36:50.0113724Z >>> else: 2024-12-18T01:36:50.0114018Z >>> scheduler.step() 2024-12-18T01:36:50.0114227Z 2024-12-18T01:36:50.0114467Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:36:50.0114949Z https://arxiv.org/abs/1803.05407 2024-12-18T01:36:50.0115252Z 2024-12-18T01:36:50.0115725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:50.0116106Z 2024-12-18T01:36:50.5156859Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1274. 2024-12-18T01:36:50.5158737Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:50.5159942Z Asserts that ``actual`` and ``expected`` are close. 2024-12-18T01:36:50.5160433Z 2024-12-18T01:36:50.5161104Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-12-18T01:36:50.5162009Z 2024-12-18T01:36:50.5162188Z .. math:: 2024-12-18T01:36:50.5162436Z 2024-12-18T01:36:50.5163132Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-12-18T01:36:50.5164021Z 2024-12-18T01:36:50.5164671Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-12-18T01:36:50.5165909Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-12-18T01:36:50.5167217Z 2024-12-18T01:36:50.5167605Z In addition, they are only considered close if they have the same 2024-12-18T01:36:50.5168189Z 2024-12-18T01:36:50.5168544Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-12-18T01:36:50.5169349Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-12-18T01:36:50.5170035Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-12-18T01:36:50.5170731Z - stride (if ``check_stride`` is ``True``). 2024-12-18T01:36:50.5171167Z 2024-12-18T01:36:50.5171879Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-12-18T01:36:50.5172670Z 2024-12-18T01:36:50.5173315Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-12-18T01:36:50.5174804Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-12-18T01:36:50.5176164Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-12-18T01:36:50.5177681Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-12-18T01:36:50.5178649Z 2024-12-18T01:36:50.5179090Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-12-18T01:36:50.5180432Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-12-18T01:36:50.5181413Z definition above. 2024-12-18T01:36:50.5181727Z 2024-12-18T01:36:50.5182327Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-12-18T01:36:50.5183845Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-12-18T01:36:50.5185504Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-12-18T01:36:50.5187142Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-12-18T01:36:50.5188540Z their elements are considered close according to the above definition. 2024-12-18T01:36:50.5189207Z 2024-12-18T01:36:50.5189363Z .. note:: 2024-12-18T01:36:50.5189566Z 2024-12-18T01:36:50.5190181Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-12-18T01:36:50.5191613Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-12-18T01:36:50.5192949Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-12-18T01:36:50.5193661Z 2024-12-18T01:36:50.5193809Z Args: 2024-12-18T01:36:50.5194197Z actual (Any): Actual input. 2024-12-18T01:36:50.5194725Z expected (Any): Expected input. 2024-12-18T01:36:50.5195870Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-12-18T01:36:50.5197033Z are allowed. Otherwise type equality is required. 2024-12-18T01:36:50.5198606Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-12-18T01:36:50.5199858Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:36:50.5200610Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-12-18T01:36:50.5201349Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:36:50.5202212Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-12-18T01:36:50.5202861Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-12-18T01:36:50.5203503Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-12-18T01:36:50.5204212Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-12-18T01:36:50.5204917Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-12-18T01:36:50.5205772Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-12-18T01:36:50.5206928Z :func:`torch.promote_types`) before being compared. 2024-12-18T01:36:50.5208071Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-12-18T01:36:50.5209480Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-12-18T01:36:50.5210437Z compared. 2024-12-18T01:36:50.5211367Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-12-18T01:36:50.5212872Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-12-18T01:36:50.5214441Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-12-18T01:36:50.5215536Z should return the new message. 2024-12-18T01:36:50.5215936Z 2024-12-18T01:36:50.5216108Z Raises: 2024-12-18T01:36:50.5216702Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-12-18T01:36:50.5217650Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-12-18T01:36:50.5218754Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-12-18T01:36:50.5220268Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-12-18T01:36:50.5221326Z different types. 2024-12-18T01:36:50.5222327Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-12-18T01:36:50.5223947Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-12-18T01:36:50.5225430Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-12-18T01:36:50.5226807Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-12-18T01:36:50.5227795Z :attr:`~torch.Tensor.layout`. 2024-12-18T01:36:50.5228614Z AssertionError: If only one of corresponding tensors is quantized. 2024-12-18T01:36:50.5229967Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-12-18T01:36:50.5231452Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-12-18T01:36:50.5232431Z :attr:`~torch.Tensor.device`. 2024-12-18T01:36:50.5233395Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-12-18T01:36:50.5234986Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-12-18T01:36:50.5236748Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-12-18T01:36:50.5237653Z 2024-12-18T01:36:50.5238337Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-12-18T01:36:50.5239489Z ``dtype``'s, the maximum of both tolerances is used. 2024-12-18T01:36:50.5239966Z 2024-12-18T01:36:50.5240188Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5240861Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-12-18T01:36:50.5241485Z +===========================+============+==========+ 2024-12-18T01:36:50.5242225Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:36:50.5242882Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5243539Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-12-18T01:36:50.5244217Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5244885Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:50.5245558Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5246298Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:36:50.5246951Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5247632Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:36:50.5248302Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5248987Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:50.5249615Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5250216Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:36:50.5250943Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5251622Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:50.5252292Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5252965Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:50.5253627Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5254314Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:50.5254986Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5255659Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:50.5256320Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5256981Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:50.5257642Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5258288Z | other | ``0.0`` | ``0.0`` | 2024-12-18T01:36:50.5258947Z +---------------------------+------------+----------+ 2024-12-18T01:36:50.5259388Z 2024-12-18T01:36:50.5259557Z .. note:: 2024-12-18T01:36:50.5259783Z 2024-12-18T01:36:50.5260494Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-12-18T01:36:50.5262066Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-12-18T01:36:50.5263412Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-12-18T01:36:50.5264105Z 2024-12-18T01:36:50.5264299Z >>> import functools 2024-12-18T01:36:50.5265107Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-12-18T01:36:50.5266022Z >>> assert_equal(1e-9, 1e-10) 2024-12-18T01:36:50.5266601Z Traceback (most recent call last): 2024-12-18T01:36:50.5267174Z ... 2024-12-18T01:36:50.5267602Z AssertionError: Scalars are not equal! 2024-12-18T01:36:50.5268202Z 2024-12-18T01:36:50.5268753Z Expected 1e-10 but got 1e-09. 2024-12-18T01:36:50.5269366Z Absolute difference: 9.000000000000001e-10 2024-12-18T01:36:50.5270002Z Relative difference: 9.0 2024-12-18T01:36:50.5270361Z 2024-12-18T01:36:50.5270518Z Examples: 2024-12-18T01:36:50.5270970Z >>> # tensor to tensor comparison 2024-12-18T01:36:50.5271621Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-12-18T01:36:50.5272309Z >>> actual = torch.acos(torch.cos(expected)) 2024-12-18T01:36:50.5273030Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:50.5273511Z 2024-12-18T01:36:50.5273724Z >>> # scalar to scalar comparison 2024-12-18T01:36:50.5274282Z >>> import math 2024-12-18T01:36:50.5274744Z >>> expected = math.sqrt(2.0) 2024-12-18T01:36:50.5275418Z >>> actual = 2.0 / math.sqrt(2.0) 2024-12-18T01:36:50.5276157Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:50.5276639Z 2024-12-18T01:36:50.5276874Z >>> # numpy array to numpy array comparison 2024-12-18T01:36:50.5277492Z >>> import numpy as np 2024-12-18T01:36:50.5278028Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-12-18T01:36:50.5278671Z >>> actual = np.arccos(np.cos(expected)) 2024-12-18T01:36:50.5279427Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:50.5279895Z 2024-12-18T01:36:50.5280113Z >>> # sequence to sequence comparison 2024-12-18T01:36:50.5280694Z >>> import numpy as np 2024-12-18T01:36:50.5281502Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-12-18T01:36:50.5282430Z >>> # length and their elements have to match. 2024-12-18T01:36:50.5283171Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-12-18T01:36:50.5283851Z >>> actual = tuple(expected) 2024-12-18T01:36:50.5284554Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:50.5285031Z 2024-12-18T01:36:50.5285243Z >>> # mapping to mapping comparison 2024-12-18T01:36:50.5285878Z >>> from collections import OrderedDict 2024-12-18T01:36:50.5286487Z >>> import numpy as np 2024-12-18T01:36:50.5287000Z >>> foo = torch.tensor(1.0) 2024-12-18T01:36:50.5287527Z >>> bar = 2.0 2024-12-18T01:36:50.5287970Z >>> baz = np.array(3.0) 2024-12-18T01:36:50.5288743Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-12-18T01:36:50.5289838Z >>> # have to have the same set of keys and their elements have to match. 2024-12-18T01:36:50.5290749Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-12-18T01:36:50.5291527Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-12-18T01:36:50.5292225Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:50.5292638Z 2024-12-18T01:36:50.5292839Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:36:50.5293405Z >>> actual = expected.clone() 2024-12-18T01:36:50.5294023Z >>> # By default, directly related instances can be compared 2024-12-18T01:36:50.5294892Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-12-18T01:36:50.5295802Z >>> # This check can be made more strict with allow_subclasses=False 2024-12-18T01:36:50.5296520Z >>> torch.testing.assert_close( 2024-12-18T01:36:50.5297367Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-12-18T01:36:50.5298279Z ... ) 2024-12-18T01:36:50.5298725Z Traceback (most recent call last): 2024-12-18T01:36:50.5299299Z ... 2024-12-18T01:36:50.5299890Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:36:50.5300924Z and . 2024-12-18T01:36:50.5302265Z >>> # If the inputs are not directly related, they are never considered close 2024-12-18T01:36:50.5303379Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-12-18T01:36:50.5304124Z Traceback (most recent call last): 2024-12-18T01:36:50.5304652Z ... 2024-12-18T01:36:50.5305352Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:36:50.5306183Z and . 2024-12-18T01:36:50.5306684Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-12-18T01:36:50.5307186Z >>> # their type if check_dtype=False. 2024-12-18T01:36:50.5307592Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-12-18T01:36:50.5307874Z 2024-12-18T01:36:50.5307978Z >>> # NaN != NaN by default. 2024-12-18T01:36:50.5308311Z >>> expected = torch.tensor(float("Nan")) 2024-12-18T01:36:50.5308772Z >>> actual = expected.clone() 2024-12-18T01:36:50.5309142Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:50.5309523Z Traceback (most recent call last): 2024-12-18T01:36:50.5309826Z ... 2024-12-18T01:36:50.5310085Z AssertionError: Scalars are not close! 2024-12-18T01:36:50.5310424Z 2024-12-18T01:36:50.5310683Z Expected nan but got nan. 2024-12-18T01:36:50.5311078Z Absolute difference: nan (up to 1e-05 allowed) 2024-12-18T01:36:50.5311487Z Relative difference: nan (up to 1.3e-06 allowed) 2024-12-18T01:36:50.5311946Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-12-18T01:36:50.5312262Z 2024-12-18T01:36:50.5312407Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:36:50.5312758Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-12-18T01:36:50.5313147Z >>> # The default error message can be overwritten. 2024-12-18T01:36:50.5313705Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-12-18T01:36:50.5314680Z Traceback (most recent call last): 2024-12-18T01:36:50.5315225Z ... 2024-12-18T01:36:50.5315763Z AssertionError: Argh, the tensors are not close! 2024-12-18T01:36:50.5316638Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-12-18T01:36:50.5317430Z >>> # extra information 2024-12-18T01:36:50.5317983Z >>> torch.testing.assert_close( 2024-12-18T01:36:50.5318770Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-12-18T01:36:50.5319471Z ... ) 2024-12-18T01:36:50.5319922Z Traceback (most recent call last): 2024-12-18T01:36:50.5320492Z ... 2024-12-18T01:36:50.5320909Z AssertionError: Header 2024-12-18T01:36:50.5321423Z 2024-12-18T01:36:50.5321849Z Tensor-likes are not close! 2024-12-18T01:36:50.5322397Z 2024-12-18T01:36:50.5322844Z Mismatched elements: 2 / 3 (66.7%) 2024-12-18T01:36:50.5323659Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-12-18T01:36:50.5324736Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-12-18T01:36:50.5325525Z 2024-12-18T01:36:50.5325940Z Footer 2024-12-18T01:36:50.5326331Z 2024-12-18T01:36:50.5326999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:50.5327685Z 2024-12-18T01:36:51.7080381Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=110. 2024-12-18T01:36:51.7081365Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.7081908Z Register a container-like type as pytree node. 2024-12-18T01:36:51.7082164Z 2024-12-18T01:36:51.7082294Z Args: 2024-12-18T01:36:51.7082609Z cls (type): A Python type to treat as an internal pytree node. 2024-12-18T01:36:51.7083196Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-12-18T01:36:51.7084055Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-12-18T01:36:51.7084718Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-12-18T01:36:51.7085260Z passed to the ``unflatten_fn``. 2024-12-18T01:36:51.7085774Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-12-18T01:36:51.7086424Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-12-18T01:36:51.7086965Z The function should return an instance of ``cls``. 2024-12-18T01:36:51.7087498Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-12-18T01:36:51.7088117Z qualified name used when serializing the tree spec. 2024-12-18T01:36:51.7088709Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-12-18T01:36:51.7089417Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-12-18T01:36:51.7090076Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-12-18T01:36:51.7090821Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-12-18T01:36:51.7091502Z how to convert the custom json dumpable representation of the context back to the 2024-12-18T01:36:51.7092149Z original context. This is used for json deserialization, which is being used in 2024-12-18T01:36:51.7092649Z :mod:`torch.export` right now. 2024-12-18T01:36:51.7092873Z 2024-12-18T01:36:51.7093024Z Example:: 2024-12-18T01:36:51.7093161Z 2024-12-18T01:36:51.7093284Z >>> # xdoctest: +SKIP 2024-12-18T01:36:51.7093605Z >>> # Registry a Python type with lambda functions 2024-12-18T01:36:51.7094026Z >>> register_pytree_node( 2024-12-18T01:36:51.7094329Z ... set, 2024-12-18T01:36:51.7094613Z ... lambda s: (sorted(s), None, None), 2024-12-18T01:36:51.7094987Z ... lambda children, _: set(children), 2024-12-18T01:36:51.7095310Z ... ) 2024-12-18T01:36:51.7095538Z 2024-12-18T01:36:51.7095918Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.7096281Z 2024-12-18T01:36:51.9097337Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1201. 2024-12-18T01:36:51.9098533Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.9098913Z 2024-12-18T01:36:51.9099144Z Context passed to policy function during selective checkpointing. 2024-12-18T01:36:51.9099489Z 2024-12-18T01:36:51.9099746Z This class is used to pass relevant metadata to the policy function during 2024-12-18T01:36:51.9100349Z selective checkpointing. The metadata includes whether the current invocation 2024-12-18T01:36:51.9100907Z of the policy function is during recomputation or not. 2024-12-18T01:36:51.9101194Z 2024-12-18T01:36:51.9101289Z Example: 2024-12-18T01:36:51.9101529Z >>> # xdoctest: +SKIP(stub) 2024-12-18T01:36:51.9101823Z >>> 2024-12-18T01:36:51.9102188Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:36:51.9102608Z >>> print(ctx.is_recompute) 2024-12-18T01:36:51.9102907Z >>> 2024-12-18T01:36:51.9103385Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:36:51.9104257Z >>> 2024-12-18T01:36:51.9104613Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:36:51.9104971Z >>> fn, x, y, 2024-12-18T01:36:51.9105239Z >>> use_reentrant=False, 2024-12-18T01:36:51.9114696Z >>> context_fn=context_fn, 2024-12-18T01:36:51.9115127Z >>> ) 2024-12-18T01:36:51.9115265Z 2024-12-18T01:36:51.9115556Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.9116211Z 2024-12-18T01:36:51.9116879Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1335. 2024-12-18T01:36:51.9117882Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.9118255Z 2024-12-18T01:36:51.9118511Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-12-18T01:36:51.9118860Z 2024-12-18T01:36:51.9119081Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-12-18T01:36:51.9119586Z operations are recomputed during the backward pass. 2024-12-18T01:36:51.9119870Z 2024-12-18T01:36:51.9119965Z Args: 2024-12-18T01:36:51.9120221Z policy_fn_or_list (Callable or List): 2024-12-18T01:36:51.9120701Z - If a policy function is provided, it should accept a 2024-12-18T01:36:51.9121236Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-12-18T01:36:51.9121798Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-12-18T01:36:51.9122372Z indicating whether the execution of the op should be recomputed or not. 2024-12-18T01:36:51.9122985Z - If a list of operations is provided, it is equivalent to a policy 2024-12-18T01:36:51.9123507Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-12-18T01:36:51.9124038Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-12-18T01:36:51.9124464Z operations. 2024-12-18T01:36:51.9124842Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-12-18T01:36:51.9125394Z raised if any tensors cached by selective activation checkpoint are 2024-12-18T01:36:51.9125946Z mutated in order to ensure correctness. If set to `True`, this check 2024-12-18T01:36:51.9126380Z is disabled. 2024-12-18T01:36:51.9126693Z Returns: 2024-12-18T01:36:51.9126927Z A tuple of two context managers. 2024-12-18T01:36:51.9127148Z 2024-12-18T01:36:51.9127246Z Example: 2024-12-18T01:36:51.9127489Z >>> # xdoctest: +REQUIRES(LINUX) 2024-12-18T01:36:51.9127808Z >>> import functools 2024-12-18T01:36:51.9128066Z >>> 2024-12-18T01:36:51.9128330Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:36:51.9128702Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:36:51.9129035Z >>> 2024-12-18T01:36:51.9129264Z >>> ops_to_save = [ 2024-12-18T01:36:51.9129542Z >>> torch.ops.aten.mm.default, 2024-12-18T01:36:51.9129860Z >>> ] 2024-12-18T01:36:51.9130081Z >>> 2024-12-18T01:36:51.9130338Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:36:51.9130697Z >>> if op in ops_to_save: 2024-12-18T01:36:51.9131023Z >>> return CheckpointPolicy.MUST_SAVE 2024-12-18T01:36:51.9131365Z >>> else: 2024-12-18T01:36:51.9131666Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-12-18T01:36:51.9132025Z >>> 2024-12-18T01:36:51.9132424Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:36:51.9132891Z >>> 2024-12-18T01:36:51.9133121Z >>> # or equivalently 2024-12-18T01:36:51.9133578Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-12-18T01:36:51.9134064Z >>> 2024-12-18T01:36:51.9134286Z >>> def fn(x, y): 2024-12-18T01:36:51.9134645Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-12-18T01:36:51.9135059Z >>> 2024-12-18T01:36:51.9135332Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:36:51.9135693Z >>> fn, x, y, 2024-12-18T01:36:51.9135961Z >>> use_reentrant=False, 2024-12-18T01:36:51.9136252Z >>> context_fn=context_fn, 2024-12-18T01:36:51.9136561Z >>> ) 2024-12-18T01:36:51.9136698Z 2024-12-18T01:36:51.9136953Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.9137359Z 2024-12-18T01:36:51.9333397Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=957. 2024-12-18T01:36:51.9334311Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.9334686Z 2024-12-18T01:36:51.9334860Z Create a :class:`setuptools.Extension` for C++. 2024-12-18T01:36:51.9335119Z 2024-12-18T01:36:51.9335363Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:36:51.9335948Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-12-18T01:36:51.9336300Z 2024-12-18T01:36:51.9336509Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:36:51.9336994Z constructor. Full list arguments can be found at 2024-12-18T01:36:51.9337723Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:36:51.9338160Z 2024-12-18T01:36:51.9338306Z .. note:: 2024-12-18T01:36:51.9338661Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:36:51.9339393Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:36:51.9340224Z the user's responsibility in their library to not use APIs from 2024-12-18T01:36:51.9340944Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:36:51.9341564Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:36:51.9342145Z example, to give access to custom ops from python, the library should 2024-12-18T01:36:51.9342666Z register the ops through the dispatcher. 2024-12-18T01:36:51.9342916Z 2024-12-18T01:36:51.9343020Z Example: 2024-12-18T01:36:51.9343305Z >>> # xdoctest: +SKIP 2024-12-18T01:36:51.9343644Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:51.9344090Z >>> from setuptools import setup 2024-12-18T01:36:51.9344627Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-12-18T01:36:51.9345087Z >>> setup( 2024-12-18T01:36:51.9345369Z ... name='extension', 2024-12-18T01:36:51.9345704Z ... ext_modules=[ 2024-12-18T01:36:51.9345993Z ... CppExtension( 2024-12-18T01:36:51.9346336Z ... name='extension', 2024-12-18T01:36:51.9346683Z ... sources=['extension.cpp'], 2024-12-18T01:36:51.9347104Z ... extra_compile_args=['-g'], 2024-12-18T01:36:51.9347507Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2024-12-18T01:36:51.9347941Z ... ], 2024-12-18T01:36:51.9348171Z ... cmdclass={ 2024-12-18T01:36:51.9348506Z ... 'build_ext': BuildExtension 2024-12-18T01:36:51.9348973Z ... }) 2024-12-18T01:36:51.9349195Z 2024-12-18T01:36:51.9349491Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.9349871Z 2024-12-18T01:36:51.9350525Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1019. 2024-12-18T01:36:51.9351504Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.9351935Z 2024-12-18T01:36:51.9352120Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-12-18T01:36:51.9352420Z 2024-12-18T01:36:51.9352694Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:36:51.9353311Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-12-18T01:36:51.9353871Z extension. This includes the CUDA include path, library path and runtime 2024-12-18T01:36:51.9354366Z library. 2024-12-18T01:36:51.9354511Z 2024-12-18T01:36:51.9354770Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:36:51.9355250Z constructor. Full list arguments can be found at 2024-12-18T01:36:51.9356027Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:36:51.9356620Z 2024-12-18T01:36:51.9356726Z .. note:: 2024-12-18T01:36:51.9357158Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:36:51.9357784Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:36:51.9358382Z the user's responsibility in their library to not use APIs from 2024-12-18T01:36:51.9359107Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:36:51.9359797Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:36:51.9360339Z example, to give access to custom ops from python, the library should 2024-12-18T01:36:51.9360815Z register the ops through the dispatcher. 2024-12-18T01:36:51.9361061Z 2024-12-18T01:36:51.9361155Z Example: 2024-12-18T01:36:51.9361455Z >>> # xdoctest: +SKIP 2024-12-18T01:36:51.9361798Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:51.9362173Z >>> from setuptools import setup 2024-12-18T01:36:51.9362625Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-12-18T01:36:51.9363068Z >>> setup( 2024-12-18T01:36:51.9363330Z ... name='cuda_extension', 2024-12-18T01:36:51.9363639Z ... ext_modules=[ 2024-12-18T01:36:51.9363914Z ... CUDAExtension( 2024-12-18T01:36:51.9364263Z ... name='cuda_extension', 2024-12-18T01:36:51.9364670Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:36:51.9365099Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:36:51.9365472Z ... 'nvcc': ['-O2']}, 2024-12-18T01:36:51.9365859Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2024-12-18T01:36:51.9366236Z ... ], 2024-12-18T01:36:51.9366478Z ... cmdclass={ 2024-12-18T01:36:51.9366773Z ... 'build_ext': BuildExtension 2024-12-18T01:36:51.9367126Z ... }) 2024-12-18T01:36:51.9367260Z 2024-12-18T01:36:51.9367377Z Compute capabilities: 2024-12-18T01:36:51.9367547Z 2024-12-18T01:36:51.9367842Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-12-18T01:36:51.9368544Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-12-18T01:36:51.9369244Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-12-18T01:36:51.9369955Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2024-12-18T01:36:51.9370663Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-12-18T01:36:51.9371185Z support (see below for details on PTX). 2024-12-18T01:36:51.9371406Z 2024-12-18T01:36:51.9371714Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-12-18T01:36:51.9372257Z CCs you want the extension to support: 2024-12-18T01:36:51.9372495Z 2024-12-18T01:36:51.9372691Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-12-18T01:36:51.9373229Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-12-18T01:36:51.9373579Z 2024-12-18T01:36:51.9373906Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-12-18T01:36:51.9374645Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-12-18T01:36:51.9375375Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-12-18T01:36:51.9376067Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-12-18T01:36:51.9376794Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-12-18T01:36:51.9377503Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-12-18T01:36:51.9378214Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-12-18T01:36:51.9379019Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-12-18T01:36:51.9379548Z "8.0 8.6" would be better. 2024-12-18T01:36:51.9379723Z 2024-12-18T01:36:51.9380019Z Note that while it's possible to include all supported archs, the more archs get included the 2024-12-18T01:36:51.9380718Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-12-18T01:36:51.9381129Z 2024-12-18T01:36:51.9381452Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-12-18T01:36:51.9382114Z To workaround the issue, move python binding logic to pure C++ file. 2024-12-18T01:36:51.9382438Z 2024-12-18T01:36:51.9382588Z Example use: 2024-12-18T01:36:51.9382833Z #include 2024-12-18T01:36:51.9383169Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-12-18T01:36:51.9383441Z 2024-12-18T01:36:51.9383539Z Instead of: 2024-12-18T01:36:51.9383790Z #include 2024-12-18T01:36:51.9384163Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-12-18T01:36:51.9384430Z 2024-12-18T01:36:51.9384715Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-12-18T01:36:51.9385634Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-12-18T01:36:51.9386237Z 2024-12-18T01:36:51.9386352Z Relocatable device code linking: 2024-12-18T01:36:51.9386566Z 2024-12-18T01:36:51.9386838Z If you want to reference device symbols across compilation units (across object files), 2024-12-18T01:36:51.9387499Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-12-18T01:36:51.9388235Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-12-18T01:36:51.9389057Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-12-18T01:36:51.9389827Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-12-18T01:36:51.9390450Z helps reduce the protentional perf degradation of `-rdc`. 2024-12-18T01:36:51.9390903Z Note that it needs to be used at both steps to be useful. 2024-12-18T01:36:51.9391186Z 2024-12-18T01:36:51.9391545Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-12-18T01:36:51.9392207Z There is also a case where `-dlink` is used without `-rdc`: 2024-12-18T01:36:51.9392752Z when an extension is linked against a static lib containing rdc-compiled objects 2024-12-18T01:36:51.9393337Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-12-18T01:36:51.9393662Z 2024-12-18T01:36:51.9393877Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-12-18T01:36:51.9394194Z 2024-12-18T01:36:51.9394288Z Example: 2024-12-18T01:36:51.9394522Z >>> # xdoctest: +SKIP 2024-12-18T01:36:51.9394854Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:51.9395221Z >>> CUDAExtension( 2024-12-18T01:36:51.9395495Z ... name='cuda_extension', 2024-12-18T01:36:51.9395984Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:36:51.9396353Z ... dlink=True, 2024-12-18T01:36:51.9396658Z ... dlink_libraries=["dlink_lib"], 2024-12-18T01:36:51.9397023Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:36:51.9397405Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-12-18T01:36:51.9397646Z 2024-12-18T01:36:51.9398114Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.9398503Z 2024-12-18T01:36:51.9399057Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1300. 2024-12-18T01:36:51.9400009Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.9400382Z 2024-12-18T01:36:51.9400548Z Load a PyTorch C++ extension just-in-time (JIT). 2024-12-18T01:36:51.9400805Z 2024-12-18T01:36:51.9401036Z To load an extension, a Ninja build file is emitted, which is used to 2024-12-18T01:36:51.9401558Z compile the given sources into a dynamic library. This library is 2024-12-18T01:36:51.9402105Z subsequently loaded into the current Python process as a module and 2024-12-18T01:36:51.9402579Z returned from this function, ready for use. 2024-12-18T01:36:51.9402827Z 2024-12-18T01:36:51.9403037Z By default, the directory to which the build file is emitted and the 2024-12-18T01:36:51.9403594Z resulting library compiled to is ``/torch_extensions/``, where 2024-12-18T01:36:51.9404214Z ```` is the temporary folder on the current platform and ```` 2024-12-18T01:36:51.9404743Z the name of the extension. This location can be overridden in two ways. 2024-12-18T01:36:51.9405290Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-12-18T01:36:51.9405839Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-12-18T01:36:51.9406398Z into subfolders of this directory. Second, if the ``build_directory`` 2024-12-18T01:36:51.9407036Z argument to this function is supplied, it overrides the entire path, i.e. 2024-12-18T01:36:51.9407557Z the library will be compiled into that folder directly. 2024-12-18T01:36:51.9407830Z 2024-12-18T01:36:51.9408039Z To compile the sources, the default system compiler (``c++``) is used, 2024-12-18T01:36:51.9408602Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-12-18T01:36:51.9409182Z additional arguments to the compilation process, ``extra_cflags`` or 2024-12-18T01:36:51.9409748Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-12-18T01:36:51.9410339Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-12-18T01:36:51.9410829Z ``extra_cflags`` to pass further include directories. 2024-12-18T01:36:51.9411088Z 2024-12-18T01:36:51.9411322Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-12-18T01:36:51.9411868Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-12-18T01:36:51.9412415Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-12-18T01:36:51.9412985Z passing the CUDA lib64 directory as a library directory, and linking 2024-12-18T01:36:51.9413472Z ``cudart``. You can pass additional flags to nvcc via 2024-12-18T01:36:51.9413936Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-12-18T01:36:51.9414495Z heuristics for finding the CUDA install directory are used, which usually 2024-12-18T01:36:51.9415064Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-12-18T01:36:51.9415494Z safest option. 2024-12-18T01:36:51.9415635Z 2024-12-18T01:36:51.9415740Z Args: 2024-12-18T01:36:51.9416085Z name: The name of the extension to build. This MUST be the same as the 2024-12-18T01:36:51.9416514Z name of the pybind11 module! 2024-12-18T01:36:51.9416934Z sources: A list of relative or absolute paths to C++ source files. 2024-12-18T01:36:51.9417483Z extra_cflags: optional list of compiler flags to forward to the build. 2024-12-18T01:36:51.9418045Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-12-18T01:36:51.9418492Z when building CUDA sources. 2024-12-18T01:36:51.9418913Z extra_ldflags: optional list of linker flags to forward to the build. 2024-12-18T01:36:51.9419471Z extra_include_paths: optional list of include directories to forward 2024-12-18T01:36:51.9419911Z to the build. 2024-12-18T01:36:51.9420259Z build_directory: optional path to use as build workspace. 2024-12-18T01:36:51.9420742Z verbose: If ``True``, turns on verbose logging of load steps. 2024-12-18T01:36:51.9421297Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:36:51.9421787Z the build. If set to ``None`` (default), this value is 2024-12-18T01:36:51.9422266Z automatically determined based on the existence of ``.cu`` or 2024-12-18T01:36:51.9422763Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-12-18T01:36:51.9423169Z and libraries to be included. 2024-12-18T01:36:51.9423593Z is_python_module: If ``True`` (default), imports the produced shared 2024-12-18T01:36:51.9424097Z library as a Python module. If ``False``, behavior depends on 2024-12-18T01:36:51.9424511Z ``is_standalone``. 2024-12-18T01:36:51.9424900Z is_standalone: If ``False`` (default) loads the constructed extension 2024-12-18T01:36:51.9425454Z into the process as a plain dynamic library. If ``True``, build a 2024-12-18T01:36:51.9425880Z standalone executable. 2024-12-18T01:36:51.9426069Z 2024-12-18T01:36:51.9426181Z Returns: 2024-12-18T01:36:51.9426413Z If ``is_python_module`` is ``True``: 2024-12-18T01:36:51.9426814Z Returns the loaded PyTorch extension as a Python module. 2024-12-18T01:36:51.9427116Z 2024-12-18T01:36:51.9427328Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-12-18T01:36:51.9427907Z Returns nothing. (The shared library is loaded into the process as 2024-12-18T01:36:51.9428334Z a side effect.) 2024-12-18T01:36:51.9428495Z 2024-12-18T01:36:51.9428609Z If ``is_standalone`` is ``True``. 2024-12-18T01:36:51.9429024Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-12-18T01:36:51.9429521Z added to the PATH environment variable as a side effect.) 2024-12-18T01:36:51.9429818Z 2024-12-18T01:36:51.9429908Z Example: 2024-12-18T01:36:51.9430141Z >>> # xdoctest: +SKIP 2024-12-18T01:36:51.9430458Z >>> from torch.utils.cpp_extension import load 2024-12-18T01:36:51.9430832Z >>> module = load( 2024-12-18T01:36:51.9431103Z ... name='extension', 2024-12-18T01:36:51.9431457Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:36:51.9431841Z ... extra_cflags=['-O2'], 2024-12-18T01:36:51.9432141Z ... verbose=True) 2024-12-18T01:36:51.9432306Z 2024-12-18T01:36:51.9432559Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.9432935Z 2024-12-18T01:36:51.9433449Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1593. 2024-12-18T01:36:51.9434337Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.9434723Z 2024-12-18T01:36:51.9434939Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-12-18T01:36:51.9435260Z 2024-12-18T01:36:51.9435503Z This function behaves exactly like :func:`load`, but takes its sources as 2024-12-18T01:36:51.9436173Z strings rather than filenames. These strings are stored to files in the 2024-12-18T01:36:51.9436736Z build directory, after which the behavior of :func:`load_inline` is 2024-12-18T01:36:51.9437159Z identical to :func:`load`. 2024-12-18T01:36:51.9437350Z 2024-12-18T01:36:51.9437443Z See `the 2024-12-18T01:36:51.9437907Z tests `_ 2024-12-18T01:36:51.9438471Z for good examples of using this function. 2024-12-18T01:36:51.9438701Z 2024-12-18T01:36:51.9438946Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-12-18T01:36:51.9439524Z the necessary header includes, as well as the (pybind11) binding code. More 2024-12-18T01:36:51.9440125Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-12-18T01:36:51.9440676Z single ``.cpp`` file. This file is then prepended with ``#include 2024-12-18T01:36:51.9441095Z ``. 2024-12-18T01:36:51.9441262Z 2024-12-18T01:36:51.9441503Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-12-18T01:36:51.9442128Z automatically generated for each function specified. ``functions`` can 2024-12-18T01:36:51.9442700Z either be a list of function names, or a dictionary mapping from function 2024-12-18T01:36:51.9443280Z names to docstrings. If a list is given, the name of each function is used 2024-12-18T01:36:51.9443738Z as its docstring. 2024-12-18T01:36:51.9443889Z 2024-12-18T01:36:51.9444125Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-12-18T01:36:51.9444646Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-12-18T01:36:51.9445158Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-12-18T01:36:51.9445706Z separately, but ultimately linked into a single library. Note that no 2024-12-18T01:36:51.9446323Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-12-18T01:36:51.9446912Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-12-18T01:36:51.9447473Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-12-18T01:36:51.9447926Z include its name in ``functions``). 2024-12-18T01:36:51.9448135Z 2024-12-18T01:36:51.9448327Z See :func:`load` for a description of arguments omitted below. 2024-12-18T01:36:51.9448634Z 2024-12-18T01:36:51.9448756Z Args: 2024-12-18T01:36:51.9449108Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-12-18T01:36:51.9449676Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-12-18T01:36:51.9450233Z functions: A list of function names for which to generate function 2024-12-18T01:36:51.9450774Z bindings. If a dictionary is given, it should map function names to 2024-12-18T01:36:51.9451283Z docstrings (which are otherwise just the function names). 2024-12-18T01:36:51.9451805Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:36:51.9452342Z the build. If set to ``None`` (default), this value is 2024-12-18T01:36:51.9452824Z automatically determined based on whether ``cuda_sources`` is 2024-12-18T01:36:51.9453298Z provided. Set it to ``True`` to force CUDA headers 2024-12-18T01:36:51.9453688Z and libraries to be included. 2024-12-18T01:36:51.9454105Z with_pytorch_error_handling: Determines whether pytorch error and 2024-12-18T01:36:51.9454637Z warning macros are handled by pytorch instead of pybind. To do 2024-12-18T01:36:51.9455173Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-12-18T01:36:51.9455718Z function. This redirection might cause issues in obscure cases 2024-12-18T01:36:51.9456233Z of cpp. This flag should be set to ``False`` when this redirect 2024-12-18T01:36:51.9456625Z causes issues. 2024-12-18T01:36:51.9456795Z 2024-12-18T01:36:51.9456889Z Example: 2024-12-18T01:36:51.9457183Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:51.9457610Z >>> from torch.utils.cpp_extension import load_inline 2024-12-18T01:36:51.9457985Z >>> source = """ 2024-12-18T01:36:51.9458289Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-12-18T01:36:51.9458651Z return x.sin() + y.sin(); 2024-12-18T01:36:51.9458930Z } 2024-12-18T01:36:51.9459145Z """ 2024-12-18T01:36:51.9459417Z >>> module = load_inline(name='inline_extension', 2024-12-18T01:36:51.9459784Z ... cpp_sources=[source], 2024-12-18T01:36:51.9460156Z ... functions=['sin_add']) 2024-12-18T01:36:51.9460399Z 2024-12-18T01:36:51.9460494Z .. note:: 2024-12-18T01:36:51.9460870Z Since load_inline will just-in-time compile the source code, please ensure 2024-12-18T01:36:51.9461462Z that you have the right toolchains installed in the runtime. For example, 2024-12-18T01:36:51.9462035Z when loading C++, make sure a C++ compiler is available. If you're loading 2024-12-18T01:36:51.9462610Z a CUDA extension, you will need to additionally install the corresponding CUDA 2024-12-18T01:36:51.9463610Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2024-12-18T01:36:51.9464212Z are not included when you install torch and must be additionally installed. 2024-12-18T01:36:51.9464575Z 2024-12-18T01:36:51.9464840Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2024-12-18T01:36:51.9465437Z the extension. This may use up too many resources on some systems. One 2024-12-18T01:36:51.9465997Z can control the number of workers by setting the `MAX_JOBS` environment 2024-12-18T01:36:51.9466444Z variable to a non-negative number. 2024-12-18T01:36:51.9466671Z 2024-12-18T01:36:51.9466920Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.9467323Z 2024-12-18T01:36:51.9584090Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-12-18T01:36:51.9585140Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:51.9585587Z 2024-12-18T01:36:51.9585889Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-12-18T01:36:51.9586359Z 2024-12-18T01:36:51.9586767Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-12-18T01:36:51.9587469Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-12-18T01:36:51.9588130Z server like load. It can emulate multiple calling threads to a single module 2024-12-18T01:36:51.9588768Z provided. In the future we plan to enhance this component to support inter and 2024-12-18T01:36:51.9589433Z intra-op parallelism as well as multiple models running in a single process. 2024-12-18T01:36:51.9589824Z 2024-12-18T01:36:51.9590166Z Please note that even though nn.Module is supported, it might incur an overhead 2024-12-18T01:36:51.9590826Z from the need to hold GIL every time we execute Python code or pass around 2024-12-18T01:36:51.9591461Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-12-18T01:36:51.9592055Z model for inference deployment it is better to switch to using it in this 2024-12-18T01:36:51.9592549Z benchmark. 2024-12-18T01:36:51.9592691Z 2024-12-18T01:36:51.9592789Z Example:: 2024-12-18T01:36:51.9592974Z 2024-12-18T01:36:51.9593097Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:51.9593473Z >>> from torch.utils import ThroughputBenchmark 2024-12-18T01:36:51.9593923Z >>> bench = ThroughputBenchmark(my_module) 2024-12-18T01:36:51.9594334Z >>> # Pre-populate benchmark's data set with the inputs 2024-12-18T01:36:51.9594764Z >>> for input in inputs: 2024-12-18T01:36:51.9595211Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-12-18T01:36:51.9595773Z ... bench.add_input(input[0], x2=input[1]) 2024-12-18T01:36:51.9596284Z >>> # Inputs supplied above are randomly used during the execution 2024-12-18T01:36:51.9596749Z >>> stats = bench.benchmark( 2024-12-18T01:36:51.9597063Z ... num_calling_threads=4, 2024-12-18T01:36:51.9597384Z ... num_warmup_iters = 100, 2024-12-18T01:36:51.9597754Z ... num_iters = 1000, 2024-12-18T01:36:51.9598234Z ... ) 2024-12-18T01:36:51.9598553Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-12-18T01:36:51.9599082Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-12-18T01:36:51.9599390Z 2024-12-18T01:36:51.9599700Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:51.9600078Z 2024-12-18T01:36:52.0752922Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=17. 2024-12-18T01:36:52.0753908Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:52.0754677Z Sampler that restricts data loading to a subset of the dataset. 2024-12-18T01:36:52.0754988Z 2024-12-18T01:36:52.0755166Z It is especially useful in conjunction with 2024-12-18T01:36:52.0755724Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-12-18T01:36:52.0756375Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-12-18T01:36:52.0756983Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-12-18T01:36:52.0757479Z original dataset that is exclusive to it. 2024-12-18T01:36:52.0757734Z 2024-12-18T01:36:52.0757840Z .. note:: 2024-12-18T01:36:52.0758224Z Dataset is assumed to be of constant size and that any instance of it always 2024-12-18T01:36:52.0758729Z returns the same elements in the same order. 2024-12-18T01:36:52.0759041Z 2024-12-18T01:36:52.0759134Z Args: 2024-12-18T01:36:52.0759402Z dataset: Dataset used for sampling. 2024-12-18T01:36:52.0759862Z num_replicas (int, optional): Number of processes participating in 2024-12-18T01:36:52.0760454Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-12-18T01:36:52.0760948Z current distributed group. 2024-12-18T01:36:52.0761462Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-12-18T01:36:52.0762026Z By default, :attr:`rank` is retrieved from the current distributed 2024-12-18T01:36:52.0762451Z group. 2024-12-18T01:36:52.0762836Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-12-18T01:36:52.0763291Z indices. 2024-12-18T01:36:52.0763660Z seed (int, optional): random seed used to shuffle the sampler if 2024-12-18T01:36:52.0764164Z :attr:`shuffle=True`. This number should be identical across all 2024-12-18T01:36:52.0764654Z processes in the distributed group. Default: ``0``. 2024-12-18T01:36:52.0765196Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-12-18T01:36:52.0765729Z tail of the data to make it evenly divisible across the number of 2024-12-18T01:36:52.0766244Z replicas. If ``False``, the sampler will add extra indices to make 2024-12-18T01:36:52.0766774Z the data evenly divisible across the replicas. Default: ``False``. 2024-12-18T01:36:52.0767095Z 2024-12-18T01:36:52.0767192Z .. warning:: 2024-12-18T01:36:52.0767537Z In distributed mode, calling the :meth:`set_epoch` method at 2024-12-18T01:36:52.0768090Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-12-18T01:36:52.0768714Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-12-18T01:36:52.0769228Z the same ordering will be always used. 2024-12-18T01:36:52.0769459Z 2024-12-18T01:36:52.0769566Z Example:: 2024-12-18T01:36:52.0769704Z 2024-12-18T01:36:52.0769811Z >>> # xdoctest: +SKIP 2024-12-18T01:36:52.0770228Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-12-18T01:36:52.0770746Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-12-18T01:36:52.0771157Z ... sampler=sampler) 2024-12-18T01:36:52.0771540Z >>> for epoch in range(start_epoch, n_epochs): 2024-12-18T01:36:52.0771888Z ... if is_distributed: 2024-12-18T01:36:52.0772212Z ... sampler.set_epoch(epoch) 2024-12-18T01:36:52.0772539Z ... train(loader) 2024-12-18T01:36:52.0772819Z 2024-12-18T01:36:52.0773189Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:52.0773552Z 2024-12-18T01:36:52.2963531Z gathering tests 2024-12-18T01:36:52.2974663Z running 705 test(s) 2024-12-18T01:36:52.3018094Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1045 <- wrt source file 2024-12-18T01:36:52.3025586Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2024-12-18T01:36:52.3026685Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1081 <- wrt source file 2024-12-18T01:36:52.3030879Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2024-12-18T01:36:52.3032133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1150 <- wrt source file 2024-12-18T01:36:52.3033589Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2024-12-18T01:36:52.3034904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1199 <- wrt source file 2024-12-18T01:36:52.3036892Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2024-12-18T01:36:52.3038336Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1236 <- wrt source file 2024-12-18T01:36:52.3039601Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2024-12-18T01:36:52.3041071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1391 <- wrt source file 2024-12-18T01:36:52.3042571Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2024-12-18T01:36:52.3044146Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2488 <- wrt source file 2024-12-18T01:36:52.3045729Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2024-12-18T01:36:52.3046935Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2744 <- wrt source file 2024-12-18T01:36:52.3048268Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2024-12-18T01:36:52.3049554Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2024-12-18T01:36:52.3050888Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2024-12-18T01:36:52.3052255Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2024-12-18T01:36:52.3053615Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2024-12-18T01:36:52.3054807Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2024-12-18T01:36:52.3055920Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2024-12-18T01:36:52.3056992Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2024-12-18T01:36:52.3058076Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2024-12-18T01:36:52.3059163Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2024-12-18T01:36:52.3587150Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2024-12-18T01:36:52.3588628Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2024-12-18T01:36:52.3590029Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2024-12-18T01:36:52.3591367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 650 <- wrt source file 2024-12-18T01:36:52.3595329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2024-12-18T01:36:52.3596794Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 707 <- wrt source file 2024-12-18T01:36:52.3614004Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2024-12-18T01:36:52.3615288Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1336 <- wrt source file 2024-12-18T01:36:52.3730138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2024-12-18T01:36:52.3733430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1381 <- wrt source file 2024-12-18T01:36:52.3738321Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2024-12-18T01:36:52.3739572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1454 <- wrt source file 2024-12-18T01:36:52.3745735Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2024-12-18T01:36:52.3747033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1484 <- wrt source file 2024-12-18T01:36:52.3751549Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2024-12-18T01:36:52.3752791Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2024-12-18T01:36:52.3771391Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2024-12-18T01:36:52.3772660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file 2024-12-18T01:36:52.3777526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2024-12-18T01:36:52.3778762Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file 2024-12-18T01:36:52.3780506Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2024-12-18T01:36:52.3781638Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 178 <- wrt source file 2024-12-18T01:36:52.3792244Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2024-12-18T01:36:52.3793332Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 292 <- wrt source file 2024-12-18T01:36:52.3842128Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2024-12-18T01:36:52.3843325Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1019 <- wrt source file 2024-12-18T01:36:52.3853619Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2024-12-18T01:36:52.3854818Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1294 <- wrt source file 2024-12-18T01:36:52.3864221Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2024-12-18T01:36:52.3865375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1378 <- wrt source file 2024-12-18T01:36:52.3871625Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2024-12-18T01:36:52.3872858Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1412 <- wrt source file 2024-12-18T01:36:52.3880710Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2024-12-18T01:36:52.3881825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1463 <- wrt source file 2024-12-18T01:36:52.3893865Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2024-12-18T01:36:52.3894981Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1504 <- wrt source file 2024-12-18T01:36:52.3910023Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2024-12-18T01:36:52.3911136Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1540 <- wrt source file 2024-12-18T01:36:52.3926762Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2024-12-18T01:36:52.3927885Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1578 <- wrt source file 2024-12-18T01:36:52.3947307Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2024-12-18T01:36:52.3948400Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1751 <- wrt source file 2024-12-18T01:36:52.3979232Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2024-12-18T01:36:52.3980433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1918 <- wrt source file 2024-12-18T01:36:52.4005140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2024-12-18T01:36:52.4006529Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2018 <- wrt source file 2024-12-18T01:36:52.4007864Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2024-12-18T01:36:52.4009884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2118 <- wrt source file 2024-12-18T01:36:52.4011029Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2024-12-18T01:36:52.4012064Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2024-12-18T01:36:52.4013085Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2024-12-18T01:36:52.4014367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2024-12-18T01:36:52.4015572Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2024-12-18T01:36:52.4016649Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2024-12-18T01:36:52.4017771Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::_load_local:0 2024-12-18T01:36:52.4018928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 135 <- wrt source file 2024-12-18T01:36:52.4020079Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2024-12-18T01:36:52.4021414Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 235 <- wrt source file 2024-12-18T01:36:52.4032699Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2024-12-18T01:36:52.4033903Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 290 <- wrt source file 2024-12-18T01:36:52.4037239Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2024-12-18T01:36:52.4038323Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 483 <- wrt source file 2024-12-18T01:36:52.4048979Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2024-12-18T01:36:52.4050037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 550 <- wrt source file 2024-12-18T01:36:52.4058688Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2024-12-18T01:36:52.4059932Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 674 <- wrt source file 2024-12-18T01:36:52.4061118Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2024-12-18T01:36:52.4062300Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 995 <- wrt source file 2024-12-18T01:36:52.4161470Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2024-12-18T01:36:52.4162662Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1084 <- wrt source file 2024-12-18T01:36:52.4312190Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2024-12-18T01:36:52.4313398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file 2024-12-18T01:36:52.4318074Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2024-12-18T01:36:52.4319294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 417 <- wrt source file 2024-12-18T01:36:52.4351158Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2024-12-18T01:36:52.4352376Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1570 <- wrt source file 2024-12-18T01:36:52.4354283Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2024-12-18T01:36:52.4355501Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1705 <- wrt source file 2024-12-18T01:36:52.4357766Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2024-12-18T01:36:52.4359070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1953 <- wrt source file 2024-12-18T01:36:52.4383441Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2024-12-18T01:36:52.4384674Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1972 <- wrt source file 2024-12-18T01:36:52.4390334Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2024-12-18T01:36:52.4392057Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2024-12-18T01:36:52.4393699Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2024-12-18T01:36:52.4395415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 289 <- wrt source file 2024-12-18T01:36:52.4396728Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2024-12-18T01:36:52.4398201Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 314 <- wrt source file 2024-12-18T01:36:52.4400450Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2024-12-18T01:36:52.4402016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 388 <- wrt source file 2024-12-18T01:36:52.4403399Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2024-12-18T01:36:52.4404790Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 460 <- wrt source file 2024-12-18T01:36:52.4406013Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2024-12-18T01:36:52.4407157Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 923 <- wrt source file 2024-12-18T01:36:52.4408274Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2024-12-18T01:36:52.4409418Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file 2024-12-18T01:36:52.4410597Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2024-12-18T01:36:52.4411782Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 245 <- wrt source file 2024-12-18T01:36:52.4413464Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2024-12-18T01:36:52.4414650Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 277 <- wrt source file 2024-12-18T01:36:52.4415857Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2024-12-18T01:36:52.4417598Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1778 <- wrt source file 2024-12-18T01:36:52.4419101Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2024-12-18T01:36:52.4420446Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 45 <- wrt source file 2024-12-18T01:36:52.4422781Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2024-12-18T01:36:52.4424162Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 105 <- wrt source file 2024-12-18T01:36:52.4425386Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2024-12-18T01:36:52.4426661Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 159 <- wrt source file 2024-12-18T01:36:52.4855077Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2024-12-18T01:36:52.4856529Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 345 <- wrt source file 2024-12-18T01:36:52.4857733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2024-12-18T01:36:52.4859196Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 376 <- wrt source file 2024-12-18T01:36:52.4860410Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2024-12-18T01:36:52.4861933Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 397 <- wrt source file 2024-12-18T01:36:52.4863865Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2024-12-18T01:36:52.4865468Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 409 <- wrt source file 2024-12-18T01:36:52.4866750Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0 2024-12-18T01:36:52.4868274Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 493 <- wrt source file 2024-12-18T01:36:52.4869538Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0 2024-12-18T01:36:52.4871042Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 591 <- wrt source file 2024-12-18T01:36:52.4872301Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0 2024-12-18T01:36:52.4873555Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file 2024-12-18T01:36:52.4874871Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2024-12-18T01:36:52.4876247Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file 2024-12-18T01:36:52.4877527Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0 2024-12-18T01:36:52.4878729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file 2024-12-18T01:36:52.4879993Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0 2024-12-18T01:36:52.4881111Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0, line 146 <- wrt source file 2024-12-18T01:36:52.4882221Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0 2024-12-18T01:36:52.4883424Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2024-12-18T01:36:52.4900550Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2024-12-18T01:36:52.4901895Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 59 <- wrt source file 2024-12-18T01:36:52.4922431Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2024-12-18T01:36:52.4925105Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 218 <- wrt source file 2024-12-18T01:36:52.4927503Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0 2024-12-18T01:36:52.4929689Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 280 <- wrt source file 2024-12-18T01:36:52.4991527Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0 2024-12-18T01:36:52.4993768Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 364 <- wrt source file 2024-12-18T01:36:52.5011949Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2024-12-18T01:36:52.5014245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 428 <- wrt source file 2024-12-18T01:36:52.5028245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0 2024-12-18T01:36:52.5030579Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file 2024-12-18T01:36:52.5044477Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2024-12-18T01:36:52.5046847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file 2024-12-18T01:36:52.5100770Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2024-12-18T01:36:52.5103341Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 254 <- wrt source file 2024-12-18T01:36:52.5105739Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2024-12-18T01:36:52.5108216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 287 <- wrt source file 2024-12-18T01:36:52.5110855Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2024-12-18T01:36:52.5113277Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 896 <- wrt source file 2024-12-18T01:36:52.5451820Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2024-12-18T01:36:52.5453999Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0, line 324 <- wrt source file 2024-12-18T01:36:52.5456056Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0 2024-12-18T01:36:52.5458340Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2024-12-18T01:36:52.5460897Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2024-12-18T01:36:52.5463464Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 232 <- wrt source file 2024-12-18T01:36:52.5491283Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2024-12-18T01:36:52.5493637Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 474 <- wrt source file 2024-12-18T01:36:52.5548574Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2024-12-18T01:36:52.5550831Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2024-12-18T01:36:52.6012815Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2024-12-18T01:36:52.6015215Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2024-12-18T01:36:52.6071771Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2024-12-18T01:36:52.6074138Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2024-12-18T01:36:52.6091506Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2024-12-18T01:36:52.6094470Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2024-12-18T01:36:52.6097012Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2024-12-18T01:36:52.6099761Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1705 <- wrt source file 2024-12-18T01:36:52.6261109Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2024-12-18T01:36:52.6263517Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2024-12-18T01:36:52.6267636Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2024-12-18T01:36:52.6269985Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2024-12-18T01:36:52.6272240Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2024-12-18T01:36:52.6274845Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 112 <- wrt source file 2024-12-18T01:36:52.6278101Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2024-12-18T01:36:52.6280810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 120 <- wrt source file 2024-12-18T01:36:52.6283408Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2024-12-18T01:36:52.6286058Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 259 <- wrt source file 2024-12-18T01:36:52.6288874Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2024-12-18T01:36:52.6291253Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 112 <- wrt source file 2024-12-18T01:36:52.6293474Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2024-12-18T01:36:52.6295562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 96 <- wrt source file 2024-12-18T01:36:52.6297702Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2024-12-18T01:36:52.6300054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 86 <- wrt source file 2024-12-18T01:36:52.6302725Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0 2024-12-18T01:36:52.6305237Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1348 <- wrt source file 2024-12-18T01:36:52.6307939Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2024-12-18T01:36:52.6310319Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 71 <- wrt source file 2024-12-18T01:36:52.6584651Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2024-12-18T01:36:52.6587056Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 200 <- wrt source file 2024-12-18T01:36:52.6662535Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2024-12-18T01:36:52.6665083Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 269 <- wrt source file 2024-12-18T01:36:52.6667642Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2024-12-18T01:36:52.6670128Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 375 <- wrt source file 2024-12-18T01:36:52.6736223Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2024-12-18T01:36:52.6738754Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 502 <- wrt source file 2024-12-18T01:36:52.6886760Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2024-12-18T01:36:52.6889279Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 674 <- wrt source file 2024-12-18T01:36:52.7033600Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2024-12-18T01:36:52.7036150Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 197 <- wrt source file 2024-12-18T01:36:52.7038700Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2024-12-18T01:36:52.7041401Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 161 <- wrt source file 2024-12-18T01:36:52.7100125Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2024-12-18T01:36:52.7101621Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 45 <- wrt source file 2024-12-18T01:36:52.7105992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2024-12-18T01:36:52.7107360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 425 <- wrt source file 2024-12-18T01:36:52.7108556Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2024-12-18T01:36:52.7109849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file 2024-12-18T01:36:52.7164916Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2024-12-18T01:36:52.7166231Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 305 <- wrt source file 2024-12-18T01:36:52.7167544Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2024-12-18T01:36:52.7168815Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 996 <- wrt source file 2024-12-18T01:36:52.7214209Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2024-12-18T01:36:52.7215532Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1061 <- wrt source file 2024-12-18T01:36:52.7216840Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2024-12-18T01:36:52.7218110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1282 <- wrt source file 2024-12-18T01:36:52.7232559Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2024-12-18T01:36:52.7233887Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1348 <- wrt source file 2024-12-18T01:36:52.7236742Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2024-12-18T01:36:52.7238246Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1411 <- wrt source file 2024-12-18T01:36:52.7241477Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2024-12-18T01:36:52.7242801Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1456 <- wrt source file 2024-12-18T01:36:52.7244022Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2024-12-18T01:36:52.7245227Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1566 <- wrt source file 2024-12-18T01:36:52.7247728Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2024-12-18T01:36:52.7249287Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file 2024-12-18T01:36:52.7250887Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2024-12-18T01:36:52.7252603Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file 2024-12-18T01:36:52.7254136Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2024-12-18T01:36:52.7255464Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file 2024-12-18T01:36:52.7257008Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2024-12-18T01:36:52.7258548Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2024-12-18T01:36:52.7260208Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2024-12-18T01:36:52.7261751Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2024-12-18T01:36:52.7263299Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2024-12-18T01:36:52.7264847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file 2024-12-18T01:36:52.7266451Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2024-12-18T01:36:52.7267996Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file 2024-12-18T01:36:52.7269550Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2024-12-18T01:36:52.7270944Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 30 <- wrt source file 2024-12-18T01:36:52.7275293Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2024-12-18T01:36:52.7277203Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 410 <- wrt source file 2024-12-18T01:36:52.7307304Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2024-12-18T01:36:52.7309582Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file 2024-12-18T01:36:52.7311332Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2024-12-18T01:36:52.7312640Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file 2024-12-18T01:36:52.7313910Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2024-12-18T01:36:52.7315283Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file 2024-12-18T01:36:52.7317090Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2024-12-18T01:36:52.7318471Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2024-12-18T01:36:52.7319833Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2024-12-18T01:36:52.7321526Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2024-12-18T01:36:52.7323548Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2024-12-18T01:36:52.7325210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file 2024-12-18T01:36:52.7327052Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2024-12-18T01:36:52.7328879Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file 2024-12-18T01:36:52.7330854Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2024-12-18T01:36:52.7332415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 208 <- wrt source file 2024-12-18T01:36:52.7333898Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2024-12-18T01:36:52.7335495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 294 <- wrt source file 2024-12-18T01:36:52.7337418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2024-12-18T01:36:52.7339309Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 376 <- wrt source file 2024-12-18T01:36:52.7341143Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2024-12-18T01:36:52.7342655Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 458 <- wrt source file 2024-12-18T01:36:52.7344157Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2024-12-18T01:36:52.7345725Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2024-12-18T01:36:52.7348091Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2024-12-18T01:36:52.7350615Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file 2024-12-18T01:36:52.7353160Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2024-12-18T01:36:52.7355695Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2024-12-18T01:36:52.7358283Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2024-12-18T01:36:52.7360798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file 2024-12-18T01:36:52.7363466Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2024-12-18T01:36:52.7366038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file 2024-12-18T01:36:52.7368661Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2024-12-18T01:36:52.7371296Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file 2024-12-18T01:36:52.7373903Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2024-12-18T01:36:52.7376413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2024-12-18T01:36:52.7378953Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2024-12-18T01:36:52.7381360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 505 <- wrt source file 2024-12-18T01:36:52.7383752Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2024-12-18T01:36:52.7386302Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 634 <- wrt source file 2024-12-18T01:36:52.7389076Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2024-12-18T01:36:52.7391940Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 890 <- wrt source file 2024-12-18T01:36:52.7394849Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2024-12-18T01:36:52.7398109Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1012 <- wrt source file 2024-12-18T01:36:52.7401153Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2024-12-18T01:36:52.7404112Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1138 <- wrt source file 2024-12-18T01:36:52.7406818Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2024-12-18T01:36:52.7409407Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file 2024-12-18T01:36:52.7412084Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2024-12-18T01:36:52.7414705Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 276 <- wrt source file 2024-12-18T01:36:52.7417427Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2024-12-18T01:36:52.7420258Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 24 <- wrt source file 2024-12-18T01:36:52.7423177Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2024-12-18T01:36:52.7426039Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 177 <- wrt source file 2024-12-18T01:36:52.7428866Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2024-12-18T01:36:52.7431442Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2024-12-18T01:36:52.7433945Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2024-12-18T01:36:52.7436972Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file 2024-12-18T01:36:52.7440455Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2024-12-18T01:36:52.7443890Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file 2024-12-18T01:36:52.7548203Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0 2024-12-18T01:36:52.7551544Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2024-12-18T01:36:52.7554815Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2024-12-18T01:36:52.7557742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file 2024-12-18T01:36:52.7560388Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2024-12-18T01:36:52.7563047Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2024-12-18T01:36:52.7565827Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2024-12-18T01:36:52.7568530Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 176 <- wrt source file 2024-12-18T01:36:52.7571044Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2024-12-18T01:36:52.7573567Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2024-12-18T01:36:52.7576239Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2024-12-18T01:36:52.7578948Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2024-12-18T01:36:52.7581715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2024-12-18T01:36:52.7584464Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2024-12-18T01:36:52.7587185Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2024-12-18T01:36:52.7589939Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2024-12-18T01:36:52.7592808Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2024-12-18T01:36:52.7595411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 93 <- wrt source file 2024-12-18T01:36:52.7598027Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0 2024-12-18T01:36:52.7600444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 115 <- wrt source file 2024-12-18T01:36:52.7603018Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2024-12-18T01:36:52.7605424Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2024-12-18T01:36:52.7607794Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2024-12-18T01:36:52.7610176Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file 2024-12-18T01:36:52.7612631Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2024-12-18T01:36:52.7615067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file 2024-12-18T01:36:52.7617593Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2024-12-18T01:36:52.7620023Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 595 <- wrt source file 2024-12-18T01:36:52.7622455Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2024-12-18T01:36:52.7625053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 654 <- wrt source file 2024-12-18T01:36:52.7627745Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2024-12-18T01:36:52.7630495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 706 <- wrt source file 2024-12-18T01:36:52.7633400Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2024-12-18T01:36:52.7636156Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 49 <- wrt source file 2024-12-18T01:36:52.7638763Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2024-12-18T01:36:52.7641269Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 128 <- wrt source file 2024-12-18T01:36:52.7643936Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2024-12-18T01:36:52.7646458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 225 <- wrt source file 2024-12-18T01:36:52.7648988Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2024-12-18T01:36:52.7651507Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file 2024-12-18T01:36:52.7653973Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2024-12-18T01:36:52.7656387Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 517 <- wrt source file 2024-12-18T01:36:52.7658869Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2024-12-18T01:36:52.7661322Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 539 <- wrt source file 2024-12-18T01:36:52.7663852Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2024-12-18T01:36:52.7666309Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 553 <- wrt source file 2024-12-18T01:36:52.7668777Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2024-12-18T01:36:52.7684846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 575 <- wrt source file 2024-12-18T01:36:52.7687356Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2024-12-18T01:36:52.7689754Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 702 <- wrt source file 2024-12-18T01:36:52.7692200Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2024-12-18T01:36:52.7694845Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2024-12-18T01:36:52.7698042Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2024-12-18T01:36:52.7700865Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file 2024-12-18T01:36:52.7703648Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2024-12-18T01:36:52.7706398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file 2024-12-18T01:36:52.7709328Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2024-12-18T01:36:52.7712194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 459 <- wrt source file 2024-12-18T01:36:52.7715217Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2024-12-18T01:36:52.7717889Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file 2024-12-18T01:36:52.7720263Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2024-12-18T01:36:52.7722498Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 83 <- wrt source file 2024-12-18T01:36:52.7724770Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2024-12-18T01:36:52.7726958Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 153 <- wrt source file 2024-12-18T01:36:52.7729228Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2024-12-18T01:36:52.7731441Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 189 <- wrt source file 2024-12-18T01:36:52.7733688Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2024-12-18T01:36:52.7736059Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 66 <- wrt source file 2024-12-18T01:36:52.7738655Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2024-12-18T01:36:52.7741216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 109 <- wrt source file 2024-12-18T01:36:52.7743800Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2024-12-18T01:36:52.7746273Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 160 <- wrt source file 2024-12-18T01:36:52.7748753Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2024-12-18T01:36:52.7751324Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 207 <- wrt source file 2024-12-18T01:36:52.7754101Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2024-12-18T01:36:52.7756823Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 236 <- wrt source file 2024-12-18T01:36:52.7759516Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2024-12-18T01:36:52.7761905Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file 2024-12-18T01:36:52.7764106Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2024-12-18T01:36:52.7766268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 294 <- wrt source file 2024-12-18T01:36:52.7768434Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2024-12-18T01:36:52.7770545Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 396 <- wrt source file 2024-12-18T01:36:52.7772803Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2024-12-18T01:36:52.7774961Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 631 <- wrt source file 2024-12-18T01:36:52.7777212Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2024-12-18T01:36:52.7779389Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 885 <- wrt source file 2024-12-18T01:36:52.7781643Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2024-12-18T01:36:52.7783771Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file 2024-12-18T01:36:52.7785930Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2024-12-18T01:36:52.7788036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file 2024-12-18T01:36:52.7790186Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2024-12-18T01:36:52.7792280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2024-12-18T01:36:52.7794494Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2024-12-18T01:36:52.7796716Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2024-12-18T01:36:52.7799102Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2024-12-18T01:36:52.7801342Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2024-12-18T01:36:52.7803692Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2024-12-18T01:36:52.7805966Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 232 <- wrt source file 2024-12-18T01:36:52.7808287Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2024-12-18T01:36:52.7810526Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 62 <- wrt source file 2024-12-18T01:36:52.7812664Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0 2024-12-18T01:36:52.7814847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 119 <- wrt source file 2024-12-18T01:36:52.7817156Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0 2024-12-18T01:36:52.7819468Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 156 <- wrt source file 2024-12-18T01:36:52.7821894Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2024-12-18T01:36:52.7824210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 280 <- wrt source file 2024-12-18T01:36:52.7826601Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2024-12-18T01:36:52.7828784Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 345 <- wrt source file 2024-12-18T01:36:52.7830931Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0 2024-12-18T01:36:52.7833228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 402 <- wrt source file 2024-12-18T01:36:52.7835828Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2024-12-18T01:36:52.7838242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 479 <- wrt source file 2024-12-18T01:36:52.7840660Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2024-12-18T01:36:52.7843099Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 735 <- wrt source file 2024-12-18T01:36:52.7845656Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2024-12-18T01:36:52.7847957Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 177 <- wrt source file 2024-12-18T01:36:52.7850148Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0 2024-12-18T01:36:52.7852361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0, line 714 <- wrt source file 2024-12-18T01:36:52.7854694Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0 2024-12-18T01:36:52.7856896Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 848 <- wrt source file 2024-12-18T01:36:52.7859084Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0 2024-12-18T01:36:52.7861241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 921 <- wrt source file 2024-12-18T01:36:52.7863449Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2024-12-18T01:36:52.7865636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file 2024-12-18T01:36:52.7867854Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2024-12-18T01:36:52.7870018Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2024-12-18T01:36:52.7872237Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2024-12-18T01:36:52.7874416Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file 2024-12-18T01:36:52.7876756Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2024-12-18T01:36:52.7879080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file 2024-12-18T01:36:52.7881624Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2024-12-18T01:36:52.7883842Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2024-12-18T01:36:52.7885934Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0 2024-12-18T01:36:52.7888122Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 411 <- wrt source file 2024-12-18T01:36:52.7890512Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2024-12-18T01:36:52.7892987Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 890 <- wrt source file 2024-12-18T01:36:52.7895635Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2024-12-18T01:36:52.7898260Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 972 <- wrt source file 2024-12-18T01:36:52.7900747Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2024-12-18T01:36:52.7903279Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2539 <- wrt source file 2024-12-18T01:36:52.7905947Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2024-12-18T01:36:52.7908542Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0, line 2629 <- wrt source file 2024-12-18T01:36:52.7911157Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0 2024-12-18T01:36:52.7913649Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0, line 2757 <- wrt source file 2024-12-18T01:36:52.7916171Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0 2024-12-18T01:36:52.7918673Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3008 <- wrt source file 2024-12-18T01:36:52.7921295Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2024-12-18T01:36:52.7923941Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3229 <- wrt source file 2024-12-18T01:36:52.7926510Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2024-12-18T01:36:52.7929040Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3327 <- wrt source file 2024-12-18T01:36:52.7931629Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2024-12-18T01:36:52.7934273Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3437 <- wrt source file 2024-12-18T01:36:52.7936980Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2024-12-18T01:36:52.7939660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3556 <- wrt source file 2024-12-18T01:36:52.7942314Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2024-12-18T01:36:52.7944818Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather:0, line 3658 <- wrt source file 2024-12-18T01:36:52.7947667Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather:0 2024-12-18T01:36:52.7950644Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3740 <- wrt source file 2024-12-18T01:36:52.7953678Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 2024-12-18T01:36:52.7956472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3865 <- wrt source file 2024-12-18T01:36:52.7959138Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0 2024-12-18T01:36:52.7961617Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0, line 3967 <- wrt source file 2024-12-18T01:36:52.7964019Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::gather:0 2024-12-18T01:36:52.7966374Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 4049 <- wrt source file 2024-12-18T01:36:52.7968783Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter:0 2024-12-18T01:36:52.7971287Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 4182 <- wrt source file 2024-12-18T01:36:52.7973983Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0 2024-12-18T01:36:52.7976586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_to_all_single:0, line 4309 <- wrt source file 2024-12-18T01:36:52.7979173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_to_all_single:0 2024-12-18T01:36:52.7981716Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_to_all:0, line 4421 <- wrt source file 2024-12-18T01:36:52.7984175Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_to_all:0 2024-12-18T01:36:52.7986690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4599 <- wrt source file 2024-12-18T01:36:52.7989322Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0 2024-12-18T01:36:52.7991832Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0, line 5177 <- wrt source file 2024-12-18T01:36:52.7994416Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0 2024-12-18T01:36:52.7997156Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0, line 5279 <- wrt source file 2024-12-18T01:36:52.8000217Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0 2024-12-18T01:36:52.8002617Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/run.py::__doc__:0, line 87 <- wrt source file 2024-12-18T01:36:52.8004718Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/run.py::__doc__:0 2024-12-18T01:36:52.8006932Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file 2024-12-18T01:36:52.8009387Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0 2024-12-18T01:36:52.8011935Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 48 <- wrt source file 2024-12-18T01:36:52.8014741Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2024-12-18T01:36:52.8017329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0, line 46 <- wrt source file 2024-12-18T01:36:52.8019828Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0 2024-12-18T01:36:52.8022321Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 188 <- wrt source file 2024-12-18T01:36:52.8024869Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2024-12-18T01:36:52.8027595Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 30 <- wrt source file 2024-12-18T01:36:52.8030624Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0 2024-12-18T01:36:52.8033516Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file 2024-12-18T01:36:52.8036435Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0 2024-12-18T01:36:52.8039318Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file 2024-12-18T01:36:52.8042193Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0 2024-12-18T01:36:52.8044887Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0, line 54 <- wrt source file 2024-12-18T01:36:52.8047499Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0 2024-12-18T01:36:52.8049944Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/join.py::Join:0, line 141 <- wrt source file 2024-12-18T01:36:52.8052341Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/join.py::Join:0 2024-12-18T01:36:52.8054950Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0, line 107 <- wrt source file 2024-12-18T01:36:52.8058000Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0 2024-12-18T01:36:52.8060914Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0, line 23 <- wrt source file 2024-12-18T01:36:52.8063855Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0 2024-12-18T01:36:52.8066798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0, line 49 <- wrt source file 2024-12-18T01:36:52.8069799Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0 2024-12-18T01:36:52.8072759Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0, line 104 <- wrt source file 2024-12-18T01:36:52.8075877Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0 2024-12-18T01:36:52.8078898Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0, line 125 <- wrt source file 2024-12-18T01:36:52.8081962Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0 2024-12-18T01:36:52.8084993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0, line 143 <- wrt source file 2024-12-18T01:36:52.8088123Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0 2024-12-18T01:36:52.8091190Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0, line 182 <- wrt source file 2024-12-18T01:36:52.8094293Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0 2024-12-18T01:36:52.8097345Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0, line 708 <- wrt source file 2024-12-18T01:36:52.8100736Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0 2024-12-18T01:36:52.8103939Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0, line 64 <- wrt source file 2024-12-18T01:36:52.8107298Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0 2024-12-18T01:36:52.8110615Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0, line 145 <- wrt source file 2024-12-18T01:36:52.8114091Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0 2024-12-18T01:36:52.8117184Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0, line 1082 <- wrt source file 2024-12-18T01:36:52.8119866Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0 2024-12-18T01:36:52.8122552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1323 <- wrt source file 2024-12-18T01:36:52.8125375Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0 2024-12-18T01:36:52.8128224Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1382 <- wrt source file 2024-12-18T01:36:52.8131118Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0 2024-12-18T01:36:52.8134042Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0, line 225 <- wrt source file 2024-12-18T01:36:52.8137045Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0 2024-12-18T01:36:52.8139859Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0, line 141 <- wrt source file 2024-12-18T01:36:52.8142633Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0 2024-12-18T01:36:52.8145165Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::StateDictType:0, line 260 <- wrt source file 2024-12-18T01:36:52.8147543Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::StateDictType:0 2024-12-18T01:36:52.8149953Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0, line 301 <- wrt source file 2024-12-18T01:36:52.8152571Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0 2024-12-18T01:36:52.8155348Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0, line 140 <- wrt source file 2024-12-18T01:36:52.8158528Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0 2024-12-18T01:36:52.8161870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0, line 1503 <- wrt source file 2024-12-18T01:36:52.8165479Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0 2024-12-18T01:36:52.8169036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0, line 1623 <- wrt source file 2024-12-18T01:36:52.8172721Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0 2024-12-18T01:36:52.8176252Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0, line 1708 <- wrt source file 2024-12-18T01:36:52.8179798Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0 2024-12-18T01:36:52.8182851Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0, line 53 <- wrt source file 2024-12-18T01:36:52.8185642Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0 2024-12-18T01:36:52.8188193Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 236 <- wrt source file 2024-12-18T01:36:52.8190589Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0 2024-12-18T01:36:52.8192983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file 2024-12-18T01:36:52.8195491Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0 2024-12-18T01:36:52.8198425Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 42 <- wrt source file 2024-12-18T01:36:52.8201523Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0 2024-12-18T01:36:52.8204570Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 113 <- wrt source file 2024-12-18T01:36:52.8207683Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0 2024-12-18T01:36:52.8210494Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 53 <- wrt source file 2024-12-18T01:36:52.8213173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0 2024-12-18T01:36:52.8215790Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 38 <- wrt source file 2024-12-18T01:36:52.8218453Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0 2024-12-18T01:36:52.8220995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0, line 334 <- wrt source file 2024-12-18T01:36:52.8223416Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0 2024-12-18T01:36:52.8226072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0, line 82 <- wrt source file 2024-12-18T01:36:52.8229018Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0 2024-12-18T01:36:52.8231967Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0, line 101 <- wrt source file 2024-12-18T01:36:52.8234906Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0 2024-12-18T01:36:52.8237514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 175 <- wrt source file 2024-12-18T01:36:52.8239752Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0 2024-12-18T01:36:52.8241938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 346 <- wrt source file 2024-12-18T01:36:52.8244174Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::shutdown:0 2024-12-18T01:36:52.8246380Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::remote:0, line 605 <- wrt source file 2024-12-18T01:36:52.8248584Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::remote:0 2024-12-18T01:36:52.8250743Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 785 <- wrt source file 2024-12-18T01:36:52.8252956Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_sync:0 2024-12-18T01:36:52.8255140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 877 <- wrt source file 2024-12-18T01:36:52.8257388Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::rpc_async:0 2024-12-18T01:36:52.8260060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0, line 61 <- wrt source file 2024-12-18T01:36:52.8263242Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0 2024-12-18T01:36:52.8265951Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0, line 796 <- wrt source file 2024-12-18T01:36:52.8268318Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_api.py::_shard_tensor:0 2024-12-18T01:36:52.8270979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 237 <- wrt source file 2024-12-18T01:36:52.8273959Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0 2024-12-18T01:36:52.8276826Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0, line 235 <- wrt source file 2024-12-18T01:36:52.8279532Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0 2024-12-18T01:36:52.8282205Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 49 <- wrt source file 2024-12-18T01:36:52.8284924Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0 2024-12-18T01:36:52.8287625Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0, line 88 <- wrt source file 2024-12-18T01:36:52.8290468Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0 2024-12-18T01:36:52.8293131Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0, line 55 <- wrt source file 2024-12-18T01:36:52.8295802Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0 2024-12-18T01:36:52.8298557Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0, line 62 <- wrt source file 2024-12-18T01:36:52.8301292Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0 2024-12-18T01:36:52.8304021Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0, line 180 <- wrt source file 2024-12-18T01:36:52.8306765Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0 2024-12-18T01:36:52.8309453Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0, line 308 <- wrt source file 2024-12-18T01:36:52.8312205Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0 2024-12-18T01:36:52.8314698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 29 <- wrt source file 2024-12-18T01:36:52.8317076Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2024-12-18T01:36:52.8319265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 20 <- wrt source file 2024-12-18T01:36:52.8321400Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0 2024-12-18T01:36:52.8323588Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 28 <- wrt source file 2024-12-18T01:36:52.8325902Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0 2024-12-18T01:36:52.8328236Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file 2024-12-18T01:36:52.8330699Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0 2024-12-18T01:36:52.8332980Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2024-12-18T01:36:52.8335267Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0 2024-12-18T01:36:52.8337397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0, line 15 <- wrt source file 2024-12-18T01:36:52.8339519Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0 2024-12-18T01:36:52.8342108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0, line 160 <- wrt source file 2024-12-18T01:36:52.8345069Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0 2024-12-18T01:36:52.8348164Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 181 <- wrt source file 2024-12-18T01:36:52.8350908Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2024-12-18T01:36:52.8353695Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 34 <- wrt source file 2024-12-18T01:36:52.8356690Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2024-12-18T01:36:52.8359309Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 39 <- wrt source file 2024-12-18T01:36:52.8361761Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2024-12-18T01:36:52.8364237Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0, line 19 <- wrt source file 2024-12-18T01:36:52.8366802Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0 2024-12-18T01:36:52.8369393Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2024-12-18T01:36:52.8372101Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2024-12-18T01:36:52.8374509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0, line 23 <- wrt source file 2024-12-18T01:36:52.8376709Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0 2024-12-18T01:36:52.8378946Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0, line 34 <- wrt source file 2024-12-18T01:36:52.8381309Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0 2024-12-18T01:36:52.8383548Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 21 <- wrt source file 2024-12-18T01:36:52.8385770Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0 2024-12-18T01:36:52.8388028Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 23 <- wrt source file 2024-12-18T01:36:52.8390435Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2024-12-18T01:36:52.8392775Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file 2024-12-18T01:36:52.8395209Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2024-12-18T01:36:52.8397676Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0, line 23 <- wrt source file 2024-12-18T01:36:52.8400264Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0 2024-12-18T01:36:52.8402693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 21 <- wrt source file 2024-12-18T01:36:52.8405186Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2024-12-18T01:36:52.8407698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 28 <- wrt source file 2024-12-18T01:36:52.8410150Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2024-12-18T01:36:52.8412480Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0, line 19 <- wrt source file 2024-12-18T01:36:52.8414727Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0 2024-12-18T01:36:52.8417013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file 2024-12-18T01:36:52.8419458Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2024-12-18T01:36:52.8421857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 20 <- wrt source file 2024-12-18T01:36:52.8424201Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0 2024-12-18T01:36:52.8426625Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 25 <- wrt source file 2024-12-18T01:36:52.8429201Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2024-12-18T01:36:52.8431990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 61 <- wrt source file 2024-12-18T01:36:52.8435046Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0 2024-12-18T01:36:52.8437812Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 36 <- wrt source file 2024-12-18T01:36:52.8440260Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0 2024-12-18T01:36:52.8442839Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 101 <- wrt source file 2024-12-18T01:36:52.8445622Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2024-12-18T01:36:52.8448050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0, line 21 <- wrt source file 2024-12-18T01:36:52.8450272Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0 2024-12-18T01:36:52.8452750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 31 <- wrt source file 2024-12-18T01:36:52.8455472Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2024-12-18T01:36:52.8457854Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0, line 17 <- wrt source file 2024-12-18T01:36:52.8460087Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0 2024-12-18T01:36:52.8462286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0, line 23 <- wrt source file 2024-12-18T01:36:52.8464587Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0 2024-12-18T01:36:52.8466836Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0, line 21 <- wrt source file 2024-12-18T01:36:52.8469176Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0 2024-12-18T01:36:52.8471544Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1033 <- wrt source file 2024-12-18T01:36:52.8474003Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0 2024-12-18T01:36:52.8476474Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1139 <- wrt source file 2024-12-18T01:36:52.8479043Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0 2024-12-18T01:36:52.8481740Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1213 <- wrt source file 2024-12-18T01:36:52.8484660Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0 2024-12-18T01:36:52.8487162Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file 2024-12-18T01:36:52.8489407Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0 2024-12-18T01:36:52.8491626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0, line 107 <- wrt source file 2024-12-18T01:36:52.8493925Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0 2024-12-18T01:36:52.8496166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0, line 115 <- wrt source file 2024-12-18T01:36:52.8498595Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0 2024-12-18T01:36:52.8500844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0, line 19 <- wrt source file 2024-12-18T01:36:52.8503094Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0 2024-12-18T01:36:52.8505293Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0, line 40 <- wrt source file 2024-12-18T01:36:52.8507553Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0 2024-12-18T01:36:52.8509931Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 602 <- wrt source file 2024-12-18T01:36:52.8512394Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2024-12-18T01:36:52.8514561Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0, line 105 <- wrt source file 2024-12-18T01:36:52.8516659Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0 2024-12-18T01:36:52.8518814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1833 <- wrt source file 2024-12-18T01:36:52.8521184Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0 2024-12-18T01:36:52.8523437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1907 <- wrt source file 2024-12-18T01:36:52.8525733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0 2024-12-18T01:36:52.8527929Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0, line 44 <- wrt source file 2024-12-18T01:36:52.8530116Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0 2024-12-18T01:36:52.8532259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0, line 454 <- wrt source file 2024-12-18T01:36:52.8534542Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0 2024-12-18T01:36:52.8536779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0, line 135 <- wrt source file 2024-12-18T01:36:52.8539159Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0 2024-12-18T01:36:52.8541364Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file 2024-12-18T01:36:52.8543506Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::TensorType:0 2024-12-18T01:36:52.8545624Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0, line 65 <- wrt source file 2024-12-18T01:36:52.8547788Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0 2024-12-18T01:36:52.8549919Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- wrt source file 2024-12-18T01:36:52.8552137Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0 2024-12-18T01:36:52.8554608Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0, line 96 <- wrt source file 2024-12-18T01:36:52.8557415Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0 2024-12-18T01:36:52.8559985Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/core.py::reify:0, line 58 <- wrt source file 2024-12-18T01:36:52.8562483Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/core.py::reify:0 2024-12-18T01:36:52.8565072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0, line 48 <- wrt source file 2024-12-18T01:36:52.8567769Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0 2024-12-18T01:36:52.8570329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unifiable:0, line 11 <- wrt source file 2024-12-18T01:36:52.8572888Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unifiable:0 2024-12-18T01:36:52.8575422Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::reify_object:0, line 37 <- wrt source file 2024-12-18T01:36:52.8578086Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::reify_object:0 2024-12-18T01:36:52.8580642Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unify_object:0, line 93 <- wrt source file 2024-12-18T01:36:52.8583305Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/more.py::unify_object:0 2024-12-18T01:36:52.8585951Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0, line 37 <- wrt source file 2024-12-18T01:36:52.8588734Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0 2024-12-18T01:36:52.8591533Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0, line 64 <- wrt source file 2024-12-18T01:36:52.8594486Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0 2024-12-18T01:36:52.8597301Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0, line 90 <- wrt source file 2024-12-18T01:36:52.8600201Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0 2024-12-18T01:36:52.8602938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0, line 106 <- wrt source file 2024-12-18T01:36:52.8605738Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0 2024-12-18T01:36:52.8608509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0, line 122 <- wrt source file 2024-12-18T01:36:52.8611340Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0 2024-12-18T01:36:52.8614141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0, line 138 <- wrt source file 2024-12-18T01:36:52.8617013Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0 2024-12-18T01:36:52.8619829Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0, line 158 <- wrt source file 2024-12-18T01:36:52.8622715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0 2024-12-18T01:36:52.8625593Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0, line 178 <- wrt source file 2024-12-18T01:36:52.8628512Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0 2024-12-18T01:36:52.8631282Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0, line 204 <- wrt source file 2024-12-18T01:36:52.8634055Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0 2024-12-18T01:36:52.8636893Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0, line 221 <- wrt source file 2024-12-18T01:36:52.8639759Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0 2024-12-18T01:36:52.8642480Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 416 <- wrt source file 2024-12-18T01:36:52.8645302Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0 2024-12-18T01:36:52.8647975Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 15 <- wrt source file 2024-12-18T01:36:52.8650651Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0 2024-12-18T01:36:52.8653260Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 42 <- wrt source file 2024-12-18T01:36:52.8655840Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0 2024-12-18T01:36:52.8658402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 70 <- wrt source file 2024-12-18T01:36:52.8661059Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2024-12-18T01:36:52.8663595Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 95 <- wrt source file 2024-12-18T01:36:52.8666115Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2024-12-18T01:36:52.8668669Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 67 <- wrt source file 2024-12-18T01:36:52.8671355Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0 2024-12-18T01:36:52.8674126Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 21 <- wrt source file 2024-12-18T01:36:52.8677156Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0 2024-12-18T01:36:52.8680168Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 113 <- wrt source file 2024-12-18T01:36:52.8683348Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2024-12-18T01:36:52.8686591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 138 <- wrt source file 2024-12-18T01:36:52.8689959Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2024-12-18T01:36:52.8693208Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 191 <- wrt source file 2024-12-18T01:36:52.8696452Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0 2024-12-18T01:36:52.8699976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file 2024-12-18T01:36:52.8703352Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2024-12-18T01:36:52.8706649Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file 2024-12-18T01:36:52.8709881Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0 2024-12-18T01:36:52.8712966Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 18 <- wrt source file 2024-12-18T01:36:52.8716164Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0 2024-12-18T01:36:52.8719141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 41 <- wrt source file 2024-12-18T01:36:52.8722156Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0 2024-12-18T01:36:52.8725100Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 68 <- wrt source file 2024-12-18T01:36:52.8728160Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2024-12-18T01:36:52.8731120Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file 2024-12-18T01:36:52.8734088Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2024-12-18T01:36:52.8737003Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file 2024-12-18T01:36:52.8739983Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2024-12-18T01:36:52.8742954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file 2024-12-18T01:36:52.8746061Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0 2024-12-18T01:36:52.8749143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 83 <- wrt source file 2024-12-18T01:36:52.8752218Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0 2024-12-18T01:36:52.8754983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 122 <- wrt source file 2024-12-18T01:36:52.8757715Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0 2024-12-18T01:36:52.8760115Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 92 <- wrt source file 2024-12-18T01:36:52.8762431Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2024-12-18T01:36:52.8764679Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0, line 85 <- wrt source file 2024-12-18T01:36:52.8767080Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0 2024-12-18T01:36:52.8769881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 53 <- wrt source file 2024-12-18T01:36:52.8773139Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2024-12-18T01:36:52.8775993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2024-12-18T01:36:52.8778527Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2024-12-18T01:36:52.8780975Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2024-12-18T01:36:52.8783474Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0 2024-12-18T01:36:52.8785990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file 2024-12-18T01:36:52.8788615Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0 2024-12-18T01:36:52.8791095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file 2024-12-18T01:36:52.8793506Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0 2024-12-18T01:36:52.8795783Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0, line 1521 <- wrt source file 2024-12-18T01:36:52.8797992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0 2024-12-18T01:36:52.8800247Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file 2024-12-18T01:36:52.8802733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0 2024-12-18T01:36:52.8805219Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 467 <- wrt source file 2024-12-18T01:36:52.8807908Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2024-12-18T01:36:52.8810461Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 586 <- wrt source file 2024-12-18T01:36:52.9549778Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2024-12-18T01:36:52.9571730Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file 2024-12-18T01:36:52.9581297Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0 2024-12-18T01:36:52.9583795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file 2024-12-18T01:36:52.9590192Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0 2024-12-18T01:36:52.9592758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file 2024-12-18T01:36:52.9600934Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0 2024-12-18T01:36:52.9603145Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3059 <- wrt source file 2024-12-18T01:36:52.9617969Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2024-12-18T01:36:52.9620263Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3136 <- wrt source file 2024-12-18T01:36:52.9624821Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2024-12-18T01:36:52.9627280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3466 <- wrt source file 2024-12-18T01:36:52.9633960Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2024-12-18T01:36:52.9640956Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3538 <- wrt source file 2024-12-18T01:36:52.9643302Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2024-12-18T01:36:52.9646183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3615 <- wrt source file 2024-12-18T01:36:52.9649171Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2024-12-18T01:36:52.9651700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5178 <- wrt source file 2024-12-18T01:36:52.9657473Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0 2024-12-18T01:36:52.9659459Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2024-12-18T01:36:52.9665538Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2024-12-18T01:36:52.9667555Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2024-12-18T01:36:52.9670744Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2024-12-18T01:36:52.9672760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2024-12-18T01:36:52.9678513Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2024-12-18T01:36:52.9680531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2024-12-18T01:36:52.9683454Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2024-12-18T01:36:52.9685472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2024-12-18T01:36:52.9717713Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2024-12-18T01:36:52.9720397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2024-12-18T01:36:52.9737821Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2024-12-18T01:36:52.9740197Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2024-12-18T01:36:52.9742496Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0 2024-12-18T01:36:52.9744477Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2024-12-18T01:36:52.9746526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2024-12-18T01:36:52.9748471Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2024-12-18T01:36:52.9750423Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2024-12-18T01:36:52.9752390Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2024-12-18T01:36:52.9768735Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2024-12-18T01:36:52.9770734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2024-12-18T01:36:52.9772727Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2024-12-18T01:36:52.9774639Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2024-12-18T01:36:52.9776570Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2024-12-18T01:36:52.9778465Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2024-12-18T01:36:52.9780405Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2024-12-18T01:36:52.9782270Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2024-12-18T01:36:52.9784169Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2024-12-18T01:36:52.9786038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2024-12-18T01:36:52.9788206Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2024-12-18T01:36:52.9790270Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2024-12-18T01:36:52.9792358Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2024-12-18T01:36:52.9794403Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2024-12-18T01:36:52.9796524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2024-12-18T01:36:52.9798725Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2024-12-18T01:36:52.9800947Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2024-12-18T01:36:52.9803011Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2024-12-18T01:36:52.9805099Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2024-12-18T01:36:52.9807159Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2024-12-18T01:36:52.9809200Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2024-12-18T01:36:52.9811159Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2024-12-18T01:36:52.9813126Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2024-12-18T01:36:52.9815268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 103 <- wrt source file 2024-12-18T01:36:52.9817572Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2024-12-18T01:36:52.9819801Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2024-12-18T01:36:52.9822024Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2024-12-18T01:36:52.9824233Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2024-12-18T01:36:52.9826536Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2024-12-18T01:36:52.9828741Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2024-12-18T01:36:52.9830943Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2024-12-18T01:36:52.9833118Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2024-12-18T01:36:52.9835360Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2024-12-18T01:36:52.9837693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2024-12-18T01:36:52.9839972Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2024-12-18T01:36:52.9842173Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2024-12-18T01:36:52.9844434Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2024-12-18T01:36:52.9846634Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2024-12-18T01:36:52.9848887Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2024-12-18T01:36:52.9851148Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2024-12-18T01:36:52.9853500Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2024-12-18T01:36:52.9855786Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2024-12-18T01:36:52.9857992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2024-12-18T01:36:52.9860158Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2024-12-18T01:36:52.9862386Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2024-12-18T01:36:52.9864530Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2024-12-18T01:36:52.9866722Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2024-12-18T01:36:52.9868932Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2024-12-18T01:36:52.9871267Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2024-12-18T01:36:52.9873462Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2024-12-18T01:36:52.9875721Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2024-12-18T01:36:52.9877863Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2024-12-18T01:36:52.9880062Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2024-12-18T01:36:52.9882200Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2024-12-18T01:36:52.9884389Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2024-12-18T01:36:52.9886531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2024-12-18T01:36:52.9888695Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2024-12-18T01:36:52.9890840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2024-12-18T01:36:52.9893030Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2024-12-18T01:36:52.9895253Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2024-12-18T01:36:52.9897597Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2024-12-18T01:36:52.9900133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2024-12-18T01:36:52.9902790Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2024-12-18T01:36:52.9905415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2024-12-18T01:36:52.9908115Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2024-12-18T01:36:52.9910357Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2024-12-18T01:36:52.9912726Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2024-12-18T01:36:52.9914984Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2024-12-18T01:36:52.9917407Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2024-12-18T01:36:52.9919820Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file 2024-12-18T01:36:52.9922345Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2024-12-18T01:36:52.9924680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file 2024-12-18T01:36:52.9926974Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2024-12-18T01:36:52.9929206Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file 2024-12-18T01:36:52.9931803Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2024-12-18T01:36:52.9934434Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file 2024-12-18T01:36:52.9937152Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2024-12-18T01:36:52.9939715Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file 2024-12-18T01:36:52.9942333Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2024-12-18T01:36:52.9944867Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file 2024-12-18T01:36:52.9947488Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2024-12-18T01:36:52.9950095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file 2024-12-18T01:36:52.9952732Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2024-12-18T01:36:52.9955360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file 2024-12-18T01:36:52.9958159Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2024-12-18T01:36:52.9960863Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2024-12-18T01:36:52.9963654Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2024-12-18T01:36:52.9965932Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2024-12-18T01:36:53.0226033Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2024-12-18T01:36:53.0228772Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2024-12-18T01:36:53.2803959Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2024-12-18T01:36:53.2962006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2024-12-18T01:36:53.2982484Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2024-12-18T01:36:53.2985102Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file 2024-12-18T01:36:53.2987420Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2024-12-18T01:36:53.2989675Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file 2024-12-18T01:36:53.2992067Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2024-12-18T01:36:53.2994340Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 474 <- wrt source file 2024-12-18T01:36:53.2996713Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2024-12-18T01:36:53.2999151Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 606 <- wrt source file 2024-12-18T01:36:53.3001534Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2024-12-18T01:36:53.3003872Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 758 <- wrt source file 2024-12-18T01:36:53.3006250Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2024-12-18T01:36:53.3008591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2024-12-18T01:36:53.3011010Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2024-12-18T01:36:53.3013359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2024-12-18T01:36:53.3015748Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2024-12-18T01:36:53.3017991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2024-12-18T01:36:53.3020178Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2024-12-18T01:36:53.3022449Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2024-12-18T01:36:53.3024690Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2024-12-18T01:36:53.3026893Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2024-12-18T01:36:53.3043599Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2024-12-18T01:36:53.3046151Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2024-12-18T01:36:53.3122122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2024-12-18T01:36:53.3124748Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2024-12-18T01:36:53.3128171Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2024-12-18T01:36:53.3131366Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2024-12-18T01:36:53.3208279Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2024-12-18T01:36:53.3210728Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2024-12-18T01:36:53.3216898Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2024-12-18T01:36:53.3219146Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2024-12-18T01:36:53.3224259Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2024-12-18T01:36:53.3226707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2024-12-18T01:36:53.3240996Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2024-12-18T01:36:53.3243928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2024-12-18T01:36:53.3255866Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2024-12-18T01:36:53.3258352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2024-12-18T01:36:53.3447069Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2024-12-18T01:36:53.3449915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2024-12-18T01:36:53.6025479Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2024-12-18T01:36:53.6182246Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2024-12-18T01:36:53.6185837Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2024-12-18T01:36:53.6187865Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2024-12-18T01:36:53.6194374Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2024-12-18T01:36:53.6196591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2024-12-18T01:36:53.6203249Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2024-12-18T01:36:53.6205402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2024-12-18T01:36:53.6224284Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2024-12-18T01:36:53.6227038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2024-12-18T01:36:53.6233865Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2024-12-18T01:36:53.6236470Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2024-12-18T01:36:53.6261513Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2024-12-18T01:36:53.6264153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2024-12-18T01:36:53.6269151Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2024-12-18T01:36:53.6271404Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2024-12-18T01:36:53.6284490Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2024-12-18T01:36:53.6286681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 519 <- wrt source file 2024-12-18T01:36:53.6294049Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2024-12-18T01:36:53.6296502Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 597 <- wrt source file 2024-12-18T01:36:53.6300555Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2024-12-18T01:36:53.6302629Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 679 <- wrt source file 2024-12-18T01:36:53.6307189Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2024-12-18T01:36:53.6309396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 750 <- wrt source file 2024-12-18T01:36:53.6319365Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2024-12-18T01:36:53.6321713Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 943 <- wrt source file 2024-12-18T01:36:53.6328068Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2024-12-18T01:36:53.6330403Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1263 <- wrt source file 2024-12-18T01:36:53.6337345Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2024-12-18T01:36:53.6340192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1403 <- wrt source file 2024-12-18T01:36:53.6347679Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2024-12-18T01:36:53.6350042Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1468 <- wrt source file 2024-12-18T01:36:53.6355396Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2024-12-18T01:36:53.6357730Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1547 <- wrt source file 2024-12-18T01:36:53.6364242Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2024-12-18T01:36:53.6366530Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1647 <- wrt source file 2024-12-18T01:36:53.6376107Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2024-12-18T01:36:53.6378307Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1888 <- wrt source file 2024-12-18T01:36:53.6408146Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2024-12-18T01:36:53.6410636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 548 <- wrt source file 2024-12-18T01:36:53.6413407Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2024-12-18T01:36:53.6415907Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1008 <- wrt source file 2024-12-18T01:36:53.6423127Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2024-12-18T01:36:53.6425432Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1262 <- wrt source file 2024-12-18T01:36:53.6431513Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2024-12-18T01:36:53.6434508Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2180 <- wrt source file 2024-12-18T01:36:53.6436980Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2024-12-18T01:36:53.6439299Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2622 <- wrt source file 2024-12-18T01:36:53.6441677Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2024-12-18T01:36:53.6444043Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2650 <- wrt source file 2024-12-18T01:36:53.6446555Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2024-12-18T01:36:53.6448892Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2677 <- wrt source file 2024-12-18T01:36:53.6451198Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2024-12-18T01:36:53.6453652Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2704 <- wrt source file 2024-12-18T01:36:53.6456061Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2024-12-18T01:36:53.6458439Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2735 <- wrt source file 2024-12-18T01:36:53.6460884Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2024-12-18T01:36:53.6463211Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2759 <- wrt source file 2024-12-18T01:36:53.6465570Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2024-12-18T01:36:53.6467797Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2797 <- wrt source file 2024-12-18T01:36:53.6470223Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2024-12-18T01:36:53.6473471Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2024-12-18T01:36:53.6487029Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2024-12-18T01:36:53.6490331Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2024-12-18T01:36:53.6496976Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2024-12-18T01:36:53.6499895Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2024-12-18T01:36:53.6505395Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2024-12-18T01:36:53.6508166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2024-12-18T01:36:53.6511074Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2024-12-18T01:36:53.6513347Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2024-12-18T01:36:53.6518408Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2024-12-18T01:36:53.6520702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2024-12-18T01:36:53.6538728Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2024-12-18T01:36:53.6541006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2024-12-18T01:36:54.3052154Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2024-12-18T01:36:54.3339090Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2024-12-18T01:36:54.3349591Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2024-12-18T01:36:54.3352157Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2024-12-18T01:36:54.3356559Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2024-12-18T01:36:54.3358872Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2024-12-18T01:36:54.3382846Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2024-12-18T01:36:54.3385190Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2024-12-18T01:36:54.3389366Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2024-12-18T01:36:54.3391714Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2024-12-18T01:36:54.3396532Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2024-12-18T01:36:54.3399053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2024-12-18T01:36:54.3401451Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2024-12-18T01:36:54.3403794Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2024-12-18T01:36:54.3407656Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2024-12-18T01:36:54.3410032Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2024-12-18T01:36:54.3414067Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2024-12-18T01:36:54.3416423Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2024-12-18T01:36:54.8755198Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2024-12-18T01:36:54.9039831Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2024-12-18T01:36:54.9051001Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2024-12-18T01:36:54.9053271Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2024-12-18T01:36:54.9057228Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2024-12-18T01:36:54.9059413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2024-12-18T01:36:54.9082575Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2024-12-18T01:36:54.9084896Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2024-12-18T01:36:54.9090259Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2024-12-18T01:36:54.9093162Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2024-12-18T01:36:54.9098407Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2024-12-18T01:36:54.9100928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2024-12-18T01:36:54.9106747Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2024-12-18T01:36:54.9108979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2024-12-18T01:36:54.9161715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2024-12-18T01:36:54.9163944Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2024-12-18T01:36:55.1505775Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2024-12-18T01:36:55.1562514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2024-12-18T01:36:55.1576007Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2024-12-18T01:36:55.1578224Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2024-12-18T01:36:55.2432629Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2024-12-18T01:36:55.2441976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2024-12-18T01:36:55.2444287Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2024-12-18T01:36:55.2446498Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2024-12-18T01:36:55.2483230Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2024-12-18T01:36:55.2485427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2024-12-18T01:36:55.4209261Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2024-12-18T01:36:55.4266546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2024-12-18T01:36:55.4320163Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2024-12-18T01:36:55.4322657Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2024-12-18T01:36:55.5132426Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2024-12-18T01:36:55.5134776Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2024-12-18T01:36:55.5141328Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2024-12-18T01:36:55.5154675Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2024-12-18T01:36:55.5198351Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2024-12-18T01:36:55.5201270Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2024-12-18T01:36:55.7415733Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2024-12-18T01:36:55.7472849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2024-12-18T01:36:55.7479110Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2024-12-18T01:36:55.7481536Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2024-12-18T01:36:55.7489616Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2024-12-18T01:36:55.7492142Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2024-12-18T01:36:55.7523599Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2024-12-18T01:36:55.7526008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2024-12-18T01:36:55.7528641Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2024-12-18T01:36:55.7531132Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2024-12-18T01:36:55.7537742Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2024-12-18T01:36:55.7540159Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2024-12-18T01:36:55.7562072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2024-12-18T01:36:55.7564261Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 591 <- wrt source file 2024-12-18T01:36:55.7573851Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2024-12-18T01:36:55.7575846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 948 <- wrt source file 2024-12-18T01:36:55.7932021Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2024-12-18T01:36:55.7934620Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file 2024-12-18T01:36:55.7948346Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2024-12-18T01:36:55.7950407Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1536 <- wrt source file 2024-12-18T01:36:55.7960267Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2024-12-18T01:36:55.7962410Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1658 <- wrt source file 2024-12-18T01:36:55.7971115Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2024-12-18T01:36:55.7973305Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1772 <- wrt source file 2024-12-18T01:36:55.7984750Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2024-12-18T01:36:55.7986854Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2024-12-18T01:36:55.7998909Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2024-12-18T01:36:55.8001419Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file 2024-12-18T01:36:55.8004306Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2024-12-18T01:36:55.8006910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file 2024-12-18T01:36:55.8011090Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2024-12-18T01:36:55.8013527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 86 <- wrt source file 2024-12-18T01:36:56.3849613Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2024-12-18T01:36:56.3966293Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 254 <- wrt source file 2024-12-18T01:36:56.3968920Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2024-12-18T01:36:56.3971394Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 319 <- wrt source file 2024-12-18T01:36:56.4590031Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2024-12-18T01:36:56.4595155Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 532 <- wrt source file 2024-12-18T01:36:56.5832044Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2024-12-18T01:36:56.5839465Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 653 <- wrt source file 2024-12-18T01:36:56.6055182Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2024-12-18T01:36:56.6057846Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 957 <- wrt source file 2024-12-18T01:36:56.6418914Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2024-12-18T01:36:56.6421388Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2024-12-18T01:36:56.6443223Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2024-12-18T01:36:56.6445614Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2024-12-18T01:36:56.6455494Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2024-12-18T01:36:56.6458002Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2024-12-18T01:36:56.6464558Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2024-12-18T01:36:56.6467104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 126 <- wrt source file 2024-12-18T01:36:56.6471056Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2024-12-18T01:36:56.6473259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file 2024-12-18T01:36:56.6476011Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2024-12-18T01:36:56.6478700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1418 <- wrt source file 2024-12-18T01:36:56.6481388Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2024-12-18T01:36:56.6484220Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1981 <- wrt source file 2024-12-18T01:36:56.6487488Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2024-12-18T01:36:56.6490475Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1991 <- wrt source file 2024-12-18T01:36:56.6493379Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2024-12-18T01:36:56.6496352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2026 <- wrt source file 2024-12-18T01:36:56.6499733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2024-12-18T01:36:56.6502730Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2024-12-18T01:36:56.6505353Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2024-12-18T01:36:56.6507703Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2024-12-18T01:36:56.6509682Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2024-12-18T01:36:56.6511394Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2024-12-18T01:36:56.6512760Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2024-12-18T01:36:56.6514046Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2024-12-18T01:36:56.6515525Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2024-12-18T01:36:56.6516944Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2024-12-18T01:36:56.6518298Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2024-12-18T01:36:56.6520006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 506 <- wrt source file 2024-12-18T01:36:56.6522449Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2024-12-18T01:36:56.6524702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 845 <- wrt source file 2024-12-18T01:36:56.6526762Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2024-12-18T01:36:56.6528910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 881 <- wrt source file 2024-12-18T01:36:56.6531024Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2024-12-18T01:36:56.6533266Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 924 <- wrt source file 2024-12-18T01:36:56.6535549Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2024-12-18T01:36:56.6537750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1191 <- wrt source file 2024-12-18T01:36:56.6539790Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2024-12-18T01:36:56.6541740Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1219 <- wrt source file 2024-12-18T01:36:56.6544873Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2024-12-18T01:36:56.6547066Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 360 <- wrt source file 2024-12-18T01:36:56.6561625Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2024-12-18T01:36:56.6563725Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 438 <- wrt source file 2024-12-18T01:36:56.6568250Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2024-12-18T01:36:56.6570428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 496 <- wrt source file 2024-12-18T01:36:56.6581976Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2024-12-18T01:36:56.6584149Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 552 <- wrt source file 2024-12-18T01:36:56.6589819Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2024-12-18T01:36:56.6591983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 580 <- wrt source file 2024-12-18T01:36:56.6606098Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2024-12-18T01:36:56.6608377Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file 2024-12-18T01:36:56.6613524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2024-12-18T01:36:56.6615949Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file 2024-12-18T01:36:56.6621337Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2024-12-18T01:36:56.6623895Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 214 <- wrt source file 2024-12-18T01:36:56.6626299Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2024-12-18T01:36:56.6628628Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file 2024-12-18T01:36:56.6633462Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2024-12-18T01:36:56.6635850Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file 2024-12-18T01:36:56.6639956Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2024-12-18T01:36:56.6642585Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 315 <- wrt source file 2024-12-18T01:36:56.6644317Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2024-12-18T01:36:56.6645848Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2024-12-18T01:36:56.6730184Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2024-12-18T01:36:56.6732840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 309 <- wrt source file 2024-12-18T01:36:56.6735059Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2024-12-18T01:36:56.6737298Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 411 <- wrt source file 2024-12-18T01:36:56.6739658Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2024-12-18T01:36:56.6741873Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 511 <- wrt source file 2024-12-18T01:36:56.6744013Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2024-12-18T01:36:56.6746181Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 571 <- wrt source file 2024-12-18T01:36:56.6748416Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2024-12-18T01:36:56.6750765Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 636 <- wrt source file 2024-12-18T01:36:56.6752999Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2024-12-18T01:36:56.6755153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 714 <- wrt source file 2024-12-18T01:36:56.6757387Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2024-12-18T01:36:56.6759569Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 847 <- wrt source file 2024-12-18T01:36:56.6761921Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2024-12-18T01:36:56.6764155Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 996 <- wrt source file 2024-12-18T01:36:56.6766422Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2024-12-18T01:36:56.6768778Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1152 <- wrt source file 2024-12-18T01:36:56.6771159Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2024-12-18T01:36:56.6773486Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1295 <- wrt source file 2024-12-18T01:36:56.6775878Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2024-12-18T01:36:56.6778161Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1543 <- wrt source file 2024-12-18T01:36:56.6780372Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2024-12-18T01:36:56.6782805Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1813 <- wrt source file 2024-12-18T01:36:56.6785528Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2024-12-18T01:36:56.6788199Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1829 <- wrt source file 2024-12-18T01:36:56.6790934Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2024-12-18T01:36:56.6793352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1973 <- wrt source file 2024-12-18T01:36:56.6795578Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2024-12-18T01:36:56.6797760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 330 <- wrt source file 2024-12-18T01:36:56.6800032Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0 2024-12-18T01:36:56.6802155Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 21 <- wrt source file 2024-12-18T01:36:56.6804383Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0 2024-12-18T01:36:56.6806938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 279 <- wrt source file 2024-12-18T01:36:56.6809757Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2024-12-18T01:36:56.6812195Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 596 <- wrt source file 2024-12-18T01:36:56.6814370Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0 2024-12-18T01:36:56.6816687Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 338 <- wrt source file 2024-12-18T01:36:56.6819354Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2024-12-18T01:36:56.6821695Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file 2024-12-18T01:36:56.6823993Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0 2024-12-18T01:36:56.6826305Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 619 <- wrt source file 2024-12-18T01:36:56.6828807Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2024-12-18T01:36:56.6831249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 740 <- wrt source file 2024-12-18T01:36:56.6833821Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0 2024-12-18T01:36:56.6836326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 829 <- wrt source file 2024-12-18T01:36:56.6838804Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2024-12-18T01:36:56.6841372Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4571 <- wrt source file 2024-12-18T01:36:56.6844132Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2024-12-18T01:36:56.6846841Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4585 <- wrt source file 2024-12-18T01:36:56.6849585Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2024-12-18T01:36:56.6852274Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4615 <- wrt source file 2024-12-18T01:36:56.6855021Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2024-12-18T01:36:56.6857574Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 192 <- wrt source file 2024-12-18T01:36:56.6860093Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2024-12-18T01:36:56.6862830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 299 <- wrt source file 2024-12-18T01:36:56.6865927Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2024-12-18T01:36:56.6868974Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2024-12-18T01:36:56.6872081Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2024-12-18T01:36:56.6874641Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 249 <- wrt source file 2024-12-18T01:36:56.6876912Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2024-12-18T01:36:56.6879075Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 292 <- wrt source file 2024-12-18T01:36:56.6881316Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2024-12-18T01:36:56.6883499Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 334 <- wrt source file 2024-12-18T01:36:56.6885745Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2024-12-18T01:36:56.6887897Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 364 <- wrt source file 2024-12-18T01:36:56.6890090Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2024-12-18T01:36:56.6892214Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 399 <- wrt source file 2024-12-18T01:36:56.6894393Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2024-12-18T01:36:56.6896562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 434 <- wrt source file 2024-12-18T01:36:56.6898942Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2024-12-18T01:36:56.6901092Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 471 <- wrt source file 2024-12-18T01:36:56.6903222Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2024-12-18T01:36:56.6905383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 847 <- wrt source file 2024-12-18T01:36:56.6907680Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2024-12-18T01:36:56.6909818Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 960 <- wrt source file 2024-12-18T01:36:56.6911876Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2024-12-18T01:36:56.6914235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2024-12-18T01:36:56.6917009Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2024-12-18T01:36:56.6919878Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file 2024-12-18T01:36:56.6922809Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2024-12-18T01:36:56.6925475Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file 2024-12-18T01:36:56.6928029Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0 2024-12-18T01:36:56.6930564Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 548 <- wrt source file 2024-12-18T01:36:56.6932991Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2024-12-18T01:36:56.6935398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 750 <- wrt source file 2024-12-18T01:36:56.6937919Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2024-12-18T01:36:56.6940165Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2024-12-18T01:36:56.6942273Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0 2024-12-18T01:36:56.6944696Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 713 <- wrt source file 2024-12-18T01:36:56.7341392Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2024-12-18T01:36:56.7344006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file 2024-12-18T01:36:56.7346398Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2024-12-18T01:36:56.7348676Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file 2024-12-18T01:36:56.7350960Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0 2024-12-18T01:36:56.7353191Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::random_split:0, line 445 <- wrt source file 2024-12-18T01:36:56.7355466Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::random_split:0 2024-12-18T01:36:56.7357710Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::Sampler:0, line 43 <- wrt source file 2024-12-18T01:36:56.7359899Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::Sampler:0 2024-12-18T01:36:56.7362200Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 241 <- wrt source file 2024-12-18T01:36:56.7364702Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2024-12-18T01:36:56.7367045Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 304 <- wrt source file 2024-12-18T01:36:56.7369532Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2024-12-18T01:36:56.7371842Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2024-12-18T01:36:56.7374283Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2024-12-18T01:36:56.7376600Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2024-12-18T01:36:56.7378882Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0 2024-12-18T01:36:56.7381268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2024-12-18T01:36:56.7383727Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2024-12-18T01:36:56.7386246Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 96 <- wrt source file 2024-12-18T01:36:56.7388795Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2024-12-18T01:36:56.7391278Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 263 <- wrt source file 2024-12-18T01:36:56.7393821Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2024-12-18T01:36:56.7396587Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 51 <- wrt source file 2024-12-18T01:36:56.7399589Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2024-12-18T01:36:56.7402392Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 197 <- wrt source file 2024-12-18T01:36:56.7405480Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2024-12-18T01:36:56.7408350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 87 <- wrt source file 2024-12-18T01:36:56.7411334Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2024-12-18T01:36:56.7414198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 48 <- wrt source file 2024-12-18T01:36:56.7417077Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2024-12-18T01:36:56.7419864Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 98 <- wrt source file 2024-12-18T01:36:56.7422682Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2024-12-18T01:36:56.7425393Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 317 <- wrt source file 2024-12-18T01:36:56.7428126Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2024-12-18T01:36:56.7431034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 403 <- wrt source file 2024-12-18T01:36:56.7434051Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2024-12-18T01:36:56.7437038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 613 <- wrt source file 2024-12-18T01:36:56.7439996Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2024-12-18T01:36:56.7442921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 681 <- wrt source file 2024-12-18T01:36:56.7445765Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2024-12-18T01:36:56.7448674Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2024-12-18T01:36:56.7451726Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2024-12-18T01:36:56.7454635Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 34 <- wrt source file 2024-12-18T01:36:56.7457652Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2024-12-18T01:36:56.7460488Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 62 <- wrt source file 2024-12-18T01:36:56.7463342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2024-12-18T01:36:56.7466135Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 122 <- wrt source file 2024-12-18T01:36:56.7469014Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2024-12-18T01:36:56.7471832Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 189 <- wrt source file 2024-12-18T01:36:56.7474679Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2024-12-18T01:36:56.7477527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 36 <- wrt source file 2024-12-18T01:36:56.7480357Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2024-12-18T01:36:56.7483275Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 24 <- wrt source file 2024-12-18T01:36:56.7486330Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2024-12-18T01:36:56.7489281Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file 2024-12-18T01:36:56.7492322Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2024-12-18T01:36:56.7495145Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file 2024-12-18T01:36:56.7498048Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2024-12-18T01:36:56.7500888Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 33 <- wrt source file 2024-12-18T01:36:56.7504179Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2024-12-18T01:36:56.7507044Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 28 <- wrt source file 2024-12-18T01:36:56.7509935Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2024-12-18T01:36:56.7512723Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 72 <- wrt source file 2024-12-18T01:36:56.7515529Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2024-12-18T01:36:56.7518374Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 28 <- wrt source file 2024-12-18T01:36:56.7521238Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2024-12-18T01:36:56.7524054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file 2024-12-18T01:36:56.7526955Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2024-12-18T01:36:56.7529701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 36 <- wrt source file 2024-12-18T01:36:56.7532469Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2024-12-18T01:36:56.7535146Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2024-12-18T01:36:56.7537842Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2024-12-18T01:36:56.7540376Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2024-12-18T01:36:56.7542906Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2024-12-18T01:36:56.7545418Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2024-12-18T01:36:56.7548041Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2024-12-18T01:36:56.7551430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2024-12-18T01:36:56.7554105Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2024-12-18T01:36:56.7556802Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2024-12-18T01:36:56.7559489Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2024-12-18T01:36:56.7562111Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2024-12-18T01:36:56.7564807Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2024-12-18T01:36:56.7567427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2024-12-18T01:36:56.7570158Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2024-12-18T01:36:56.7572778Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2024-12-18T01:36:56.7575445Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2024-12-18T01:36:56.7578128Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2024-12-18T01:36:56.7580862Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2024-12-18T01:36:56.7583583Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2024-12-18T01:36:56.7586372Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2024-12-18T01:36:56.7589054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2024-12-18T01:36:56.7591722Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2024-12-18T01:36:56.7594344Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2024-12-18T01:36:56.7597048Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2024-12-18T01:36:56.7599779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2024-12-18T01:36:56.7602427Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2024-12-18T01:36:56.7605284Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2024-12-18T01:36:56.7608041Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2024-12-18T01:36:56.7610799Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2024-12-18T01:36:56.7613495Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2024-12-18T01:36:56.7616393Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2024-12-18T01:36:56.7619511Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2024-12-18T01:36:56.7622512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2024-12-18T01:36:56.7625598Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2024-12-18T01:36:56.7628521Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2024-12-18T01:36:56.7631353Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2024-12-18T01:36:56.7634026Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2024-12-18T01:36:56.7636702Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2024-12-18T01:36:56.7638070Z ============ 2024-12-18T01:36:56.7638575Z Finished doctests 2024-12-18T01:36:56.7638957Z 338 / 705 passed 2024-12-18T01:36:56.7639360Z  2024-12-18T01:36:56.7639867Z === Found 105 parse-time warnings === 2024-12-18T01:36:56.7640582Z --- Parse Warning: 1 / 105 --- 2024-12-18T01:36:56.7642554Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1496. 2024-12-18T01:36:56.7644765Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.7645572Z 2024-12-18T01:36:56.7645965Z dim_order(ambiguity_check=False) -> tuple 2024-12-18T01:36:56.7646515Z 2024-12-18T01:36:56.7647115Z Returns the uniquely determined tuple of int describing the dim order or 2024-12-18T01:36:56.7647929Z physical layout of :attr:`self`. 2024-12-18T01:36:56.7648411Z 2024-12-18T01:36:56.7648936Z The dim order represents how dimensions are laid out in memory, 2024-12-18T01:36:56.7649780Z starting from the outermost to the innermost dimension. 2024-12-18T01:36:56.7650417Z 2024-12-18T01:36:56.7650923Z Note that the dim order may not always be uniquely determined. 2024-12-18T01:36:56.7652137Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2024-12-18T01:36:56.7653758Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2024-12-18T01:36:56.7655137Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2024-12-18T01:36:56.7656389Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2024-12-18T01:36:56.7657378Z Otherwise, it will raise TypeError. 2024-12-18T01:36:56.7657902Z 2024-12-18T01:36:56.7658210Z Args: 2024-12-18T01:36:56.7658990Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2024-12-18T01:36:56.7659968Z 2024-12-18T01:36:56.7660350Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2024-12-18T01:36:56.7660886Z (0, 1, 2, 3) 2024-12-18T01:36:56.7661381Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2024-12-18T01:36:56.7661998Z (0, 2, 1, 3) 2024-12-18T01:36:56.7662594Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2024-12-18T01:36:56.7663307Z (0, 2, 3, 1) 2024-12-18T01:36:56.7663726Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2024-12-18T01:36:56.7664241Z (0, 1, 2, 3) 2024-12-18T01:36:56.7664618Z >>> try: 2024-12-18T01:36:56.7665135Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2024-12-18T01:36:56.7665863Z ... except RuntimeError as e: 2024-12-18T01:36:56.7666377Z ... print(e) 2024-12-18T01:36:56.7667190Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2024-12-18T01:36:56.7668136Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2024-12-18T01:36:56.7668881Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2024-12-18T01:36:56.7669672Z ... ) # It can be mapped to contiguous format 2024-12-18T01:36:56.7670273Z (0, 1, 2, 3) 2024-12-18T01:36:56.7670642Z >>> try: 2024-12-18T01:36:56.7671175Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2024-12-18T01:36:56.7671873Z ... except TypeError as e: 2024-12-18T01:36:56.7672368Z ... print(e) 2024-12-18T01:36:56.7673040Z The ambiguity_check argument must be a bool or a list of memory formats. 2024-12-18T01:36:56.7673820Z .. warning:: 2024-12-18T01:36:56.7674386Z The dim_order tensor API is experimental and subject to change. 2024-12-18T01:36:56.7675073Z 2024-12-18T01:36:56.7675836Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.7676646Z 2024-12-18T01:36:56.7676981Z warnings.warn(msg) 2024-12-18T01:36:56.7677401Z 2024-12-18T01:36:56.7677947Z --- Parse Warning: 2 / 105 --- 2024-12-18T01:36:56.7679876Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=431. 2024-12-18T01:36:56.7682084Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.7683181Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2024-12-18T01:36:56.7683940Z 2024-12-18T01:36:56.7684422Z This is helpful when you want to visualize data over some 2024-12-18T01:36:56.7685201Z range of inputs. See below for a plotting example. 2024-12-18T01:36:56.7685812Z 2024-12-18T01:36:56.7686281Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2024-12-18T01:36:56.7687080Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2024-12-18T01:36:56.7687953Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2024-12-18T01:36:56.7688763Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2024-12-18T01:36:56.7689580Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2024-12-18T01:36:56.7690279Z to the result shape. 2024-12-18T01:36:56.7690745Z 2024-12-18T01:36:56.7691066Z .. note:: 2024-12-18T01:36:56.7691591Z 0D inputs are treated equivalently to 1D inputs of a 2024-12-18T01:36:56.7692239Z single element. 2024-12-18T01:36:56.7692693Z 2024-12-18T01:36:56.7693017Z .. warning:: 2024-12-18T01:36:56.7693609Z `torch.meshgrid(*tensors)` currently has the same behavior 2024-12-18T01:36:56.7694422Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2024-12-18T01:36:56.7695134Z 2024-12-18T01:36:56.7695571Z In the future `torch.meshgrid` will transition to 2024-12-18T01:36:56.7696226Z `indexing='xy'` as the default. 2024-12-18T01:36:56.7696740Z 2024-12-18T01:36:56.7697248Z https://github.com/pytorch/pytorch/issues/50276 tracks 2024-12-18T01:36:56.7698216Z this issue with the goal of migrating to NumPy's behavior. 2024-12-18T01:36:56.7698874Z 2024-12-18T01:36:56.7699204Z .. seealso:: 2024-12-18T01:36:56.7699593Z 2024-12-18T01:36:56.7700061Z :func:`torch.cartesian_prod` has the same effect but it 2024-12-18T01:36:56.7700785Z collects the data in a tensor of vectors. 2024-12-18T01:36:56.7701338Z 2024-12-18T01:36:56.7701659Z Args: 2024-12-18T01:36:56.7702421Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2024-12-18T01:36:56.7703643Z treated as tensors of size :math:`(1,)` automatically 2024-12-18T01:36:56.7704275Z 2024-12-18T01:36:56.7704769Z indexing: (str, optional): the indexing mode, either "xy" 2024-12-18T01:36:56.7705572Z or "ij", defaults to "ij". See warning for future changes. 2024-12-18T01:36:56.7706197Z 2024-12-18T01:36:56.7706704Z If "xy" is selected, the first dimension corresponds 2024-12-18T01:36:56.7707452Z to the cardinality of the second input and the second 2024-12-18T01:36:56.7708236Z dimension corresponds to the cardinality of the first 2024-12-18T01:36:56.7708890Z input. 2024-12-18T01:36:56.7709291Z 2024-12-18T01:36:56.7709725Z If "ij" is selected, the dimensions are in the same 2024-12-18T01:36:56.7710414Z order as the cardinality of the inputs. 2024-12-18T01:36:56.7710978Z 2024-12-18T01:36:56.7711306Z Returns: 2024-12-18T01:36:56.7711861Z seq (sequence of Tensors): If the input has :math:`N` 2024-12-18T01:36:56.7712613Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2024-12-18T01:36:56.7713398Z output will also have :math:`N` tensors, where each tensor 2024-12-18T01:36:56.7714133Z is of shape :math:`(S_0, ..., S_{N-1})`. 2024-12-18T01:36:56.7714681Z 2024-12-18T01:36:56.7715011Z Example:: 2024-12-18T01:36:56.7715404Z 2024-12-18T01:36:56.7715884Z >>> x = torch.tensor([1, 2, 3]) 2024-12-18T01:36:56.7716458Z >>> y = torch.tensor([4, 5, 6]) 2024-12-18T01:36:56.7716983Z 2024-12-18T01:36:56.7717481Z Observe the element-wise pairings across the grid, (1, 4), 2024-12-18T01:36:56.7718245Z (1, 5), ..., (3, 6). This is the same thing as the 2024-12-18T01:36:56.7718844Z cartesian product. 2024-12-18T01:36:56.7719456Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2024-12-18T01:36:56.7720088Z >>> grid_x 2024-12-18T01:36:56.7720529Z tensor([[1, 1, 1], 2024-12-18T01:36:56.7720981Z [2, 2, 2], 2024-12-18T01:36:56.7721461Z [3, 3, 3]]) 2024-12-18T01:36:56.7721926Z >>> grid_y 2024-12-18T01:36:56.7722362Z tensor([[4, 5, 6], 2024-12-18T01:36:56.7722819Z [4, 5, 6], 2024-12-18T01:36:56.7723287Z [4, 5, 6]]) 2024-12-18T01:36:56.7723741Z 2024-12-18T01:36:56.7724205Z This correspondence can be seen when these grids are 2024-12-18T01:36:56.7724870Z stacked properly. 2024-12-18T01:36:56.7725545Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2024-12-18T01:36:56.7726306Z ... torch.cartesian_prod(x, y)) 2024-12-18T01:36:56.7726871Z True 2024-12-18T01:36:56.7727249Z 2024-12-18T01:36:56.7727724Z `torch.meshgrid` is commonly used to produce a grid for 2024-12-18T01:36:56.7728473Z plotting. 2024-12-18T01:36:56.7728960Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2024-12-18T01:36:56.7729598Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2024-12-18T01:36:56.7730232Z >>> import matplotlib.pyplot as plt 2024-12-18T01:36:56.7730855Z >>> xs = torch.linspace(-5, 5, steps=100) 2024-12-18T01:36:56.7731478Z >>> ys = torch.linspace(-5, 5, steps=100) 2024-12-18T01:36:56.7732100Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2024-12-18T01:36:56.7732745Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2024-12-18T01:36:56.7733362Z >>> ax = plt.axes(projection='3d') 2024-12-18T01:36:56.7734012Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2024-12-18T01:36:56.7734676Z >>> plt.show() 2024-12-18T01:36:56.7735104Z 2024-12-18T01:36:56.7735493Z .. image:: ../_static/img/meshgrid.png 2024-12-18T01:36:56.7736054Z :width: 512 2024-12-18T01:36:56.7736478Z 2024-12-18T01:36:56.7736824Z 2024-12-18T01:36:56.7737460Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.7738260Z 2024-12-18T01:36:56.7738644Z warnings.warn(msg) 2024-12-18T01:36:56.7739069Z 2024-12-18T01:36:56.7739636Z --- Parse Warning: 3 / 105 --- 2024-12-18T01:36:56.7741579Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=820. 2024-12-18T01:36:56.7743784Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.7745144Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor] 2024-12-18T01:36:56.7746225Z 2024-12-18T01:36:56.7746671Z Returns the unique elements of the input tensor. 2024-12-18T01:36:56.7747270Z 2024-12-18T01:36:56.7747959Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2024-12-18T01:36:56.7749053Z this function also eliminates non-consecutive duplicate values. 2024-12-18T01:36:56.7749772Z 2024-12-18T01:36:56.7750353Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2024-12-18T01:36:56.7751479Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2024-12-18T01:36:56.7752706Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2024-12-18T01:36:56.7753713Z :func:`torch.unique_consecutive` which avoids the sorting. 2024-12-18T01:36:56.7754374Z 2024-12-18T01:36:56.7754703Z Args: 2024-12-18T01:36:56.7755117Z input (Tensor): the input tensor 2024-12-18T01:36:56.7755947Z sorted (bool): Whether to sort the unique elements in ascending order 2024-12-18T01:36:56.7756706Z before returning as output. 2024-12-18T01:36:56.7757452Z return_inverse (bool): Whether to also return the indices for where 2024-12-18T01:36:56.7758413Z elements in the original input ended up in the returned unique list. 2024-12-18T01:36:56.7759399Z return_counts (bool): Whether to also return the counts for each unique 2024-12-18T01:36:56.7760149Z element. 2024-12-18T01:36:56.7760759Z dim (int, optional): the dimension to operate upon. If ``None``, the 2024-12-18T01:36:56.7761676Z unique of the flattened input is returned. Otherwise, each of the 2024-12-18T01:36:56.7762595Z tensors indexed by the given dimension is treated as one of the 2024-12-18T01:36:56.7763539Z elements to apply the unique operation upon. See examples for more 2024-12-18T01:36:56.7764356Z details. Default: ``None`` 2024-12-18T01:36:56.7764864Z 2024-12-18T01:36:56.7765174Z Returns: 2024-12-18T01:36:56.7765904Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2024-12-18T01:36:56.7766769Z 2024-12-18T01:36:56.7767288Z - **output** (*Tensor*): the output list of unique scalar elements. 2024-12-18T01:36:56.7768078Z - **inverse_indices** (*Tensor*): (optional) if 2024-12-18T01:36:56.7768816Z :attr:`return_inverse` is True, there will be an additional 2024-12-18T01:36:56.7769698Z returned tensor (same shape as input) representing the indices 2024-12-18T01:36:56.7770604Z for where elements in the original input map to in the output; 2024-12-18T01:36:56.7771515Z otherwise, this function will only return a single tensor. 2024-12-18T01:36:56.7772264Z - **counts** (*Tensor*): (optional) if 2024-12-18T01:36:56.7772966Z :attr:`return_counts` is True, there will be an additional 2024-12-18T01:36:56.7773792Z returned tensor (same shape as output or output.size(dim), 2024-12-18T01:36:56.7774655Z if dim was specified) representing the number of occurrences 2024-12-18T01:36:56.7775420Z for each unique value or tensor. 2024-12-18T01:36:56.7775959Z 2024-12-18T01:36:56.7776298Z Example:: 2024-12-18T01:36:56.7776654Z 2024-12-18T01:36:56.7777210Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2024-12-18T01:36:56.7777954Z >>> output 2024-12-18T01:36:56.7778373Z tensor([1, 2, 3]) 2024-12-18T01:36:56.7778815Z 2024-12-18T01:36:56.7779200Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:36:56.7780029Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:36:56.7780841Z >>> output 2024-12-18T01:36:56.7781257Z tensor([1, 2, 3]) 2024-12-18T01:36:56.7781721Z >>> inverse_indices 2024-12-18T01:36:56.7782175Z tensor([0, 2, 1, 2]) 2024-12-18T01:36:56.7782628Z 2024-12-18T01:36:56.7783029Z >>> output, inverse_indices = torch.unique( 2024-12-18T01:36:56.7783862Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2024-12-18T01:36:56.7784622Z >>> output 2024-12-18T01:36:56.7785016Z tensor([1, 2, 3]) 2024-12-18T01:36:56.7785479Z >>> inverse_indices 2024-12-18T01:36:56.7785946Z tensor([[0, 2], 2024-12-18T01:36:56.7786383Z [1, 2]]) 2024-12-18T01:36:56.7786802Z 2024-12-18T01:36:56.7787133Z >>> a = torch.tensor([ 2024-12-18T01:36:56.7787608Z ... [ 2024-12-18T01:36:56.7788010Z ... [1, 1, 0, 0], 2024-12-18T01:36:56.7788498Z ... [1, 1, 0, 0], 2024-12-18T01:36:56.7788985Z ... [0, 0, 1, 1], 2024-12-18T01:36:56.7789444Z ... ], 2024-12-18T01:36:56.7789831Z ... [ 2024-12-18T01:36:56.7790231Z ... [0, 0, 1, 1], 2024-12-18T01:36:56.7790716Z ... [0, 0, 1, 1], 2024-12-18T01:36:56.7791198Z ... [1, 1, 1, 1], 2024-12-18T01:36:56.7791654Z ... ], 2024-12-18T01:36:56.7792046Z ... [ 2024-12-18T01:36:56.7792442Z ... [1, 1, 0, 0], 2024-12-18T01:36:56.7792923Z ... [1, 1, 0, 0], 2024-12-18T01:36:56.7793408Z ... [0, 0, 1, 1], 2024-12-18T01:36:56.7793866Z ... ], 2024-12-18T01:36:56.7794253Z ... ]) 2024-12-18T01:36:56.7794617Z 2024-12-18T01:36:56.7795165Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2024-12-18T01:36:56.7796180Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2024-12-18T01:36:56.7797019Z >>> # each other, so one of them will be removed. 2024-12-18T01:36:56.7797621Z >>> (a[0, :, :] == a[2, :, :]).all() 2024-12-18T01:36:56.7798279Z tensor(True) 2024-12-18T01:36:56.7798764Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2024-12-18T01:36:56.7799337Z >>> a_unique_dim0 2024-12-18T01:36:56.7799786Z tensor([[[0, 0, 1, 1], 2024-12-18T01:36:56.7800265Z [0, 0, 1, 1], 2024-12-18T01:36:56.7800735Z [1, 1, 1, 1]], 2024-12-18T01:36:56.7801215Z [[1, 1, 0, 0], 2024-12-18T01:36:56.7801681Z [1, 1, 0, 0], 2024-12-18T01:36:56.7802139Z [0, 0, 1, 1]]]) 2024-12-18T01:36:56.7802853Z 2024-12-18T01:36:56.7803504Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2024-12-18T01:36:56.7804261Z >>> # `a_unique_dim0`: 2024-12-18T01:36:56.7804800Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2024-12-18T01:36:56.7805358Z tensor(True) 2024-12-18T01:36:56.7805842Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2024-12-18T01:36:56.7806401Z tensor(True) 2024-12-18T01:36:56.7806810Z 2024-12-18T01:36:56.7807397Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2024-12-18T01:36:56.7808269Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2024-12-18T01:36:56.7808965Z >>> # them will be removed. 2024-12-18T01:36:56.7809503Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2024-12-18T01:36:56.7810026Z tensor(True) 2024-12-18T01:36:56.7810477Z >>> torch.unique(a, dim=1) 2024-12-18T01:36:56.7810982Z tensor([[[0, 0, 1, 1], 2024-12-18T01:36:56.7811463Z [1, 1, 0, 0]], 2024-12-18T01:36:56.7811933Z [[1, 1, 1, 1], 2024-12-18T01:36:56.7812463Z [0, 0, 1, 1]], 2024-12-18T01:36:56.7812935Z [[0, 0, 1, 1], 2024-12-18T01:36:56.7813385Z [1, 1, 0, 0]]]) 2024-12-18T01:36:56.7813848Z 2024-12-18T01:36:56.7814385Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2024-12-18T01:36:56.7815224Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2024-12-18T01:36:56.7815994Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2024-12-18T01:36:56.7816693Z >>> # sub-tensors will be removed. 2024-12-18T01:36:56.7817239Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2024-12-18T01:36:56.7817757Z tensor(True) 2024-12-18T01:36:56.7818197Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2024-12-18T01:36:56.7818717Z tensor(True) 2024-12-18T01:36:56.7819160Z >>> torch.unique(a, dim=2) 2024-12-18T01:36:56.7819664Z tensor([[[0, 1], 2024-12-18T01:36:56.7820114Z [0, 1], 2024-12-18T01:36:56.7820548Z [1, 0]], 2024-12-18T01:36:56.7820990Z [[1, 0], 2024-12-18T01:36:56.7821406Z [1, 0], 2024-12-18T01:36:56.7821841Z [1, 1]], 2024-12-18T01:36:56.7822276Z [[0, 1], 2024-12-18T01:36:56.7822711Z [0, 1], 2024-12-18T01:36:56.7823143Z [1, 0]]]) 2024-12-18T01:36:56.7823563Z 2024-12-18T01:36:56.7824202Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.7825003Z 2024-12-18T01:36:56.7825349Z warnings.warn(msg) 2024-12-18T01:36:56.7825764Z 2024-12-18T01:36:56.7826315Z --- Parse Warning: 4 / 105 --- 2024-12-18T01:36:56.7828166Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560. 2024-12-18T01:36:56.7830334Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.7831154Z 2024-12-18T01:36:56.7831608Z Load a model from a github repo or a local directory. 2024-12-18T01:36:56.7832217Z 2024-12-18T01:36:56.7832791Z Note: Loading a model is the typical use case, but this can also be used to 2024-12-18T01:36:56.7833777Z for loading other objects such as tokenizers, loss functions, etc. 2024-12-18T01:36:56.7834500Z 2024-12-18T01:36:56.7834965Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2024-12-18T01:36:56.7835816Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2024-12-18T01:36:56.7836461Z ref (a tag or a branch). 2024-12-18T01:36:56.7836899Z 2024-12-18T01:36:56.7837367Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2024-12-18T01:36:56.7838081Z path to a local directory. 2024-12-18T01:36:56.7838540Z 2024-12-18T01:36:56.7838848Z Args: 2024-12-18T01:36:56.7839264Z repo_or_dir (str): If ``source`` is 'github', 2024-12-18T01:36:56.7840184Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2024-12-18T01:36:56.7841444Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2024-12-18T01:36:56.7842676Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2024-12-18T01:36:56.7843683Z If ``source`` is 'local' then it should be a path to a local directory. 2024-12-18T01:36:56.7844573Z model (str): the name of a callable (entrypoint) defined in the 2024-12-18T01:36:56.7845288Z repo/dir's ``hubconf.py``. 2024-12-18T01:36:56.7845985Z *args (optional): the corresponding args for callable ``model``. 2024-12-18T01:36:56.7846843Z source (str, optional): 'github' or 'local'. Specifies how 2024-12-18T01:36:56.7847676Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2024-12-18T01:36:56.7848524Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2024-12-18T01:36:56.7849506Z This parameter was introduced in v1.12 and helps ensuring that users 2024-12-18T01:36:56.7850335Z only run code from repos that they trust. 2024-12-18T01:36:56.7850894Z 2024-12-18T01:36:56.7851396Z - If ``False``, a prompt will ask the user whether the repo should 2024-12-18T01:36:56.7852074Z be trusted. 2024-12-18T01:36:56.7852677Z - If ``True``, the repo will be added to the trusted list and loaded 2024-12-18T01:36:56.7853441Z without requiring explicit confirmation. 2024-12-18T01:36:56.7854158Z - If ``"check"``, the repo will be checked against the list of 2024-12-18T01:36:56.7855022Z trusted repos in the cache. If it is not present in that list, the 2024-12-18T01:36:56.7855953Z behaviour will fall back onto the ``trust_repo=False`` option. 2024-12-18T01:36:56.7856814Z - If ``None``: this will raise a warning, inviting the user to set 2024-12-18T01:36:56.7857664Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2024-12-18T01:36:56.7858557Z is only present for backward compatibility and will be removed in 2024-12-18T01:36:56.7859285Z v2.0. 2024-12-18T01:36:56.7859659Z 2024-12-18T01:36:56.7860188Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2024-12-18T01:36:56.7861126Z force_reload (bool, optional): whether to force a fresh download of 2024-12-18T01:36:56.7862043Z the github repo unconditionally. Does not have any effect if 2024-12-18T01:36:56.7862797Z ``source = 'local'``. Default is ``False``. 2024-12-18T01:36:56.7863560Z verbose (bool, optional): If ``False``, mute messages about hitting 2024-12-18T01:36:56.7864496Z local caches. Note that the message about first download cannot be 2024-12-18T01:36:56.7865388Z muted. Does not have any effect if ``source = 'local'``. 2024-12-18T01:36:56.7866051Z Default is ``True``. 2024-12-18T01:36:56.7866892Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2024-12-18T01:36:56.7868131Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2024-12-18T01:36:56.7869325Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2024-12-18T01:36:56.7870328Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2024-12-18T01:36:56.7871211Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2024-12-18T01:36:56.7871941Z 2024-12-18T01:36:56.7872308Z Returns: 2024-12-18T01:36:56.7872839Z The output of the ``model`` callable when called with the given 2024-12-18T01:36:56.7873542Z ``*args`` and ``**kwargs``. 2024-12-18T01:36:56.7874005Z 2024-12-18T01:36:56.7874326Z Example: 2024-12-18T01:36:56.7874758Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:36:56.7875345Z >>> # from a github repo 2024-12-18T01:36:56.7875909Z >>> repo = "pytorch/vision" 2024-12-18T01:36:56.7876407Z >>> model = torch.hub.load( 2024-12-18T01:36:56.7877106Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2024-12-18T01:36:56.7877770Z ... ) 2024-12-18T01:36:56.7878144Z >>> # from a local directory 2024-12-18T01:36:56.7878704Z >>> path = "/some/local/path/pytorch/vision" 2024-12-18T01:36:56.7879270Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.7879995Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2024-12-18T01:36:56.7880779Z 2024-12-18T01:36:56.7881407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.7882246Z 2024-12-18T01:36:56.7882580Z warnings.warn(msg) 2024-12-18T01:36:56.7882995Z 2024-12-18T01:36:56.7883520Z --- Parse Warning: 5 / 105 --- 2024-12-18T01:36:56.7885479Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=687. 2024-12-18T01:36:56.7887696Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.7888630Z Download object at the given URL to a local path. 2024-12-18T01:36:56.7889214Z 2024-12-18T01:36:56.7889534Z Args: 2024-12-18T01:36:56.7889960Z url (str): URL of the object to download 2024-12-18T01:36:56.7890780Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2024-12-18T01:36:56.7891994Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2024-12-18T01:36:56.7892946Z Default: None 2024-12-18T01:36:56.7893689Z progress (bool, optional): whether or not to display a progress bar to stderr 2024-12-18T01:36:56.7894503Z Default: True 2024-12-18T01:36:56.7894947Z 2024-12-18T01:36:56.7895282Z Example: 2024-12-18T01:36:56.7895730Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:36:56.7896356Z >>> # xdoctest: +REQUIRES(POSIX) 2024-12-18T01:36:56.7896939Z >>> torch.hub.download_url_to_file( 2024-12-18T01:36:56.7897720Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2024-12-18T01:36:56.7898679Z ... "/tmp/temporary_file", 2024-12-18T01:36:56.7899184Z ... ) 2024-12-18T01:36:56.7899560Z 2024-12-18T01:36:56.7899889Z 2024-12-18T01:36:56.7900523Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.7901316Z 2024-12-18T01:36:56.7901745Z warnings.warn(msg) 2024-12-18T01:36:56.7902167Z 2024-12-18T01:36:56.7902899Z --- Parse Warning: 6 / 105 --- 2024-12-18T01:36:56.7904863Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=812. 2024-12-18T01:36:56.7907112Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.7908045Z Loads the Torch serialized object at the given URL. 2024-12-18T01:36:56.7908619Z 2024-12-18T01:36:56.7909112Z If downloaded file is a zip file, it will be automatically 2024-12-18T01:36:56.7909778Z decompressed. 2024-12-18T01:36:56.7910171Z 2024-12-18T01:36:56.7910721Z If the object is already present in `model_dir`, it's deserialized and 2024-12-18T01:36:56.7911501Z returned. 2024-12-18T01:36:56.7912099Z The default value of ``model_dir`` is ``/checkpoints`` where 2024-12-18T01:36:56.7913012Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2024-12-18T01:36:56.7913693Z 2024-12-18T01:36:56.7914011Z Args: 2024-12-18T01:36:56.7914417Z url (str): URL of the object to download 2024-12-18T01:36:56.7915210Z model_dir (str, optional): directory in which to save the object 2024-12-18T01:36:56.7916461Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2024-12-18T01:36:56.7917741Z progress (bool, optional): whether or not to display a progress bar to stderr. 2024-12-18T01:36:56.7918563Z Default: True 2024-12-18T01:36:56.7952317Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2024-12-18T01:36:56.7953693Z ``filename-.ext`` where ```` is the first eight or more 2024-12-18T01:36:56.7954854Z digits of the SHA256 hash of the contents of the file. The hash is used to 2024-12-18T01:36:56.7955880Z ensure unique names and to verify the contents of the file. 2024-12-18T01:36:56.7956582Z Default: False 2024-12-18T01:36:56.7957502Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2024-12-18T01:36:56.7958875Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2024-12-18T01:36:56.7960122Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2024-12-18T01:36:56.7960943Z 2024-12-18T01:36:56.7961277Z Example: 2024-12-18T01:36:56.7961740Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2024-12-18T01:36:56.7962424Z >>> state_dict = torch.hub.load_state_dict_from_url( 2024-12-18T01:36:56.7963273Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2024-12-18T01:36:56.7964015Z ... ) 2024-12-18T01:36:56.7964385Z 2024-12-18T01:36:56.7964710Z 2024-12-18T01:36:56.7965333Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.7966136Z 2024-12-18T01:36:56.7966494Z warnings.warn(msg) 2024-12-18T01:36:56.7966926Z 2024-12-18T01:36:56.7967496Z --- Parse Warning: 7 / 105 --- 2024-12-18T01:36:56.7969464Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=360. 2024-12-18T01:36:56.7971662Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:56.7972747Z Registers the function implementation as the fallback for the given key. 2024-12-18T01:36:56.7973529Z 2024-12-18T01:36:56.7974102Z This function only works for a library with global namespace ("_"). 2024-12-18T01:36:56.7974900Z 2024-12-18T01:36:56.7975220Z Args: 2024-12-18T01:36:56.7975947Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2024-12-18T01:36:56.7976842Z to register a fallthrough. 2024-12-18T01:36:56.7977803Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2024-12-18T01:36:56.7978870Z the dispatch key that the library was created with. 2024-12-18T01:36:56.7980024Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2024-12-18T01:36:56.7981453Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2024-12-18T01:36:56.7982422Z 2024-12-18T01:36:56.7982782Z Example:: 2024-12-18T01:36:56.7983229Z >>> my_lib = Library("_", "IMPL") 2024-12-18T01:36:56.7983849Z >>> def fallback_kernel(op, *args, **kwargs): 2024-12-18T01:36:56.7984484Z >>> # Handle all autocast ops generically 2024-12-18T01:36:56.7985058Z >>> # ... 2024-12-18T01:36:56.7985635Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:36:56.7986229Z 2024-12-18T01:36:56.7987570Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2024-12-18T01:36:56.7989045Z 2024-12-18T01:36:56.7989438Z my_lib.fallback(fallback_kernel, "Autocast") 2024-12-18T01:36:56.7990008Z ^ 2024-12-18T01:36:56.7990359Z warnings.warn(msg) 2024-12-18T01:36:56.7990789Z 2024-12-18T01:36:56.7991275Z --- Parse Warning: 8 / 105 --- 2024-12-18T01:36:56.7993240Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=725. 2024-12-18T01:36:56.7995424Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:56.7996555Z Register a FakeTensor implementation ("fake impl") for this operator. 2024-12-18T01:36:56.7997316Z 2024-12-18T01:36:56.7997813Z Also sometimes known as a "meta kernel", "abstract impl". 2024-12-18T01:36:56.7998605Z 2024-12-18T01:36:56.7999229Z An "FakeTensor implementation" specifies the behavior of this operator on 2024-12-18T01:36:56.8000261Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2024-12-18T01:36:56.8001286Z certain properties (sizes/strides/storage_offset/device), it specifies 2024-12-18T01:36:56.8002169Z what the properties of the output Tensors are. 2024-12-18T01:36:56.8003077Z 2024-12-18T01:36:56.8003674Z The FakeTensor implementation has the same signature as the operator. 2024-12-18T01:36:56.8004666Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2024-12-18T01:36:56.8005640Z implementation, assume that all Tensor inputs to the operator are 2024-12-18T01:36:56.8006592Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2024-12-18T01:36:56.8007520Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2024-12-18T01:36:56.8008481Z The FakeTensor implementation must consist of only PyTorch operations 2024-12-18T01:36:56.8009445Z (and may not directly access the storage or data of any input or 2024-12-18T01:36:56.8010164Z intermediate Tensors). 2024-12-18T01:36:56.8010630Z 2024-12-18T01:36:56.8011075Z This API may be used as a decorator (see examples). 2024-12-18T01:36:56.8011670Z 2024-12-18T01:36:56.8012098Z For a detailed guide on custom ops, please see 2024-12-18T01:36:56.8012947Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2024-12-18T01:36:56.8013816Z 2024-12-18T01:36:56.8014150Z Examples: 2024-12-18T01:36:56.8014524Z >>> import torch 2024-12-18T01:36:56.8014989Z >>> import numpy as np 2024-12-18T01:36:56.8015509Z >>> from torch import Tensor 2024-12-18T01:36:56.8016028Z >>> 2024-12-18T01:36:56.8016576Z >>> # Example 1: an operator without data-dependent output shape 2024-12-18T01:36:56.8017473Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2024-12-18T01:36:56.8018431Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2024-12-18T01:36:56.8019352Z >>> raise NotImplementedError("Implementation goes here") 2024-12-18T01:36:56.8020077Z >>> 2024-12-18T01:36:56.8020584Z >>> @torch.library.register_fake("mylib::custom_linear") 2024-12-18T01:36:56.8021243Z >>> def _(x, weight, bias): 2024-12-18T01:36:56.8021772Z >>> assert x.dim() == 2 2024-12-18T01:36:56.8022313Z >>> assert weight.dim() == 2 2024-12-18T01:36:56.8022873Z >>> assert bias.dim() == 1 2024-12-18T01:36:56.8023458Z >>> assert x.shape[1] == weight.shape[1] 2024-12-18T01:36:56.8024127Z >>> assert weight.shape[0] == bias.shape[0] 2024-12-18T01:36:56.8024756Z >>> assert x.device == weight.device 2024-12-18T01:36:56.8025300Z >>> 2024-12-18T01:36:56.8025701Z >>> return (x @ weight.t()) + bias 2024-12-18T01:36:56.8026231Z >>> 2024-12-18T01:36:56.8026735Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2024-12-18T01:36:56.8027413Z >>> x = torch.randn(2, 3) 2024-12-18T01:36:56.8027954Z >>> w = torch.randn(3, 3) 2024-12-18T01:36:56.8028484Z >>> b = torch.randn(3) 2024-12-18T01:36:56.8029109Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2024-12-18T01:36:56.8029684Z >>> 2024-12-18T01:36:56.8030075Z >>> assert y.shape == (2, 3) 2024-12-18T01:36:56.8030576Z >>> 2024-12-18T01:36:56.8031089Z >>> # Example 2: an operator with data-dependent output shape 2024-12-18T01:36:56.8031976Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2024-12-18T01:36:56.8032782Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2024-12-18T01:36:56.8033364Z >>> x_np = x.numpy(force=True) 2024-12-18T01:36:56.8033964Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2024-12-18T01:36:56.8034612Z >>> return torch.tensor(res, device=x.device) 2024-12-18T01:36:56.8035185Z >>> 2024-12-18T01:36:56.8035771Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2024-12-18T01:36:56.8036419Z >>> def _(x): 2024-12-18T01:36:56.8036953Z >>> # Number of nonzero-elements is data-dependent. 2024-12-18T01:36:56.8037669Z >>> # Since we cannot peek at the data in an fake impl, 2024-12-18T01:36:56.8038400Z >>> # we use the ctx object to construct a new symint that 2024-12-18T01:36:56.8039099Z >>> # represents the data-dependent size. 2024-12-18T01:36:56.8039704Z >>> ctx = torch.library.get_ctx() 2024-12-18T01:36:56.8040291Z >>> nnz = ctx.new_dynamic_size() 2024-12-18T01:36:56.8040859Z >>> shape = [nnz, x.dim()] 2024-12-18T01:36:56.8041474Z >>> result = x.new_empty(shape, dtype=torch.int64) 2024-12-18T01:36:56.8042090Z >>> return result 2024-12-18T01:36:56.8042538Z >>> 2024-12-18T01:36:56.8043065Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:36:56.8043728Z >>> 2024-12-18T01:36:56.8044132Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2024-12-18T01:36:56.8044947Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2024-12-18T01:36:56.8045830Z >>> trace.print_readable() 2024-12-18T01:36:56.8046320Z >>> 2024-12-18T01:36:56.8046926Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2024-12-18T01:36:56.8047676Z 2024-12-18T01:36:56.8047995Z 2024-12-18T01:36:56.8049138Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2024-12-18T01:36:56.8050424Z 2024-12-18T01:36:56.8050748Z _._ = None 2024-12-18T01:36:56.8051106Z ^ 2024-12-18T01:36:56.8051458Z warnings.warn(msg) 2024-12-18T01:36:56.8051874Z 2024-12-18T01:36:56.8052398Z --- Parse Warning: 9 / 105 --- 2024-12-18T01:36:56.8054419Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=846. 2024-12-18T01:36:56.8056654Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8057580Z Register a backward formula for this custom op. 2024-12-18T01:36:56.8058163Z 2024-12-18T01:36:56.8058738Z In order for an operator to work with autograd, you need to register 2024-12-18T01:36:56.8059464Z a backward formula: 2024-12-18T01:36:56.8060111Z 1. You must tell us how to compute gradients during the backward pass 2024-12-18T01:36:56.8060882Z by providing us a "backward" function. 2024-12-18T01:36:56.8061656Z 2. If you need any values from the forward to compute gradients, you can 2024-12-18T01:36:56.8062487Z use `setup_context` to save values for backward. 2024-12-18T01:36:56.8063052Z 2024-12-18T01:36:56.8063631Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2024-12-18T01:36:56.8064617Z - ``grads`` is one or more gradients. The number of gradients matches 2024-12-18T01:36:56.8065378Z the number of outputs of the operator. 2024-12-18T01:36:56.8066155Z The ``ctx`` object is `the same ctx object `_ used by 2024-12-18T01:36:56.8067163Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2024-12-18T01:36:56.8068062Z same as :meth:`torch.autograd.Function.backward`. 2024-12-18T01:36:56.8068680Z 2024-12-18T01:36:56.8069227Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2024-12-18T01:36:56.8070207Z Please save quantities needed for backward onto the ``ctx`` object via 2024-12-18T01:36:56.8071233Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2024-12-18T01:36:56.8072193Z or assigning them as attributes of ``ctx``. If your custom op has 2024-12-18T01:36:56.8073115Z kwarg-only arguments, we expect the signature of ``setup_context`` 2024-12-18T01:36:56.8074027Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2024-12-18T01:36:56.8074705Z 2024-12-18T01:36:56.8075258Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2024-12-18T01:36:56.8076317Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2024-12-18T01:36:56.8077322Z not depend on or mutate global state. If you need a non-traceable backward, 2024-12-18T01:36:56.8078329Z you can make it a separate custom_op that you call inside ``backward_fn``. 2024-12-18T01:36:56.8079074Z 2024-12-18T01:36:56.8079637Z If you need different autograd behavior on different devices, then we 2024-12-18T01:36:56.8080637Z recommend creating two different custom operators, one for each device 2024-12-18T01:36:56.8081638Z that needs different behavior, and switching between them at runtime. 2024-12-18T01:36:56.8082379Z 2024-12-18T01:36:56.8082703Z Examples: 2024-12-18T01:36:56.8083096Z >>> import torch 2024-12-18T01:36:56.8083613Z >>> import numpy as np 2024-12-18T01:36:56.8084114Z >>> from torch import Tensor 2024-12-18T01:36:56.8084623Z >>> 2024-12-18T01:36:56.8085197Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2024-12-18T01:36:56.8085953Z >>> def numpy_sin(x: Tensor) -> Tensor: 2024-12-18T01:36:56.8086532Z >>> x_np = x.cpu().numpy() 2024-12-18T01:36:56.8087069Z >>> y_np = np.sin(x_np) 2024-12-18T01:36:56.8087672Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:36:56.8088292Z >>> 2024-12-18T01:36:56.8088766Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2024-12-18T01:36:56.8089385Z >>> x, = inputs 2024-12-18T01:36:56.8089937Z >>> ctx.save_for_backward(x) 2024-12-18T01:36:56.8090437Z >>> 2024-12-18T01:36:56.8090842Z >>> def backward(ctx, grad): 2024-12-18T01:36:56.8091389Z >>> x, = ctx.saved_tensors 2024-12-18T01:36:56.8091926Z >>> return grad * x.cos() 2024-12-18T01:36:56.8092431Z >>> 2024-12-18T01:36:56.8092839Z >>> torch.library.register_autograd( 2024-12-18T01:36:56.8093595Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2024-12-18T01:36:56.8094262Z ... ) 2024-12-18T01:36:56.8094623Z >>> 2024-12-18T01:36:56.8095049Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:36:56.8095606Z >>> y = numpy_sin(x) 2024-12-18T01:36:56.8096206Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:36:56.8096924Z >>> assert torch.allclose(grad_x, x.cos()) 2024-12-18T01:36:56.8097474Z >>> 2024-12-18T01:36:56.8098025Z >>> # Example with a keyword-only arg 2024-12-18T01:36:56.8098840Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:36:56.8099670Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2024-12-18T01:36:56.8100299Z >>> x_np = x.cpu().numpy() 2024-12-18T01:36:56.8100827Z >>> y_np = x_np * val 2024-12-18T01:36:56.8101430Z >>> return torch.from_numpy(y_np).to(device=x.device) 2024-12-18T01:36:56.8102036Z >>> 2024-12-18T01:36:56.8102913Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2024-12-18T01:36:56.8103732Z >>> ctx.val = keyword_only_inputs["val"] 2024-12-18T01:36:56.8104284Z >>> 2024-12-18T01:36:56.8104675Z >>> def backward(ctx, grad): 2024-12-18T01:36:56.8105199Z >>> return grad * ctx.val 2024-12-18T01:36:56.8105700Z >>> 2024-12-18T01:36:56.8106115Z >>> torch.library.register_autograd( 2024-12-18T01:36:56.8106831Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2024-12-18T01:36:56.8107496Z ... ) 2024-12-18T01:36:56.8107861Z >>> 2024-12-18T01:36:56.8108270Z >>> x = torch.randn(3, requires_grad=True) 2024-12-18T01:36:56.8108854Z >>> y = numpy_mul(x, val=3.14) 2024-12-18T01:36:56.8109509Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2024-12-18T01:36:56.8110308Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2024-12-18T01:36:56.8110959Z 2024-12-18T01:36:56.8111270Z 2024-12-18T01:36:56.8111907Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8112699Z 2024-12-18T01:36:56.8113041Z warnings.warn(msg) 2024-12-18T01:36:56.8113460Z 2024-12-18T01:36:56.8113988Z --- Parse Warning: 10 / 105 --- 2024-12-18T01:36:56.8115969Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1258. 2024-12-18T01:36:56.8118232Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8119291Z Given an operator and some sample arguments, tests if the operator is 2024-12-18T01:36:56.8120061Z registered correctly. 2024-12-18T01:36:56.8120500Z 2024-12-18T01:36:56.8121061Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2024-12-18T01:36:56.8122067Z custom op, you specified metadata (e.g. mutability info) about the custom op 2024-12-18T01:36:56.8123120Z and these APIs require that the functions you pass them satisfy certain 2024-12-18T01:36:56.8124124Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2024-12-18T01:36:56.8124987Z ``opcheck`` tests these metadata and properties. 2024-12-18T01:36:56.8125610Z 2024-12-18T01:36:56.8125993Z Concretely, we test the following: 2024-12-18T01:36:56.8126520Z 2024-12-18T01:36:56.8127009Z - test_schema: If the schema matches the implementation of 2024-12-18T01:36:56.8127913Z the operator. For example: if the schema specifies a Tensor is mutated, 2024-12-18T01:36:56.8128860Z then we check the implementation mutates the Tensor. If the schema 2024-12-18T01:36:56.8129817Z specifies that we return a new Tensor, then we check that the 2024-12-18T01:36:56.8130748Z implementation returns a new Tensor (instead of an existing one or 2024-12-18T01:36:56.8131527Z a view of an existing one). 2024-12-18T01:36:56.8132236Z - test_autograd_registration: If the operator supports training 2024-12-18T01:36:56.8133135Z (autograd): we check that its autograd formula is registered via 2024-12-18T01:36:56.8134039Z torch.library.register_autograd or a manual registration to one 2024-12-18T01:36:56.8134974Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2024-12-18T01:36:56.8135815Z registrations may lead to undefined behavior. 2024-12-18T01:36:56.8136568Z - test_faketensor: If the operator has a FakeTensor kernel 2024-12-18T01:36:56.8137379Z (and if it is correct). The FakeTensor kernel is necessary ( 2024-12-18T01:36:56.8138255Z but not sufficient) for the operator to work with PyTorch compilation 2024-12-18T01:36:56.8139221Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2024-12-18T01:36:56.8140123Z (also sometimes known as a meta kernel) was registered for the 2024-12-18T01:36:56.8141007Z operator and that it is correct. This test takes the result of 2024-12-18T01:36:56.8141887Z running the operator on real tensors and the result of running 2024-12-18T01:36:56.8142773Z the operator on FakeTensors and checks that they have the same 2024-12-18T01:36:56.8143574Z Tensor metadata (sizes/strides/dtype/device/etc). 2024-12-18T01:36:56.8144391Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2024-12-18T01:36:56.8145262Z with PyTorch compilation APIs (torch.compile/export/FX). 2024-12-18T01:36:56.8146134Z This checks that the outputs (and gradients, if applicable) are the 2024-12-18T01:36:56.8146975Z same under eager-mode PyTorch and torch.compile. 2024-12-18T01:36:56.8147764Z This test is a superset of ``test_faketensor`` and is an e2e test; 2024-12-18T01:36:56.8148579Z other things it tests are that the operator supports 2024-12-18T01:36:56.8149440Z functionalization and that the backward pass (if it exists) also 2024-12-18T01:36:56.8150267Z supports FakeTensor and functionalization. 2024-12-18T01:36:56.8150840Z 2024-12-18T01:36:56.8151364Z For best results, please call ``opcheck`` multiple times with a 2024-12-18T01:36:56.8152214Z representative set of inputs. If your operator supports 2024-12-18T01:36:56.8153135Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2024-12-18T01:36:56.8154196Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2024-12-18T01:36:56.8155078Z use ``opcheck`` with inputs on all supported devices. 2024-12-18T01:36:56.8155743Z 2024-12-18T01:36:56.8156058Z Args: 2024-12-18T01:36:56.8156580Z op: The operator. Must either be a function decorated with 2024-12-18T01:36:56.8157477Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2024-12-18T01:36:56.8158440Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2024-12-18T01:36:56.8159215Z args: The args to the operator 2024-12-18T01:36:56.8159788Z kwargs: The kwargs to the operator 2024-12-18T01:36:56.8160479Z test_utils: Tests that we should run. Default: all of them. 2024-12-18T01:36:56.8161277Z Example: ("test_schema", "test_faketensor") 2024-12-18T01:36:56.8162045Z raise_exception: If we should raise an exception on the first 2024-12-18T01:36:56.8162877Z error. If False, we will return a dict with information 2024-12-18T01:36:56.8163570Z on if each test passed or not. 2024-12-18T01:36:56.8164087Z 2024-12-18T01:36:56.8164425Z .. warning:: 2024-12-18T01:36:56.8164811Z 2024-12-18T01:36:56.8165410Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2024-12-18T01:36:56.8166371Z opcheck tests if your usage of torch.library APIs is correct while 2024-12-18T01:36:56.8167314Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2024-12-18T01:36:56.8168292Z mathematically correct. Use both to test custom ops that support 2024-12-18T01:36:56.8169062Z gradient computation. 2024-12-18T01:36:56.8169535Z 2024-12-18T01:36:56.8169858Z Example: 2024-12-18T01:36:56.8170204Z 2024-12-18T01:36:56.8170647Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:56.8171447Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2024-12-18T01:36:56.8172237Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2024-12-18T01:36:56.8172856Z >>> x_np = x.numpy(force=True) 2024-12-18T01:36:56.8173395Z >>> z_np = x_np * y 2024-12-18T01:36:56.8173946Z >>> return torch.from_numpy(z_np).to(x.device) 2024-12-18T01:36:56.8174521Z >>> 2024-12-18T01:36:56.8174921Z >>> @numpy_mul.register_fake 2024-12-18T01:36:56.8175449Z >>> def _(x, y): 2024-12-18T01:36:56.8175941Z >>> return torch.empty_like(x) 2024-12-18T01:36:56.8176452Z >>> 2024-12-18T01:36:56.8176888Z >>> def setup_context(ctx, inputs, output): 2024-12-18T01:36:56.8177456Z >>> y, = inputs 2024-12-18T01:36:56.8177907Z >>> ctx.y = y 2024-12-18T01:36:56.8178347Z >>> 2024-12-18T01:36:56.8178730Z >>> def backward(ctx, grad): 2024-12-18T01:36:56.8179279Z >>> return grad * ctx.y, None 2024-12-18T01:36:56.8179796Z >>> 2024-12-18T01:36:56.8180416Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2024-12-18T01:36:56.8181160Z >>> 2024-12-18T01:36:56.8181525Z >>> sample_inputs = [ 2024-12-18T01:36:56.8182030Z >>> (torch.randn(3), 3.14), 2024-12-18T01:36:56.8182615Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2024-12-18T01:36:56.8183278Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2024-12-18T01:36:56.8184061Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2024-12-18T01:36:56.8184753Z >>> ] 2024-12-18T01:36:56.8185126Z >>> 2024-12-18T01:36:56.8185528Z >>> for args in sample_inputs: 2024-12-18T01:36:56.8186129Z >>> torch.library.opcheck(numpy_mul, args) 2024-12-18T01:36:56.8186704Z 2024-12-18T01:36:56.8187074Z 2024-12-18T01:36:56.8187710Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8188512Z 2024-12-18T01:36:56.8188855Z warnings.warn(msg) 2024-12-18T01:36:56.8189281Z 2024-12-18T01:36:56.8189807Z --- Parse Warning: 11 / 105 --- 2024-12-18T01:36:56.8191756Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1226. 2024-12-18T01:36:56.8193934Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8195200Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2024-12-18T01:36:56.8196237Z 2024-12-18T01:36:56.8196538Z Loads an object saved with :func:`torch.save` from a file. 2024-12-18T01:36:56.8196686Z 2024-12-18T01:36:56.8197094Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2024-12-18T01:36:56.8197486Z which underlie tensors, specially. They are first deserialized on the 2024-12-18T01:36:56.8198022Z CPU and are then moved to the device they were saved from. If this fails 2024-12-18T01:36:56.8198516Z (e.g. because the run time system doesn't have certain devices), an exception 2024-12-18T01:36:56.8198938Z is raised. However, storages can be dynamically remapped to an alternative 2024-12-18T01:36:56.8199208Z set of devices using the :attr:`map_location` argument. 2024-12-18T01:36:56.8199352Z 2024-12-18T01:36:56.8199764Z If :attr:`map_location` is a callable, it will be called once for each serialized 2024-12-18T01:36:56.8200147Z storage with two arguments: storage and location. The storage argument 2024-12-18T01:36:56.8200559Z will be the initial deserialization of the storage, residing on the CPU. 2024-12-18T01:36:56.8200971Z Each serialized storage has a location tag associated with it which 2024-12-18T01:36:56.8201347Z identifies the device it was saved from, and this tag is the second 2024-12-18T01:36:56.8201778Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2024-12-18T01:36:56.8202171Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2024-12-18T01:36:56.8202794Z :attr:`map_location` should return either ``None`` or a storage. If 2024-12-18T01:36:56.8203229Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2024-12-18T01:36:56.8203665Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2024-12-18T01:36:56.8204083Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2024-12-18T01:36:56.8204233Z 2024-12-18T01:36:56.8204632Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2024-12-18T01:36:56.8205046Z a device tag, it indicates the location where all tensors should be loaded. 2024-12-18T01:36:56.8205178Z 2024-12-18T01:36:56.8205622Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2024-12-18T01:36:56.8205992Z appearing in the file (keys), to ones that specify where to put the 2024-12-18T01:36:56.8206150Z storages (values). 2024-12-18T01:36:56.8206297Z 2024-12-18T01:36:56.8206682Z User extensions can register their own location tags and tagging and 2024-12-18T01:36:56.8207162Z deserialization methods using :func:`torch.serialization.register_package`. 2024-12-18T01:36:56.8207291Z 2024-12-18T01:36:56.8207426Z Args: 2024-12-18T01:36:56.8207994Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2024-12-18T01:36:56.8208284Z or a string or os.PathLike object containing a file name 2024-12-18T01:36:56.8208941Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2024-12-18T01:36:56.8209084Z locations 2024-12-18T01:36:56.8209491Z pickle_module: module used for unpickling metadata and objects (has to 2024-12-18T01:36:56.8209776Z match the :attr:`pickle_module` used to serialize file) 2024-12-18T01:36:56.8210140Z weights_only: Indicates whether unpickler should be restricted to 2024-12-18T01:36:56.8210425Z loading only tensors, primitive types, dictionaries 2024-12-18T01:36:56.8210803Z and any types added via :func:`torch.serialization.add_safe_globals`. 2024-12-18T01:36:56.8211032Z See :ref:`weights-only` for more details. 2024-12-18T01:36:56.8211622Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2024-12-18T01:36:56.8212276Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2024-12-18T01:36:56.8212878Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2024-12-18T01:36:56.8213497Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2024-12-18T01:36:56.8213915Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2024-12-18T01:36:56.8214330Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2024-12-18T01:36:56.8214719Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2024-12-18T01:36:56.8214884Z :attr:`errors=...`. 2024-12-18T01:36:56.8215032Z 2024-12-18T01:36:56.8215195Z .. warning:: 2024-12-18T01:36:56.8215556Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2024-12-18T01:36:56.8215937Z uses ``pickle`` module implicitly, which is known to be insecure. 2024-12-18T01:36:56.8216422Z It is possible to construct malicious pickle data which will execute arbitrary code 2024-12-18T01:36:56.8216851Z during unpickling. Never load data that could have come from an untrusted 2024-12-18T01:36:56.8217364Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2024-12-18T01:36:56.8217513Z 2024-12-18T01:36:56.8217657Z .. note:: 2024-12-18T01:36:56.8218117Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2024-12-18T01:36:56.8218560Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2024-12-18T01:36:56.8219023Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2024-12-18T01:36:56.8219172Z 2024-12-18T01:36:56.8219316Z .. note:: 2024-12-18T01:36:56.8219761Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2024-12-18T01:36:56.8220132Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2024-12-18T01:36:56.8220498Z when loading files saved by Python 2 in Python 3. If this default 2024-12-18T01:36:56.8220942Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2024-12-18T01:36:56.8221351Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2024-12-18T01:36:56.8221775Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2024-12-18T01:36:56.8222162Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2024-12-18T01:36:56.8222306Z 2024-12-18T01:36:56.8222448Z Example: 2024-12-18T01:36:56.8222680Z >>> # xdoctest: +SKIP("undefined filepaths") 2024-12-18T01:36:56.8222913Z >>> torch.load("tensors.pt", weights_only=True) 2024-12-18T01:36:56.8223131Z # Load all tensors onto the CPU 2024-12-18T01:36:56.8223572Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2024-12-18T01:36:56.8223811Z # Load all tensors onto the CPU, using a function 2024-12-18T01:36:56.8223973Z >>> torch.load( 2024-12-18T01:36:56.8224385Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2024-12-18T01:36:56.8224541Z ... ) 2024-12-18T01:36:56.8224718Z # Load all tensors onto GPU 1 2024-12-18T01:36:56.8224865Z >>> torch.load( 2024-12-18T01:36:56.8225042Z ... "tensors.pt", 2024-12-18T01:36:56.8225309Z ... map_location=lambda storage, loc: storage.cuda(1), 2024-12-18T01:36:56.8225522Z ... weights_only=True, 2024-12-18T01:36:56.8225714Z ... ) # type: ignore[attr-defined] 2024-12-18T01:36:56.8225902Z # Map tensors from GPU 1 to GPU 0 2024-12-18T01:36:56.8226334Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2024-12-18T01:36:56.8226528Z # Load tensor from io.BytesIO object 2024-12-18T01:36:56.8226983Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2024-12-18T01:36:56.8227208Z >>> with open("tensor.pt", "rb") as f: 2024-12-18T01:36:56.8227403Z ... buffer = io.BytesIO(f.read()) 2024-12-18T01:36:56.8227610Z >>> torch.load(buffer, weights_only=False) 2024-12-18T01:36:56.8227863Z # Load a module with 'ascii' encoding for unpickling 2024-12-18T01:36:56.8228300Z # Loading from a module setting weights_only=False, warning this can be unsafe 2024-12-18T01:36:56.8228627Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2024-12-18T01:36:56.8228780Z 2024-12-18T01:36:56.8229246Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8229393Z 2024-12-18T01:36:56.8229551Z warnings.warn(msg) 2024-12-18T01:36:56.8229680Z 2024-12-18T01:36:56.8230047Z --- Parse Warning: 12 / 105 --- 2024-12-18T01:36:56.8231675Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=21. 2024-12-18T01:36:56.8232115Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:56.8232439Z Check if there is an available :ref:`accelerator`. 2024-12-18T01:36:56.8232589Z 2024-12-18T01:36:56.8232727Z Returns: 2024-12-18T01:36:56.8233205Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2024-12-18T01:36:56.8233354Z 2024-12-18T01:36:56.8233497Z Example:: 2024-12-18T01:36:56.8233643Z 2024-12-18T01:36:56.8234117Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:56.8234252Z 2024-12-18T01:36:56.8235275Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2024-12-18T01:36:56.8235409Z 2024-12-18T01:36:56.8235966Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:56.8236125Z ^ 2024-12-18T01:36:56.8236300Z warnings.warn(msg) 2024-12-18T01:36:56.8236431Z 2024-12-18T01:36:56.8236742Z --- Parse Warning: 13 / 105 --- 2024-12-18T01:36:56.8238387Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=110. 2024-12-18T01:36:56.8238817Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:56.8239226Z Wait for all kernels in all streams on the given device to complete. 2024-12-18T01:36:56.8239360Z 2024-12-18T01:36:56.8239511Z Args: 2024-12-18T01:36:56.8240063Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2024-12-18T01:36:56.8240454Z the current :ref:`accelerator` device type. If not given, 2024-12-18T01:36:56.8240790Z use :func:`torch.accelerator.current_device_idx` by default. 2024-12-18T01:36:56.8240921Z 2024-12-18T01:36:56.8241484Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2024-12-18T01:36:56.8241620Z 2024-12-18T01:36:56.8241820Z Example:: 2024-12-18T01:36:56.8241949Z 2024-12-18T01:36:56.8242178Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:56.8242668Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:56.8242906Z >>> start_event = torch.Event(enable_timing=True) 2024-12-18T01:36:56.8243144Z >>> end_event = torch.Event(enable_timing=True) 2024-12-18T01:36:56.8243308Z >>> start_event.record() 2024-12-18T01:36:56.8243773Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2024-12-18T01:36:56.8243946Z >>> sum = torch.sum(tensor) 2024-12-18T01:36:56.8244108Z >>> end_event.record() 2024-12-18T01:36:56.8244333Z >>> torch.accelerator.synchronize() 2024-12-18T01:36:56.8244617Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2024-12-18T01:36:56.8244770Z 2024-12-18T01:36:56.8245761Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2024-12-18T01:36:56.8245949Z 2024-12-18T01:36:56.8246405Z assert torch.accelerator.is_available() "No available accelerators detected." 2024-12-18T01:36:56.8246560Z ^ 2024-12-18T01:36:56.8246736Z warnings.warn(msg) 2024-12-18T01:36:56.8246871Z 2024-12-18T01:36:56.8247195Z --- Parse Warning: 14 / 105 --- 2024-12-18T01:36:56.8248724Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=343. 2024-12-18T01:36:56.8249167Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:56.8249368Z Retrieves the CUDA runtime API module. 2024-12-18T01:36:56.8249500Z 2024-12-18T01:36:56.8249651Z 2024-12-18T01:36:56.8250087Z This function initializes the CUDA runtime environment if it is not already 2024-12-18T01:36:56.8250503Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2024-12-18T01:36:56.8250894Z runtime API module provides access to various CUDA runtime functions. 2024-12-18T01:36:56.8251028Z 2024-12-18T01:36:56.8251183Z Args: 2024-12-18T01:36:56.8251325Z ``None`` 2024-12-18T01:36:56.8251471Z 2024-12-18T01:36:56.8251613Z Returns: 2024-12-18T01:36:56.8251851Z module: The CUDA runtime API module (_cudart). 2024-12-18T01:36:56.8252000Z 2024-12-18T01:36:56.8252135Z Raises: 2024-12-18T01:36:56.8252548Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2024-12-18T01:36:56.8253181Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2024-12-18T01:36:56.8253321Z 2024-12-18T01:36:56.8253545Z Example of CUDA operations with profiling: 2024-12-18T01:36:56.8253703Z >>> import torch 2024-12-18T01:36:56.8253951Z >>> from torch.cuda import cudart, check_error 2024-12-18T01:36:56.8254144Z >>> import os 2024-12-18T01:36:56.8254296Z >>> 2024-12-18T01:36:56.8254491Z >>> os.environ['CUDA_PROFILE'] = '1' 2024-12-18T01:36:56.8254624Z >>> 2024-12-18T01:36:56.8254871Z >>> def perform_cuda_operations_with_streams(): 2024-12-18T01:36:56.8255064Z >>> stream = torch.cuda.Stream() 2024-12-18T01:36:56.8255279Z >>> with torch.cuda.stream(stream): 2024-12-18T01:36:56.8255482Z >>> x = torch.randn(100, 100, device='cuda') 2024-12-18T01:36:56.8255695Z >>> y = torch.randn(100, 100, device='cuda') 2024-12-18T01:36:56.8255862Z >>> z = torch.mul(x, y) 2024-12-18T01:36:56.8256004Z >>> return z 2024-12-18T01:36:56.8256191Z >>> 2024-12-18T01:36:56.8256373Z >>> torch.cuda.synchronize() 2024-12-18T01:36:56.8256601Z >>> print("====== Start nsys profiling ======") 2024-12-18T01:36:56.8256828Z >>> check_error(cudart().cudaProfilerStart()) 2024-12-18T01:36:56.8257071Z >>> with torch.autograd.profiler.emit_nvtx(): 2024-12-18T01:36:56.8257321Z >>> result = perform_cuda_operations_with_streams() 2024-12-18T01:36:56.8257526Z >>> print("CUDA operations completed.") 2024-12-18T01:36:56.8257842Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2024-12-18T01:36:56.8258055Z >>> print("====== End nsys profiling ======") 2024-12-18T01:36:56.8258198Z 2024-12-18T01:36:56.8258539Z To run this example and save the profiling information, execute: 2024-12-18T01:36:56.8259166Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:36:56.8259313Z 2024-12-18T01:36:56.8259735Z This command profiles the CUDA operations in the provided script and saves 2024-12-18T01:36:56.8260131Z the profiling information to a file named `trace_name.prof`. 2024-12-18T01:36:56.8260538Z The `--profile-from-start off` option ensures that profiling starts only 2024-12-18T01:36:56.8260798Z after the `cudaProfilerStart` call in the script. 2024-12-18T01:36:56.8261172Z The `--csv` and `--print-summary` options format the profiling output as a 2024-12-18T01:36:56.8261394Z CSV file and print a summary, respectively. 2024-12-18T01:36:56.8261828Z The `-o` option specifies the output file name, and the `-f` option forces the 2024-12-18T01:36:56.8262080Z overwrite of the output file if it already exists. 2024-12-18T01:36:56.8262229Z 2024-12-18T01:36:56.8263379Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2024-12-18T01:36:56.8263528Z 2024-12-18T01:36:56.8264142Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2024-12-18T01:36:56.8264276Z ^ 2024-12-18T01:36:56.8264450Z warnings.warn(msg) 2024-12-18T01:36:56.8264581Z 2024-12-18T01:36:56.8264909Z --- Parse Warning: 15 / 105 --- 2024-12-18T01:36:56.8266485Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2024-12-18T01:36:56.8266955Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8267090Z 2024-12-18T01:36:56.8267482Z Append the given callback function to this ``Future``, which will be run 2024-12-18T01:36:56.8267847Z when the ``Future`` is completed. Multiple callbacks can be added to 2024-12-18T01:36:56.8268205Z the same ``Future``, but the order in which they will be executed cannot 2024-12-18T01:36:56.8268531Z be guaranteed (to enforce a certain order consider chaining: 2024-12-18T01:36:56.8268910Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2024-12-18T01:36:56.8269275Z is the reference to this ``Future``. The callback function can use the 2024-12-18T01:36:56.8269622Z :meth:`value` method to get the value. Note that if this ``Future`` is 2024-12-18T01:36:56.8270008Z already completed, the given callback will be run immediately inline. 2024-12-18T01:36:56.8270157Z 2024-12-18T01:36:56.8270480Z If the ``Future``'s value contains tensors that reside on GPUs, the 2024-12-18T01:36:56.8270865Z callback might be invoked while the async kernels that are populating 2024-12-18T01:36:56.8271260Z those tensors haven't yet finished executing on the device. However, the 2024-12-18T01:36:56.8271635Z callback will be invoked with some dedicated streams set as current 2024-12-18T01:36:56.8272016Z (fetched from a global pool) which will be synchronized with those 2024-12-18T01:36:56.8272409Z kernels. Hence any operation performed by the callback on these tensors 2024-12-18T01:36:56.8272781Z will be scheduled on the device after the kernels complete. In other 2024-12-18T01:36:56.8273117Z words, as long as the callback doesn't switch streams, it can safely 2024-12-18T01:36:56.8273557Z manipulate the result without any additional synchronization. This is 2024-12-18T01:36:56.8273823Z similar to the non-blocking behavior of :meth:`wait`. 2024-12-18T01:36:56.8273971Z 2024-12-18T01:36:56.8274338Z Similarly, if the callback returns a value that contains tensors that 2024-12-18T01:36:56.8274671Z reside on a GPU, it can do so even if the kernels that are producing 2024-12-18T01:36:56.8275061Z these tensors are still running on the device, as long as the callback 2024-12-18T01:36:56.8275407Z didn't change streams during its execution. If one wants to change 2024-12-18T01:36:56.8275898Z streams, one must be careful to re-synchronize them with the original 2024-12-18T01:36:56.8276276Z streams, that is, those that were current when the callback was invoked. 2024-12-18T01:36:56.8276423Z 2024-12-18T01:36:56.8276565Z Args: 2024-12-18T01:36:56.8276919Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2024-12-18T01:36:56.8277114Z the only argument. 2024-12-18T01:36:56.8277249Z 2024-12-18T01:36:56.8277411Z Returns: 2024-12-18T01:36:56.8277691Z A new ``Future`` object that holds the return value of the 2024-12-18T01:36:56.8278007Z ``callback`` and will be marked as completed when the given 2024-12-18T01:36:56.8278173Z ``callback`` finishes. 2024-12-18T01:36:56.8278305Z 2024-12-18T01:36:56.8278609Z .. note:: Note that if the callback function throws, either 2024-12-18T01:36:56.8278968Z through the original future being completed with an exception and 2024-12-18T01:36:56.8279314Z calling ``fut.wait()``, or through other code in the callback, the 2024-12-18T01:36:56.8279660Z future returned by ``then`` will be marked appropriately with the 2024-12-18T01:36:56.8280000Z encountered error. However, if this callback later completes 2024-12-18T01:36:56.8280380Z additional futures, those futures are not marked as completed with 2024-12-18T01:36:56.8280726Z an error and the user is responsible for handling completion/waiting 2024-12-18T01:36:56.8280926Z on those futures independently. 2024-12-18T01:36:56.8281057Z 2024-12-18T01:36:56.8281212Z Example:: 2024-12-18T01:36:56.8281456Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:36:56.8281612Z >>> def callback(fut): 2024-12-18T01:36:56.8281855Z ... print(f"RPC return value is {fut.wait()}.") 2024-12-18T01:36:56.8282037Z >>> fut = torch.futures.Future() 2024-12-18T01:36:56.8282332Z >>> # The inserted callback will print the return value when 2024-12-18T01:36:56.8282534Z >>> # receiving the response from "worker1" 2024-12-18T01:36:56.8282755Z >>> cb_fut = fut.then(callback) 2024-12-18T01:36:56.8282929Z >>> chain_cb_fut = cb_fut.then( 2024-12-18T01:36:56.8283173Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2024-12-18T01:36:56.8283321Z ... ) 2024-12-18T01:36:56.8283478Z >>> fut.set_result(5) 2024-12-18T01:36:56.8283649Z RPC return value is 5. 2024-12-18T01:36:56.8283812Z Chained cb done. None 2024-12-18T01:36:56.8283944Z 2024-12-18T01:36:56.8284403Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8284536Z 2024-12-18T01:36:56.8284706Z warnings.warn(msg) 2024-12-18T01:36:56.8284839Z 2024-12-18T01:36:56.8285156Z --- Parse Warning: 16 / 105 --- 2024-12-18T01:36:56.8286792Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=209. 2024-12-18T01:36:56.8287294Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8287438Z 2024-12-18T01:36:56.8287787Z Set the result for this ``Future``, which will mark this ``Future`` as 2024-12-18T01:36:56.8288206Z completed and trigger all attached callbacks. Note that a ``Future`` 2024-12-18T01:36:56.8288391Z cannot be marked completed twice. 2024-12-18T01:36:56.8288537Z 2024-12-18T01:36:56.8288913Z If the result contains tensors that reside on GPUs, this method can be 2024-12-18T01:36:56.8289262Z called even if the asynchronous kernels that are populating those 2024-12-18T01:36:56.8289653Z tensors haven't yet completed running on the device, provided that the 2024-12-18T01:36:56.8290038Z streams on which those kernels were enqueued are set as the current ones 2024-12-18T01:36:56.8290443Z when this method is called. Put simply, it's safe to call this method 2024-12-18T01:36:56.8290815Z immediately after launching those kernels, without any additional 2024-12-18T01:36:56.8291218Z synchronization, as long as one doesn't change streams in between. This 2024-12-18T01:36:56.8291589Z method will record events on all the relevant current streams and will 2024-12-18T01:36:56.8291933Z use them to ensure proper scheduling for all the consumers of this 2024-12-18T01:36:56.8292088Z ``Future``. 2024-12-18T01:36:56.8292225Z 2024-12-18T01:36:56.8292373Z Args: 2024-12-18T01:36:56.8292646Z result (object): the result object of this ``Future``. 2024-12-18T01:36:56.8292775Z 2024-12-18T01:36:56.8292934Z Example:: 2024-12-18T01:36:56.8293177Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2024-12-18T01:36:56.8293349Z >>> import threading 2024-12-18T01:36:56.8293498Z >>> import time 2024-12-18T01:36:56.8293682Z >>> def slow_set_future(fut, value): 2024-12-18T01:36:56.8293852Z ... time.sleep(0.5) 2024-12-18T01:36:56.8294023Z ... fut.set_result(value) 2024-12-18T01:36:56.8294219Z >>> fut = torch.futures.Future() 2024-12-18T01:36:56.8294382Z >>> t = threading.Thread( 2024-12-18T01:36:56.8294560Z ... target=slow_set_future, 2024-12-18T01:36:56.8294741Z ... args=(fut, torch.ones(2) * 3) 2024-12-18T01:36:56.8294880Z ... ) 2024-12-18T01:36:56.8295048Z >>> t.start() 2024-12-18T01:36:56.8295200Z >>> print(fut.wait()) 2024-12-18T01:36:56.8295362Z tensor([3., 3.]) 2024-12-18T01:36:56.8295506Z >>> t.join() 2024-12-18T01:36:56.8295639Z 2024-12-18T01:36:56.8296095Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8296225Z 2024-12-18T01:36:56.8296397Z warnings.warn(msg) 2024-12-18T01:36:56.8296533Z 2024-12-18T01:36:56.8296844Z --- Parse Warning: 17 / 105 --- 2024-12-18T01:36:56.8298609Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144. 2024-12-18T01:36:56.8299180Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8299577Z Compiles compute shader from source and allows one to invoke kernels 2024-12-18T01:36:56.8299826Z defined there from the comfort of Python runtime 2024-12-18T01:36:56.8299990Z Example:: 2024-12-18T01:36:56.8300124Z 2024-12-18T01:36:56.8300364Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2024-12-18T01:36:56.8300556Z >>> lib = torch.mps._compile_shader( 2024-12-18T01:36:56.8301226Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2024-12-18T01:36:56.8301433Z ... ) 2024-12-18T01:36:56.8301624Z >>> x = torch.zeros(16, device="mps") 2024-12-18T01:36:56.8301799Z >>> lib.full(x, 3.14) 2024-12-18T01:36:56.8301935Z 2024-12-18T01:36:56.8302370Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8302827Z 2024-12-18T01:36:56.8302990Z warnings.warn(msg) 2024-12-18T01:36:56.8303136Z 2024-12-18T01:36:56.8303517Z --- Parse Warning: 18 / 105 --- 2024-12-18T01:36:56.8305030Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2024-12-18T01:36:56.8305486Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8305756Z Return the sum of each row of the given sparse tensor. 2024-12-18T01:36:56.8305908Z 2024-12-18T01:36:56.8306292Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2024-12-18T01:36:56.8306687Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2024-12-18T01:36:56.8307050Z reduce over all of them. When sum over all ``sparse_dim``, this method 2024-12-18T01:36:56.8307321Z returns a dense tensor instead of a sparse tensor. 2024-12-18T01:36:56.8307455Z 2024-12-18T01:36:56.8307906Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2024-12-18T01:36:56.8308251Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2024-12-18T01:36:56.8308384Z 2024-12-18T01:36:56.8308776Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2024-12-18T01:36:56.8309192Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2024-12-18T01:36:56.8309343Z 2024-12-18T01:36:56.8309480Z Args: 2024-12-18T01:36:56.8309689Z input (Tensor): the input sparse tensor 2024-12-18T01:36:56.8310173Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2024-12-18T01:36:56.8310330Z over all dims. 2024-12-18T01:36:56.8310778Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2024-12-18T01:36:56.8310966Z Default: dtype of :attr:`input`. 2024-12-18T01:36:56.8311095Z 2024-12-18T01:36:56.8311257Z Example:: 2024-12-18T01:36:56.8311389Z 2024-12-18T01:36:56.8311541Z >>> nnz = 3 2024-12-18T01:36:56.8311693Z >>> dims = [5, 5, 2, 3] 2024-12-18T01:36:56.8311972Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2024-12-18T01:36:56.8312280Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2024-12-18T01:36:56.8312475Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2024-12-18T01:36:56.8312663Z >>> size = torch.Size(dims) 2024-12-18T01:36:56.8312892Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:56.8313113Z >>> S = torch.sparse_coo_tensor(I, V, size) 2024-12-18T01:36:56.8313286Z >>> S 2024-12-18T01:36:56.8313469Z tensor(indices=tensor([[2, 0, 3], 2024-12-18T01:36:56.8313640Z [2, 4, 1]]), 2024-12-18T01:36:56.8313846Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2024-12-18T01:36:56.8314038Z [ 0.3411, 0.0918, -0.2312]], 2024-12-18T01:36:56.8314171Z 2024-12-18T01:36:56.8314360Z [[ 0.5348, 0.0634, -2.0494], 2024-12-18T01:36:56.8314535Z [-0.7125, -1.0646, 2.1844]], 2024-12-18T01:36:56.8314665Z 2024-12-18T01:36:56.8314852Z [[ 0.1276, 0.1874, -0.6334], 2024-12-18T01:36:56.8315030Z [-1.9682, -0.5340, 0.7483]]]), 2024-12-18T01:36:56.8315316Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:36:56.8315451Z 2024-12-18T01:36:56.8315845Z # when sum over only part of sparse_dims, return a sparse tensor 2024-12-18T01:36:56.8316028Z >>> torch.sparse.sum(S, [1, 3]) 2024-12-18T01:36:56.8316214Z tensor(indices=tensor([[0, 2, 3]]), 2024-12-18T01:36:56.8316414Z values=tensor([[-1.4512, 0.4073], 2024-12-18T01:36:56.8316634Z [-0.8901, 0.2017], 2024-12-18T01:36:56.8316816Z [-0.3183, -1.7539]]), 2024-12-18T01:36:56.8317038Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2024-12-18T01:36:56.8317172Z 2024-12-18T01:36:56.8317436Z # when sum over all sparse dim, return a dense tensor 2024-12-18T01:36:56.8317610Z # with summed dims squeezed 2024-12-18T01:36:56.8317810Z >>> torch.sparse.sum(S, [0, 1, 3]) 2024-12-18T01:36:56.8317973Z tensor([-2.6596, -1.1450]) 2024-12-18T01:36:56.8318122Z 2024-12-18T01:36:56.8318591Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8318727Z 2024-12-18T01:36:56.8318901Z warnings.warn(msg) 2024-12-18T01:36:56.8319031Z 2024-12-18T01:36:56.8319356Z --- Parse Warning: 19 / 105 --- 2024-12-18T01:36:56.8320878Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=40. 2024-12-18T01:36:56.8321353Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8321487Z 2024-12-18T01:36:56.8321860Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2024-12-18T01:36:56.8322208Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2024-12-18T01:36:56.8322583Z pushes the map into PyTorch operations called by ``func``, effectively 2024-12-18T01:36:56.8322781Z vectorizing those operations. 2024-12-18T01:36:56.8322916Z 2024-12-18T01:36:56.8323280Z vmap is useful for handling batch dimensions: one can write a function 2024-12-18T01:36:56.8323639Z ``func`` that runs on examples and then lift it to a function that can 2024-12-18T01:36:56.8324005Z take batches of examples with ``vmap(func)``. vmap can also be used to 2024-12-18T01:36:56.8324307Z compute batched gradients when composed with autograd. 2024-12-18T01:36:56.8324443Z 2024-12-18T01:36:56.8324598Z .. note:: 2024-12-18T01:36:56.8324908Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2024-12-18T01:36:56.8325127Z convenience. Use whichever one you'd like. 2024-12-18T01:36:56.8325277Z 2024-12-18T01:36:56.8325413Z Args: 2024-12-18T01:36:56.8325781Z func (function): A Python function that takes one or more arguments. 2024-12-18T01:36:56.8325970Z Must return one or more Tensors. 2024-12-18T01:36:56.8326325Z in_dims (int or nested structure): Specifies which dimension of the 2024-12-18T01:36:56.8326646Z inputs should be mapped over. ``in_dims`` should have a 2024-12-18T01:36:56.8326968Z structure like the inputs. If the ``in_dim`` for a particular 2024-12-18T01:36:56.8327298Z input is None, then that indicates there is no map dimension. 2024-12-18T01:36:56.8327447Z Default: 0. 2024-12-18T01:36:56.8327785Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2024-12-18T01:36:56.8328108Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2024-12-18T01:36:56.8328376Z it should have one element per output. Default: 0. 2024-12-18T01:36:56.8328684Z randomness (str): Specifies whether the randomness in this 2024-12-18T01:36:56.8329037Z vmap should be the same or different across batches. If 'different', 2024-12-18T01:36:56.8329504Z the randomness for each batch will be different. If 'same', the 2024-12-18T01:36:56.8329875Z randomness will be the same across batches. If 'error', any calls to 2024-12-18T01:36:56.8330250Z random functions will error. Default: 'error'. WARNING: this flag 2024-12-18T01:36:56.8330590Z only applies to random PyTorch operations and does not apply to 2024-12-18T01:36:56.8330858Z Python's random module or numpy randomness. 2024-12-18T01:36:56.8331243Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2024-12-18T01:36:56.8331617Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2024-12-18T01:36:56.8332078Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2024-12-18T01:36:56.8332539Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2024-12-18T01:36:56.8332687Z 2024-12-18T01:36:56.8332825Z Returns: 2024-12-18T01:36:56.8333188Z Returns a new "batched" function. It takes the same inputs as 2024-12-18T01:36:56.8333502Z ``func``, except each input has an extra dimension at the index 2024-12-18T01:36:56.8333820Z specified by ``in_dims``. It takes returns the same outputs as 2024-12-18T01:36:56.8334140Z ``func``, except each output has an extra dimension at the index 2024-12-18T01:36:56.8334309Z specified by ``out_dims``. 2024-12-18T01:36:56.8334458Z 2024-12-18T01:36:56.8334596Z .. warning: 2024-12-18T01:36:56.8334935Z :func:`vmap` works best with functional-style code. Please do not 2024-12-18T01:36:56.8335260Z perform any side-effects in ``func``, with the exception of 2024-12-18T01:36:56.8335664Z in-place PyTorch operations. Examples of side-effects include mutating 2024-12-18T01:36:56.8336062Z Python data structures and assigning values to variables not captured 2024-12-18T01:36:56.8336205Z in ``func``. 2024-12-18T01:36:56.8336344Z 2024-12-18T01:36:56.8336747Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2024-12-18T01:36:56.8337123Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2024-12-18T01:36:56.8337509Z rummaging through docs, use :func:`vmap` to construct a new function. 2024-12-18T01:36:56.8337638Z 2024-12-18T01:36:56.8337882Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:36:56.8338218Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2024-12-18T01:36:56.8338434Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:36:56.8338593Z >>> batched_dot(x, y) 2024-12-18T01:36:56.8338726Z 2024-12-18T01:36:56.8339131Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2024-12-18T01:36:56.8339302Z model authoring experience. 2024-12-18T01:36:56.8339453Z 2024-12-18T01:36:56.8339634Z >>> batch_size, feature_size = 3, 5 2024-12-18T01:36:56.8339924Z >>> weights = torch.randn(feature_size, requires_grad=True) 2024-12-18T01:36:56.8340112Z >>> 2024-12-18T01:36:56.8340283Z >>> def model(feature_vec): 2024-12-18T01:36:56.8340516Z >>> # Very simple linear model with activation 2024-12-18T01:36:56.8340721Z >>> return feature_vec.dot(weights).relu() 2024-12-18T01:36:56.8340870Z >>> 2024-12-18T01:36:56.8341121Z >>> examples = torch.randn(batch_size, feature_size) 2024-12-18T01:36:56.8341321Z >>> result = torch.vmap(model)(examples) 2024-12-18T01:36:56.8341469Z 2024-12-18T01:36:56.8341900Z :func:`vmap` can also help vectorize computations that were previously difficult 2024-12-18T01:36:56.8342317Z or impossible to batch. One example is higher-order gradient computation. 2024-12-18T01:36:56.8342710Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2024-12-18T01:36:56.8343154Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2024-12-18T01:36:56.8343582Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2024-12-18T01:36:56.8343988Z we can vectorize the whole computation, computing the Jacobian in a single 2024-12-18T01:36:56.8344169Z call to ``autograd.grad``. 2024-12-18T01:36:56.8344300Z 2024-12-18T01:36:56.8344455Z >>> # Setup 2024-12-18T01:36:56.8344591Z >>> N = 5 2024-12-18T01:36:56.8344783Z >>> f = lambda x: x ** 2 2024-12-18T01:36:56.8344998Z >>> x = torch.randn(N, requires_grad=True) 2024-12-18T01:36:56.8345138Z >>> y = f(x) 2024-12-18T01:36:56.8345304Z >>> I_N = torch.eye(N) 2024-12-18T01:36:56.8345440Z >>> 2024-12-18T01:36:56.8345605Z >>> # Sequential approach 2024-12-18T01:36:56.8345989Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2024-12-18T01:36:56.8346167Z >>> for v in I_N.unbind()] 2024-12-18T01:36:56.8346376Z >>> jacobian = torch.stack(jacobian_rows) 2024-12-18T01:36:56.8346542Z >>> 2024-12-18T01:36:56.8346755Z >>> # vectorized gradient computation 2024-12-18T01:36:56.8346909Z >>> def get_vjp(v): 2024-12-18T01:36:56.8347113Z >>> return torch.autograd.grad(y, x, v) 2024-12-18T01:36:56.8347325Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2024-12-18T01:36:56.8347459Z 2024-12-18T01:36:56.8347928Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2024-12-18T01:36:56.8348061Z 2024-12-18T01:36:56.8348290Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:36:56.8348773Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2024-12-18T01:36:56.8348998Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2024-12-18T01:36:56.8349215Z >>> batched_dot(x, y) # tensor of size [2, 3] 2024-12-18T01:36:56.8349354Z 2024-12-18T01:36:56.8349785Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2024-12-18T01:36:56.8350042Z the dimension that each inputs are batched along as 2024-12-18T01:36:56.8350177Z 2024-12-18T01:36:56.8350427Z >>> torch.dot # [N], [N] -> [] 2024-12-18T01:36:56.8350793Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2024-12-18T01:36:56.8351020Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2024-12-18T01:36:56.8351432Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2024-12-18T01:36:56.8351573Z 2024-12-18T01:36:56.8352022Z If there are multiple inputs each of which is batched along different dimensions, 2024-12-18T01:36:56.8352355Z ``in_dims`` must be a tuple with the batch dimension for each input as 2024-12-18T01:36:56.8352502Z 2024-12-18T01:36:56.8352735Z >>> torch.dot # [D], [D] -> [] 2024-12-18T01:36:56.8353144Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2024-12-18T01:36:56.8353381Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:36:56.8353808Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2024-12-18T01:36:56.8353939Z 2024-12-18T01:36:56.8354341Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2024-12-18T01:36:56.8354542Z matching the shape of the input: 2024-12-18T01:36:56.8354676Z 2024-12-18T01:36:56.8354921Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2024-12-18T01:36:56.8355119Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2024-12-18T01:36:56.8355280Z >>> input = {'x': x, 'y': y} 2024-12-18T01:36:56.8355592Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2024-12-18T01:36:56.8355862Z >>> batched_dot(input) 2024-12-18T01:36:56.8356054Z 2024-12-18T01:36:56.8356531Z By default, the output is batched along the first dimension. However, it can be batched 2024-12-18T01:36:56.8356750Z along any dimension by using ``out_dims`` 2024-12-18T01:36:56.8356882Z 2024-12-18T01:36:56.8357035Z >>> f = lambda x: x ** 2 2024-12-18T01:36:56.8357209Z >>> x = torch.randn(2, 5) 2024-12-18T01:36:56.8357407Z >>> batched_pow = torch.vmap(f, out_dims=1) 2024-12-18T01:36:56.8357614Z >>> batched_pow(x) # [5, 2] 2024-12-18T01:36:56.8357744Z 2024-12-18T01:36:56.8358253Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2024-12-18T01:36:56.8358411Z accept kwargs 2024-12-18T01:36:56.8358546Z 2024-12-18T01:36:56.8358718Z >>> x = torch.randn([2, 5]) 2024-12-18T01:36:56.8358873Z >>> def fn(x, scale=4.): 2024-12-18T01:36:56.8359037Z >>> return x * scale 2024-12-18T01:36:56.8359169Z >>> 2024-12-18T01:36:56.8359349Z >>> batched_pow = torch.vmap(fn) 2024-12-18T01:36:56.8359590Z >>> assert torch.allclose(batched_pow(x), x * 4) 2024-12-18T01:36:56.8359997Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2024-12-18T01:36:56.8360147Z 2024-12-18T01:36:56.8360290Z .. note:: 2024-12-18T01:36:56.8360670Z vmap does not provide general autobatching or handle variable-length 2024-12-18T01:36:56.8360847Z sequences out of the box. 2024-12-18T01:36:56.8360979Z 2024-12-18T01:36:56.8361432Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8361561Z 2024-12-18T01:36:56.8361731Z warnings.warn(msg) 2024-12-18T01:36:56.8361863Z 2024-12-18T01:36:56.8362200Z --- Parse Warning: 20 / 105 --- 2024-12-18T01:36:56.8363754Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=20. 2024-12-18T01:36:56.8364217Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8364673Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2024-12-18T01:36:56.8364804Z 2024-12-18T01:36:56.8365170Z This is a more structured way of using triton kernels with PyTorch. 2024-12-18T01:36:56.8365611Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2024-12-18T01:36:56.8366046Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2024-12-18T01:36:56.8366208Z that is simpler; 2024-12-18T01:36:56.8366647Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2024-12-18T01:36:56.8367048Z want to create an operator that behaves like PyTorch built-in operators. 2024-12-18T01:36:56.8367408Z For example, you may use a ``torch.library`` wrapper API to define the 2024-12-18T01:36:56.8367795Z behavior of the triton kernel when passed a tensor subclass or under 2024-12-18T01:36:56.8367967Z a TorchDispatchMode. 2024-12-18T01:36:56.8368136Z 2024-12-18T01:36:56.8368586Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2024-12-18T01:36:56.8368751Z when the implementation 2024-12-18T01:36:56.8369137Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2024-12-18T01:36:56.8369423Z custom operators as opaque (:func:`torch.compile` and 2024-12-18T01:36:56.8369839Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2024-12-18T01:36:56.8370222Z makes the implementation visible to these subsystems, allowing them 2024-12-18T01:36:56.8370407Z to optimize the triton kernel(s). 2024-12-18T01:36:56.8370553Z 2024-12-18T01:36:56.8370878Z Note that ``fn`` must only consist of calls to PyTorch-understood 2024-12-18T01:36:56.8371340Z operators and triton kernels. Any triton kernels called inside ``fn`` 2024-12-18T01:36:56.8371676Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2024-12-18T01:36:56.8371812Z 2024-12-18T01:36:56.8371964Z Args: 2024-12-18T01:36:56.8372346Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2024-12-18T01:36:56.8372717Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2024-12-18T01:36:56.8373026Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2024-12-18T01:36:56.8373446Z To avoid name collisions, please use your project name as the namespace; 2024-12-18T01:36:56.8373801Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2024-12-18T01:36:56.8374289Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2024-12-18T01:36:56.8374701Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2024-12-18T01:36:56.8375198Z it pessimistically assumes that all inputs to the operator are being mutated. 2024-12-18T01:36:56.8375527Z schema (None | str): A schema string for the operator. If None 2024-12-18T01:36:56.8375878Z (recommended) we'll infer a schema for the operator from its type 2024-12-18T01:36:56.8376244Z annotations. We recommend letting us infer a schema unless you 2024-12-18T01:36:56.8376436Z have a specific reason not to. 2024-12-18T01:36:56.8376695Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2024-12-18T01:36:56.8376827Z 2024-12-18T01:36:56.8376974Z Example:: 2024-12-18T01:36:56.8377120Z 2024-12-18T01:36:56.8377348Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:56.8377515Z >>> import torch 2024-12-18T01:36:56.8377765Z >>> from torch._library import triton_op, wrap_triton 2024-12-18T01:36:56.8377908Z >>> 2024-12-18T01:36:56.8378076Z >>> import triton 2024-12-18T01:36:56.8378273Z >>> from triton import language as tl 2024-12-18T01:36:56.8378426Z >>> 2024-12-18T01:36:56.8378576Z >>> @triton.jit 2024-12-18T01:36:56.8378741Z >>> def add_kernel( 2024-12-18T01:36:56.8378891Z >>> in_ptr0, 2024-12-18T01:36:56.8379034Z >>> in_ptr1, 2024-12-18T01:36:56.8379195Z >>> out_ptr, 2024-12-18T01:36:56.8379341Z >>> n_elements, 2024-12-18T01:36:56.8379539Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:36:56.8379673Z >>> ): 2024-12-18T01:36:56.8379854Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:36:56.8380058Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:36:56.8380305Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:36:56.8380498Z >>> mask = offsets < n_elements 2024-12-18T01:36:56.8380709Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:36:56.8380928Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:36:56.8381122Z >>> output = x + y 2024-12-18T01:36:56.8381358Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:36:56.8381502Z >>> 2024-12-18T01:36:56.8381714Z >>> @triton_op("mylib::add", mutates_args={}) 2024-12-18T01:36:56.8382033Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2024-12-18T01:36:56.8382220Z >>> output = torch.empty_like(x) 2024-12-18T01:36:56.8382399Z >>> n_elements = output.numel() 2024-12-18T01:36:56.8382546Z >>> 2024-12-18T01:36:56.8382701Z >>> def grid(meta): 2024-12-18T01:36:56.8382995Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:36:56.8383161Z >>> 2024-12-18T01:36:56.8383479Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2024-12-18T01:36:56.8383791Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2024-12-18T01:36:56.8383946Z >>> return output 2024-12-18T01:36:56.8384093Z >>> 2024-12-18T01:36:56.8384246Z >>> @torch.compile 2024-12-18T01:36:56.8384402Z >>> def f(x, y): 2024-12-18T01:36:56.8384591Z >>> return add(x, y) 2024-12-18T01:36:56.8384728Z >>> 2024-12-18T01:36:56.8384930Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:36:56.8385116Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:36:56.8385262Z >>> 2024-12-18T01:36:56.8385405Z >>> z = f(x, y) 2024-12-18T01:36:56.8385609Z >>> assert torch.allclose(z, x + y) 2024-12-18T01:36:56.8385742Z 2024-12-18T01:36:56.8385876Z 2024-12-18T01:36:56.8386330Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8386463Z 2024-12-18T01:36:56.8386673Z warnings.warn(msg) 2024-12-18T01:36:56.8386804Z 2024-12-18T01:36:56.8387123Z --- Parse Warning: 21 / 105 --- 2024-12-18T01:36:56.8388715Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=183. 2024-12-18T01:36:56.8389174Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8389508Z Allows capture of a triton kernel into a graph via make_fx or 2024-12-18T01:36:56.8389682Z non-strict ``torch.export``. 2024-12-18T01:36:56.8389828Z 2024-12-18T01:36:56.8390141Z These technologies perform Dispatcher-based tracing (via 2024-12-18T01:36:56.8390472Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2024-12-18T01:36:56.8390824Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2024-12-18T01:36:56.8391018Z can actually be traced into a graph. 2024-12-18T01:36:56.8391167Z 2024-12-18T01:36:56.8391527Z Please use this API together with :func:`torch.library.triton_op`. 2024-12-18T01:36:56.8391687Z 2024-12-18T01:36:56.8391826Z Examples: 2024-12-18T01:36:56.8391961Z 2024-12-18T01:36:56.8392131Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8392288Z >>> import torch 2024-12-18T01:36:56.8392454Z >>> import triton 2024-12-18T01:36:56.8392652Z >>> from triton import language as tl 2024-12-18T01:36:56.8392956Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2024-12-18T01:36:56.8393177Z >>> from torch.library import wrap_triton 2024-12-18T01:36:56.8393319Z >>> 2024-12-18T01:36:56.8393480Z >>> @triton.jit 2024-12-18T01:36:56.8393635Z >>> def add_kernel( 2024-12-18T01:36:56.8393781Z >>> in_ptr0, 2024-12-18T01:36:56.8393945Z >>> in_ptr1, 2024-12-18T01:36:56.8394092Z >>> out_ptr, 2024-12-18T01:36:56.8394294Z >>> n_elements, 2024-12-18T01:36:56.8394481Z >>> BLOCK_SIZE: "tl.constexpr", 2024-12-18T01:36:56.8394636Z >>> ): 2024-12-18T01:36:56.8394823Z >>> pid = tl.program_id(axis=0) 2024-12-18T01:36:56.8395009Z >>> block_start = pid * BLOCK_SIZE 2024-12-18T01:36:56.8395270Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2024-12-18T01:36:56.8395447Z >>> mask = offsets < n_elements 2024-12-18T01:36:56.8395756Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2024-12-18T01:36:56.8395964Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2024-12-18T01:36:56.8396130Z >>> output = x + y 2024-12-18T01:36:56.8396372Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2024-12-18T01:36:56.8396554Z >>> 2024-12-18T01:36:56.8396726Z >>> def add(x, y): 2024-12-18T01:36:56.8396917Z >>> output = torch.empty_like(x) 2024-12-18T01:36:56.8397118Z >>> n_elements = output.numel() 2024-12-18T01:36:56.8397258Z >>> 2024-12-18T01:36:56.8397419Z >>> def grid_fn(meta): 2024-12-18T01:36:56.8397723Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2024-12-18T01:36:56.8398079Z >>> 2024-12-18T01:36:56.8398438Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2024-12-18T01:36:56.8398594Z >>> return output 2024-12-18T01:36:56.8398749Z >>> 2024-12-18T01:36:56.8398938Z >>> x = torch.randn(3, device="cuda") 2024-12-18T01:36:56.8399123Z >>> y = torch.randn(3, device="cuda") 2024-12-18T01:36:56.8399306Z >>> gm = make_fx(add)(x, y) 2024-12-18T01:36:56.8399464Z >>> print(gm.code) 2024-12-18T01:36:56.8399660Z >>> # def forward(self, x_1, y_1): 2024-12-18T01:36:56.8400112Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2024-12-18T01:36:56.8400539Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2024-12-18T01:36:56.8400764Z >>> # kernel_idx = 0, constant_args_idx = 0, 2024-12-18T01:36:56.8400948Z >>> # grid = [(1, 1, 1)], kwargs = { 2024-12-18T01:36:56.8401208Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2024-12-18T01:36:56.8401403Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2024-12-18T01:36:56.8401558Z >>> # }) 2024-12-18T01:36:56.8401721Z >>> # return empty_like 2024-12-18T01:36:56.8401850Z 2024-12-18T01:36:56.8401996Z 2024-12-18T01:36:56.8402433Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8402845Z 2024-12-18T01:36:56.8403005Z warnings.warn(msg) 2024-12-18T01:36:56.8403135Z 2024-12-18T01:36:56.8403489Z --- Parse Warning: 22 / 105 --- 2024-12-18T01:36:56.8405173Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=330. 2024-12-18T01:36:56.8405654Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8405787Z 2024-12-18T01:36:56.8406157Z Raises an AssertionError if two items are not equal up to desired 2024-12-18T01:36:56.8406298Z precision. 2024-12-18T01:36:56.8406433Z 2024-12-18T01:36:56.8406740Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:36:56.8407044Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:36:56.8407382Z instead of this function for more consistent floating point 2024-12-18T01:36:56.8407534Z comparisons. 2024-12-18T01:36:56.8407741Z 2024-12-18T01:36:56.8408112Z The test verifies that the elements of `actual` and `desired` satisfy. 2024-12-18T01:36:56.8408245Z 2024-12-18T01:36:56.8408521Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2024-12-18T01:36:56.8408651Z 2024-12-18T01:36:56.8409063Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:36:56.8409464Z actual implementation in `assert_array_almost_equal` did up to rounding 2024-12-18T01:36:56.8409882Z vagaries. An exception is raised at conflicting values. For ndarrays this 2024-12-18T01:36:56.8410080Z delegates to assert_array_almost_equal 2024-12-18T01:36:56.8410212Z 2024-12-18T01:36:56.8410365Z Parameters 2024-12-18T01:36:56.8410507Z ---------- 2024-12-18T01:36:56.8410671Z actual : array_like 2024-12-18T01:36:56.8410882Z The object to check. 2024-12-18T01:36:56.8411040Z desired : array_like 2024-12-18T01:36:56.8411214Z The expected object. 2024-12-18T01:36:56.8411376Z decimal : int, optional 2024-12-18T01:36:56.8411576Z Desired precision, default is 7. 2024-12-18T01:36:56.8411736Z err_msg : str, optional 2024-12-18T01:36:56.8411992Z The error message to be printed in case of failure. 2024-12-18T01:36:56.8412172Z verbose : bool, optional 2024-12-18T01:36:56.8412554Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:56.8412700Z 2024-12-18T01:36:56.8412838Z Raises 2024-12-18T01:36:56.8412975Z ------ 2024-12-18T01:36:56.8413141Z AssertionError 2024-12-18T01:36:56.8413468Z If actual and desired are not equal up to specified precision. 2024-12-18T01:36:56.8413613Z 2024-12-18T01:36:56.8413754Z See Also 2024-12-18T01:36:56.8413909Z -------- 2024-12-18T01:36:56.8414323Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:56.8414531Z relative and/or absolute precision. 2024-12-18T01:36:56.8414945Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:56.8415079Z 2024-12-18T01:36:56.8415230Z Examples 2024-12-18T01:36:56.8415368Z -------- 2024-12-18T01:36:56.8415640Z >>> from torch._numpy.testing import assert_almost_equal 2024-12-18T01:36:56.8415879Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2024-12-18T01:36:56.8416170Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2024-12-18T01:36:56.8416364Z Traceback (most recent call last): 2024-12-18T01:36:56.8416501Z ... 2024-12-18T01:36:56.8416666Z AssertionError: 2024-12-18T01:36:56.8416871Z Arrays are not almost equal to 10 decimals 2024-12-18T01:36:56.8417024Z ACTUAL: 2.3333333333333 2024-12-18T01:36:56.8417192Z DESIRED: 2.33333334 2024-12-18T01:36:56.8417325Z 2024-12-18T01:36:56.8417589Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2024-12-18T01:36:56.8417790Z ... np.array([1.0,2.33333334]), decimal=9) 2024-12-18T01:36:56.8417974Z Traceback (most recent call last): 2024-12-18T01:36:56.8418129Z ... 2024-12-18T01:36:56.8418281Z AssertionError: 2024-12-18T01:36:56.8418492Z Arrays are not almost equal to 9 decimals 2024-12-18T01:36:56.8418631Z 2024-12-18T01:36:56.8418804Z Mismatched elements: 1 / 2 (50%) 2024-12-18T01:36:56.8419030Z Max absolute difference: 6.666699636781459e-09 2024-12-18T01:36:56.8419244Z Max relative difference: 2.8571569790287484e-09 2024-12-18T01:36:56.8419480Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:36:56.8419704Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2024-12-18T01:36:56.8419851Z 2024-12-18T01:36:56.8419984Z 2024-12-18T01:36:56.8420424Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8420568Z 2024-12-18T01:36:56.8420726Z warnings.warn(msg) 2024-12-18T01:36:56.8420873Z 2024-12-18T01:36:56.8421199Z --- Parse Warning: 23 / 105 --- 2024-12-18T01:36:56.8422929Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=455. 2024-12-18T01:36:56.8423404Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8423538Z 2024-12-18T01:36:56.8423935Z Raises an AssertionError if two items are not equal up to significant 2024-12-18T01:36:56.8424068Z digits. 2024-12-18T01:36:56.8424220Z 2024-12-18T01:36:56.8424506Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:36:56.8424806Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:36:56.8425163Z instead of this function for more consistent floating point 2024-12-18T01:36:56.8425316Z comparisons. 2024-12-18T01:36:56.8425463Z 2024-12-18T01:36:56.8425777Z Given two numbers, check that they are approximately equal. 2024-12-18T01:36:56.8426159Z Approximately equal is defined as the number of significant digits 2024-12-18T01:36:56.8426303Z that agree. 2024-12-18T01:36:56.8426430Z 2024-12-18T01:36:56.8426585Z Parameters 2024-12-18T01:36:56.8426721Z ---------- 2024-12-18T01:36:56.8426912Z actual : scalar 2024-12-18T01:36:56.8427075Z The object to check. 2024-12-18T01:36:56.8427221Z desired : scalar 2024-12-18T01:36:56.8427393Z The expected object. 2024-12-18T01:36:56.8427558Z significant : int, optional 2024-12-18T01:36:56.8427762Z Desired precision, default is 7. 2024-12-18T01:36:56.8427919Z err_msg : str, optional 2024-12-18T01:36:56.8428173Z The error message to be printed in case of failure. 2024-12-18T01:36:56.8428348Z verbose : bool, optional 2024-12-18T01:36:56.8428725Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:56.8428870Z 2024-12-18T01:36:56.8429005Z Raises 2024-12-18T01:36:56.8429158Z ------ 2024-12-18T01:36:56.8429303Z AssertionError 2024-12-18T01:36:56.8429631Z If actual and desired are not equal up to specified precision. 2024-12-18T01:36:56.8429775Z 2024-12-18T01:36:56.8429912Z See Also 2024-12-18T01:36:56.8430065Z -------- 2024-12-18T01:36:56.8430475Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:56.8430680Z relative and/or absolute precision. 2024-12-18T01:36:56.8431050Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:56.8431184Z 2024-12-18T01:36:56.8431339Z Examples 2024-12-18T01:36:56.8431476Z -------- 2024-12-18T01:36:56.8431944Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2024-12-18T01:36:56.8432397Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2024-12-18T01:36:56.8432585Z ... significant=8) 2024-12-18T01:36:56.8433029Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2024-12-18T01:36:56.8433212Z ... significant=8) 2024-12-18T01:36:56.8433411Z Traceback (most recent call last): 2024-12-18T01:36:56.8433544Z ... 2024-12-18T01:36:56.8433698Z AssertionError: 2024-12-18T01:36:56.8433928Z Items are not equal to 8 significant digits: 2024-12-18T01:36:56.8434074Z ACTUAL: 1.234567e-21 2024-12-18T01:36:56.8434241Z DESIRED: 1.2345672e-21 2024-12-18T01:36:56.8434373Z 2024-12-18T01:36:56.8434641Z the evaluated condition that raises the exception is 2024-12-18T01:36:56.8434792Z 2024-12-18T01:36:56.8435075Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2024-12-18T01:36:56.8435224Z True 2024-12-18T01:36:56.8435351Z 2024-12-18T01:36:56.8435497Z 2024-12-18T01:36:56.8436027Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8436197Z 2024-12-18T01:36:56.8436372Z warnings.warn(msg) 2024-12-18T01:36:56.8436506Z 2024-12-18T01:36:56.8436842Z --- Parse Warning: 24 / 105 --- 2024-12-18T01:36:56.8438523Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=734. 2024-12-18T01:36:56.8438993Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8439125Z 2024-12-18T01:36:56.8439486Z Raises an AssertionError if two array_like objects are not equal. 2024-12-18T01:36:56.8439635Z 2024-12-18T01:36:56.8440016Z Given two array_like objects, check that the shape is equal and all 2024-12-18T01:36:56.8440412Z elements of these objects are equal (but see the Notes for the special 2024-12-18T01:36:56.8440752Z handling of a scalar). An exception is raised at shape mismatch or 2024-12-18T01:36:56.8441138Z conflicting values. In contrast to the standard usage in numpy, NaNs 2024-12-18T01:36:56.8441534Z are compared like numbers, no assertion is raised if both objects have 2024-12-18T01:36:56.8441741Z NaNs in the same positions. 2024-12-18T01:36:56.8441890Z 2024-12-18T01:36:56.8442286Z The usual caution for verifying equality with floating point numbers is 2024-12-18T01:36:56.8442443Z advised. 2024-12-18T01:36:56.8442577Z 2024-12-18T01:36:56.8442726Z Parameters 2024-12-18T01:36:56.8442878Z ---------- 2024-12-18T01:36:56.8443019Z x : array_like 2024-12-18T01:36:56.8443213Z The actual object to check. 2024-12-18T01:36:56.8443357Z y : array_like 2024-12-18T01:36:56.8443542Z The desired, expected object. 2024-12-18T01:36:56.8443714Z err_msg : str, optional 2024-12-18T01:36:56.8444004Z The error message to be printed in case of failure. 2024-12-18T01:36:56.8444189Z verbose : bool, optional 2024-12-18T01:36:56.8444535Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:56.8444706Z strict : bool, optional 2024-12-18T01:36:56.8445048Z If True, raise an AssertionError when either the shape or the data 2024-12-18T01:36:56.8445345Z type of the array_like objects does not match. The special 2024-12-18T01:36:56.8445752Z handling for scalars mentioned in the Notes section is disabled. 2024-12-18T01:36:56.8445889Z 2024-12-18T01:36:56.8446039Z Raises 2024-12-18T01:36:56.8446179Z ------ 2024-12-18T01:36:56.8446331Z AssertionError 2024-12-18T01:36:56.8446565Z If actual and desired objects are not equal. 2024-12-18T01:36:56.8446695Z 2024-12-18T01:36:56.8446848Z See Also 2024-12-18T01:36:56.8446987Z -------- 2024-12-18T01:36:56.8447394Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:56.8447622Z relative and/or absolute precision. 2024-12-18T01:36:56.8447975Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:56.8448123Z 2024-12-18T01:36:56.8448257Z Notes 2024-12-18T01:36:56.8448404Z ----- 2024-12-18T01:36:56.8448711Z When one of `x` and `y` is a scalar and the other is array_like, the 2024-12-18T01:36:56.8449094Z function checks that each element of the array_like object is equal to 2024-12-18T01:36:56.8449572Z the scalar. This behaviour can be disabled with the `strict` parameter. 2024-12-18T01:36:56.8449744Z 2024-12-18T01:36:56.8450072Z Examples 2024-12-18T01:36:56.8450326Z -------- 2024-12-18T01:36:56.8450590Z The first assert does not raise an exception: 2024-12-18T01:36:56.8450819Z 2024-12-18T01:36:56.8451121Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2024-12-18T01:36:56.8451440Z ... [np.exp(0),2.33333, np.nan]) 2024-12-18T01:36:56.8451743Z 2024-12-18T01:36:56.8452171Z Use `assert_allclose` or one of the nulp (number of floating point values) 2024-12-18T01:36:56.8452445Z functions for these cases instead: 2024-12-18T01:36:56.8452614Z 2024-12-18T01:36:56.8452936Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2024-12-18T01:36:56.8453223Z ... [1, np.sqrt(np.pi)**2, np.nan], 2024-12-18T01:36:56.8453521Z ... rtol=1e-10, atol=0) 2024-12-18T01:36:56.8453693Z 2024-12-18T01:36:56.8454082Z As mentioned in the Notes section, `assert_array_equal` has special 2024-12-18T01:36:56.8454569Z handling for scalars. Here the test checks that each value in `x` is 3: 2024-12-18T01:36:56.8454716Z 2024-12-18T01:36:56.8455035Z >>> x = np.full((2, 5), fill_value=3) 2024-12-18T01:36:56.8455304Z >>> np.testing.assert_array_equal(x, 3) 2024-12-18T01:36:56.8455472Z 2024-12-18T01:36:56.8475365Z Use `strict` to raise an AssertionError when comparing a scalar with an 2024-12-18T01:36:56.8475719Z array: 2024-12-18T01:36:56.8475870Z 2024-12-18T01:36:56.8476139Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2024-12-18T01:36:56.8476345Z Traceback (most recent call last): 2024-12-18T01:36:56.8476478Z ... 2024-12-18T01:36:56.8476634Z AssertionError: 2024-12-18T01:36:56.8476949Z Arrays are not equal 2024-12-18T01:36:56.8477094Z 2024-12-18T01:36:56.8477269Z (shapes (2, 5), () mismatch) 2024-12-18T01:36:56.8477437Z x: torch.ndarray([[3, 3, 3, 3, 3], 2024-12-18T01:36:56.8477584Z [3, 3, 3, 3, 3]]) 2024-12-18T01:36:56.8477752Z y: torch.ndarray(3) 2024-12-18T01:36:56.8477884Z 2024-12-18T01:36:56.8478274Z The `strict` parameter also ensures that the array data types match: 2024-12-18T01:36:56.8478416Z 2024-12-18T01:36:56.8478575Z >>> x = np.array([2, 2, 2]) 2024-12-18T01:36:56.8478795Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2024-12-18T01:36:56.8479087Z >>> np.testing.assert_array_equal(x, y, strict=True) 2024-12-18T01:36:56.8479291Z Traceback (most recent call last): 2024-12-18T01:36:56.8479427Z ... 2024-12-18T01:36:56.8479595Z AssertionError: 2024-12-18T01:36:56.8479749Z Arrays are not equal 2024-12-18T01:36:56.8479888Z 2024-12-18T01:36:56.8480142Z (dtypes dtype("int64"), dtype("float32") mismatch) 2024-12-18T01:36:56.8480310Z x: torch.ndarray([2, 2, 2]) 2024-12-18T01:36:56.8480494Z y: torch.ndarray([2., 2., 2.]) 2024-12-18T01:36:56.8480624Z 2024-12-18T01:36:56.8481067Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8481212Z 2024-12-18T01:36:56.8481372Z warnings.warn(msg) 2024-12-18T01:36:56.8481516Z 2024-12-18T01:36:56.8481887Z --- Parse Warning: 25 / 105 --- 2024-12-18T01:36:56.8483643Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=840. 2024-12-18T01:36:56.8484106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8484240Z 2024-12-18T01:36:56.8484630Z Raises an AssertionError if two objects are not equal up to desired 2024-12-18T01:36:56.8484777Z precision. 2024-12-18T01:36:56.8484925Z 2024-12-18T01:36:56.8485228Z .. note:: It is recommended to use one of `assert_allclose`, 2024-12-18T01:36:56.8485532Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2024-12-18T01:36:56.8485862Z instead of this function for more consistent floating point 2024-12-18T01:36:56.8486019Z comparisons. 2024-12-18T01:36:56.8486169Z 2024-12-18T01:36:56.8486586Z The test verifies identical shapes and that the elements of ``actual`` and 2024-12-18T01:36:56.8486763Z ``desired`` satisfy. 2024-12-18T01:36:56.8486897Z 2024-12-18T01:36:56.8487156Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2024-12-18T01:36:56.8487307Z 2024-12-18T01:36:56.8487701Z That is a looser test than originally documented, but agrees with what the 2024-12-18T01:36:56.8488139Z actual implementation did up to rounding vagaries. An exception is raised 2024-12-18T01:36:56.8488549Z at shape mismatch or conflicting values. In contrast to the standard usage 2024-12-18T01:36:56.8488943Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2024-12-18T01:36:56.8489146Z objects have NaNs in the same positions. 2024-12-18T01:36:56.8489285Z 2024-12-18T01:36:56.8489442Z Parameters 2024-12-18T01:36:56.8489587Z ---------- 2024-12-18T01:36:56.8489748Z x : array_like 2024-12-18T01:36:56.8489921Z The actual object to check. 2024-12-18T01:36:56.8490107Z y : array_like 2024-12-18T01:36:56.8490302Z The desired, expected object. 2024-12-18T01:36:56.8490469Z decimal : int, optional 2024-12-18T01:36:56.8490676Z Desired precision, default is 6. 2024-12-18T01:36:56.8490832Z err_msg : str, optional 2024-12-18T01:36:56.8491088Z The error message to be printed in case of failure. 2024-12-18T01:36:56.8491268Z verbose : bool, optional 2024-12-18T01:36:56.8491653Z If True, the conflicting values are appended to the error message. 2024-12-18T01:36:56.8491807Z 2024-12-18T01:36:56.8491946Z Raises 2024-12-18T01:36:56.8492099Z ------ 2024-12-18T01:36:56.8492255Z AssertionError 2024-12-18T01:36:56.8492587Z If actual and desired are not equal up to specified precision. 2024-12-18T01:36:56.8492739Z 2024-12-18T01:36:56.8492878Z See Also 2024-12-18T01:36:56.8493036Z -------- 2024-12-18T01:36:56.8493444Z assert_allclose: Compare two array_like objects for equality with desired 2024-12-18T01:36:56.8493658Z relative and/or absolute precision. 2024-12-18T01:36:56.8494065Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2024-12-18T01:36:56.8494202Z 2024-12-18T01:36:56.8494359Z Examples 2024-12-18T01:36:56.8494500Z -------- 2024-12-18T01:36:56.8494718Z the first assert does not raise an exception 2024-12-18T01:36:56.8494866Z 2024-12-18T01:36:56.8495149Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2024-12-18T01:36:56.8495347Z ... [1.0,2.333,np.nan]) 2024-12-18T01:36:56.8495480Z 2024-12-18T01:36:56.8495786Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:36:56.8495993Z ... [1.0,2.33339,np.nan], decimal=5) 2024-12-18T01:36:56.8496174Z Traceback (most recent call last): 2024-12-18T01:36:56.8496324Z ... 2024-12-18T01:36:56.8496481Z AssertionError: 2024-12-18T01:36:56.8496694Z Arrays are not almost equal to 5 decimals 2024-12-18T01:36:56.8496841Z 2024-12-18T01:36:56.8497019Z Mismatched elements: 1 / 3 (33.3%) 2024-12-18T01:36:56.8497247Z Max absolute difference: 5.999999999994898e-05 2024-12-18T01:36:56.8497455Z Max relative difference: 2.5713661239633743e-05 2024-12-18T01:36:56.8497739Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:36:56.8498191Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2024-12-18T01:36:56.8498345Z 2024-12-18T01:36:56.8498644Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2024-12-18T01:36:56.8498841Z ... [1.0,2.33333, 5], decimal=5) 2024-12-18T01:36:56.8499036Z Traceback (most recent call last): 2024-12-18T01:36:56.8499171Z ... 2024-12-18T01:36:56.8499338Z AssertionError: 2024-12-18T01:36:56.8499533Z Arrays are not almost equal to 5 decimals 2024-12-18T01:36:56.8499678Z 2024-12-18T01:36:56.8499863Z x and y nan location mismatch: 2024-12-18T01:36:56.8500131Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2024-12-18T01:36:56.8500500Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2024-12-18T01:36:56.8500632Z 2024-12-18T01:36:56.8500783Z 2024-12-18T01:36:56.8501222Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8501357Z 2024-12-18T01:36:56.8501531Z warnings.warn(msg) 2024-12-18T01:36:56.8501669Z 2024-12-18T01:36:56.8502022Z --- Parse Warning: 26 / 105 --- 2024-12-18T01:36:56.8504015Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1790. 2024-12-18T01:36:56.8504489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8504922Z Context manager that resets warning registry for catching warnings 2024-12-18T01:36:56.8505058Z 2024-12-18T01:36:56.8505490Z Warnings can be slippery, because, whenever a warning is triggered, Python 2024-12-18T01:36:56.8505879Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2024-12-18T01:36:56.8506307Z it impossible to retrigger the warning in this module, whatever you put in 2024-12-18T01:36:56.8506772Z the warnings filters. This context manager accepts a sequence of `modules` 2024-12-18T01:36:56.8507016Z as a keyword argument to its constructor and: 2024-12-18T01:36:56.8507149Z 2024-12-18T01:36:56.8507538Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2024-12-18T01:36:56.8507696Z on entry; 2024-12-18T01:36:56.8508010Z * resets ``__warningregistry__`` to its previous state on exit. 2024-12-18T01:36:56.8508161Z 2024-12-18T01:36:56.8508548Z This makes it possible to trigger any warning afresh inside the context 2024-12-18T01:36:56.8508914Z manager without disturbing the state of warnings outside. 2024-12-18T01:36:56.8509070Z 2024-12-18T01:36:56.8509473Z For compatibility with Python 3.0, please consider all arguments to be 2024-12-18T01:36:56.8509641Z keyword-only. 2024-12-18T01:36:56.8509775Z 2024-12-18T01:36:56.8509938Z Parameters 2024-12-18T01:36:56.8510082Z ---------- 2024-12-18T01:36:56.8510248Z record : bool, optional 2024-12-18T01:36:56.8510582Z Specifies whether warnings should be captured by a custom 2024-12-18T01:36:56.8510989Z implementation of ``warnings.showwarning()`` and be appended to a list 2024-12-18T01:36:56.8511361Z returned by the context manager. Otherwise None is returned by the 2024-12-18T01:36:56.8511751Z context manager. The objects appended to the list are arguments whose 2024-12-18T01:36:56.8512040Z attributes mirror the arguments to ``showwarning()``. 2024-12-18T01:36:56.8512232Z modules : sequence, optional 2024-12-18T01:36:56.8512615Z Sequence of modules for which to reset warnings registry on entry and 2024-12-18T01:36:56.8512963Z restore on exit. To work correctly, all 'ignore' filters should 2024-12-18T01:36:56.8513149Z filter by one of these modules. 2024-12-18T01:36:56.8513304Z 2024-12-18T01:36:56.8513450Z Examples 2024-12-18T01:36:56.8513598Z -------- 2024-12-18T01:36:56.8513769Z >>> import warnings 2024-12-18T01:36:56.8514079Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2024-12-18T01:36:56.8514297Z ... modules=[np.core.fromnumeric]): 2024-12-18T01:36:56.8514500Z ... warnings.simplefilter('always') 2024-12-18T01:36:56.8514902Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2024-12-18T01:36:56.8515189Z ... # do something that raises a warning but ignore those in 2024-12-18T01:36:56.8515366Z ... # np.core.fromnumeric 2024-12-18T01:36:56.8515519Z 2024-12-18T01:36:56.8516065Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8516211Z 2024-12-18T01:36:56.8516376Z warnings.warn(msg) 2024-12-18T01:36:56.8516522Z 2024-12-18T01:36:56.8516849Z --- Parse Warning: 27 / 105 --- 2024-12-18T01:36:56.8518507Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2024-12-18T01:36:56.8518985Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8519340Z Applies a 1D convolution over a quantized input signal composed of 2024-12-18T01:36:56.8519537Z several quantized input planes. 2024-12-18T01:36:56.8519712Z 2024-12-18T01:36:56.8520087Z For details on input arguments, parameters, and implementation see 2024-12-18T01:36:56.8520257Z :class:`~torch.nn.Conv1d`. 2024-12-18T01:36:56.8520393Z 2024-12-18T01:36:56.8520558Z .. note:: 2024-12-18T01:36:56.8520889Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2024-12-18T01:36:56.8521033Z 2024-12-18T01:36:56.8521176Z .. note:: 2024-12-18T01:36:56.8521500Z Only `torch.quint8` is supported for the input data type. 2024-12-18T01:36:56.8521644Z 2024-12-18T01:36:56.8521774Z 2024-12-18T01:36:56.8521932Z Attributes: 2024-12-18T01:36:56.8522288Z weight (Tensor): packed tensor derived from the learnable weight 2024-12-18T01:36:56.8522467Z parameter. 2024-12-18T01:36:56.8522706Z scale (Tensor): scalar for the output scale 2024-12-18T01:36:56.8522977Z zero_point (Tensor): scalar for the output zero point 2024-12-18T01:36:56.8523118Z 2024-12-18T01:36:56.8523370Z See :class:`~torch.nn.Conv1d` for other attributes. 2024-12-18T01:36:56.8523550Z 2024-12-18T01:36:56.8523701Z Examples:: 2024-12-18T01:36:56.8523838Z 2024-12-18T01:36:56.8524097Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2024-12-18T01:36:56.8524313Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2024-12-18T01:36:56.8524507Z >>> input = torch.randn(20, 16, 100) 2024-12-18T01:36:56.8524687Z >>> # quantize input to quint8 2024-12-18T01:36:56.8524859Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8525217Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2024-12-18T01:36:56.8525422Z ... dtype=torch.quint8) 2024-12-18T01:36:56.8525595Z >>> output = m(q_input) 2024-12-18T01:36:56.8525726Z 2024-12-18T01:36:56.8525873Z 2024-12-18T01:36:56.8526313Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8526444Z 2024-12-18T01:36:56.8526615Z warnings.warn(msg) 2024-12-18T01:36:56.8526748Z 2024-12-18T01:36:56.8527064Z --- Parse Warning: 28 / 105 --- 2024-12-18T01:36:56.8528678Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2024-12-18T01:36:56.8529150Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8529358Z A quantized long short-term memory (LSTM). 2024-12-18T01:36:56.8529486Z 2024-12-18T01:36:56.8529974Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2024-12-18T01:36:56.8530108Z 2024-12-18T01:36:56.8530262Z Attributes: 2024-12-18T01:36:56.8530462Z layers : instances of the `_LSTMLayer` 2024-12-18T01:36:56.8530610Z 2024-12-18T01:36:56.8530748Z .. note:: 2024-12-18T01:36:56.8531116Z To access the weights and biases, you need to access them per layer. 2024-12-18T01:36:56.8531460Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2024-12-18T01:36:56.8531591Z 2024-12-18T01:36:56.8531754Z Examples:: 2024-12-18T01:36:56.8531910Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8532083Z >>> custom_module_config = { 2024-12-18T01:36:56.8532328Z ... 'float_to_observed_custom_module_class': { 2024-12-18T01:36:56.8532530Z ... nn.LSTM: nn.quantizable.LSTM, 2024-12-18T01:36:56.8532680Z ... }, 2024-12-18T01:36:56.8532925Z ... 'observed_to_quantized_custom_module_class': { 2024-12-18T01:36:56.8533175Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2024-12-18T01:36:56.8533312Z ... } 2024-12-18T01:36:56.8533505Z ... } 2024-12-18T01:36:56.8533887Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2024-12-18T01:36:56.8534251Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2024-12-18T01:36:56.8534399Z 2024-12-18T01:36:56.8534841Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8534973Z 2024-12-18T01:36:56.8535143Z warnings.warn(msg) 2024-12-18T01:36:56.8535276Z 2024-12-18T01:36:56.8535626Z --- Parse Warning: 29 / 105 --- 2024-12-18T01:36:56.8537569Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2024-12-18T01:36:56.8538041Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8538337Z Squashes the sparse masks into the appropriate tensors. 2024-12-18T01:36:56.8538471Z 2024-12-18T01:36:56.8538864Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2024-12-18T01:36:56.8539178Z the module will have a `sparse_params` dict attached to it. 2024-12-18T01:36:56.8539325Z 2024-12-18T01:36:56.8539464Z Args: 2024-12-18T01:36:56.8539796Z params_to_keep: List of keys to save in the module or a dict 2024-12-18T01:36:56.8540064Z representing the modules and keys that will have 2024-12-18T01:36:56.8540264Z sparsity parameters saved 2024-12-18T01:36:56.8540643Z params_to_keep_per_layer: Dict to specify the params that should be 2024-12-18T01:36:56.8540892Z saved for specific layers. The keys in the dict 2024-12-18T01:36:56.8541165Z should be the module fqn, while the values should 2024-12-18T01:36:56.8541431Z be a list of strings with the names of the variables 2024-12-18T01:36:56.8541649Z to save in the `sparse_params` 2024-12-18T01:36:56.8541783Z 2024-12-18T01:36:56.8541929Z Examples: 2024-12-18T01:36:56.8542155Z >>> # xdoctest: +SKIP("locals are undefined") 2024-12-18T01:36:56.8542343Z >>> # Don't save any sparse params 2024-12-18T01:36:56.8542543Z >>> sparsifier.squash_mask() 2024-12-18T01:36:56.8542772Z >>> hasattr(model.submodule1, 'sparse_params') 2024-12-18T01:36:56.8542919Z False 2024-12-18T01:36:56.8543052Z 2024-12-18T01:36:56.8543240Z >>> # Keep sparse params per layer 2024-12-18T01:36:56.8543434Z >>> sparsifier.squash_mask( 2024-12-18T01:36:56.8543618Z ... params_to_keep_per_layer={ 2024-12-18T01:36:56.8543843Z ... 'submodule1.linear1': ('foo', 'bar'), 2024-12-18T01:36:56.8544047Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:36:56.8544213Z ... }) 2024-12-18T01:36:56.8544510Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:36:56.8544667Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:56.8544951Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:36:56.8545098Z {'baz': 0.1} 2024-12-18T01:36:56.8545242Z 2024-12-18T01:36:56.8545446Z >>> # Keep sparse params for all layers 2024-12-18T01:36:56.8545737Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2024-12-18T01:36:56.8546005Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:36:56.8546159Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:56.8546441Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:36:56.8546595Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:56.8546777Z 2024-12-18T01:36:56.8547107Z >>> # Keep some sparse params for all layers, and specific ones for 2024-12-18T01:36:56.8547271Z >>> # some other layers 2024-12-18T01:36:56.8547463Z >>> sparsifier.squash_mask( 2024-12-18T01:36:56.8547653Z ... params_to_keep=('foo', 'bar'), 2024-12-18T01:36:56.8547845Z ... params_to_keep_per_layer={ 2024-12-18T01:36:56.8548081Z ... 'submodule2.linear42': ('baz',) 2024-12-18T01:36:56.8548231Z ... }) 2024-12-18T01:36:56.8548491Z >>> print(model.submodule1.linear1.sparse_params) 2024-12-18T01:36:56.8548644Z {'foo': 42, 'bar': 24} 2024-12-18T01:36:56.8548926Z >>> print(model.submodule2.linear42.sparse_params) 2024-12-18T01:36:56.8549099Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2024-12-18T01:36:56.8549242Z 2024-12-18T01:36:56.8549682Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8549815Z 2024-12-18T01:36:56.8550020Z warnings.warn(msg) 2024-12-18T01:36:56.8550151Z 2024-12-18T01:36:56.8550468Z --- Parse Warning: 30 / 105 --- 2024-12-18T01:36:56.8552352Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2024-12-18T01:36:56.8552820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8552954Z 2024-12-18T01:36:56.8553388Z Config object that specifies the supported data types passed as arguments to 2024-12-18T01:36:56.8553807Z quantize ops in the reference model spec, for input and output activations, 2024-12-18T01:36:56.8553960Z weights, and biases. 2024-12-18T01:36:56.8554103Z 2024-12-18T01:36:56.8554370Z For example, consider the following reference model: 2024-12-18T01:36:56.8554511Z 2024-12-18T01:36:56.8554771Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2024-12-18T01:36:56.8554903Z 2024-12-18T01:36:56.8555281Z The pattern in the square brackets refers to the reference pattern of 2024-12-18T01:36:56.8555772Z statically quantized linear. Setting the input dtype as `torch.quint8` 2024-12-18T01:36:56.8556177Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2024-12-18T01:36:56.8556560Z to the first quantize op (quant1). Similarly, setting the output dtype as 2024-12-18T01:36:56.8556949Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2024-12-18T01:36:56.8557120Z the second quantize op (quant2). 2024-12-18T01:36:56.8557250Z 2024-12-18T01:36:56.8557627Z Note that the dtype here does not refer to the interface dtypes of the 2024-12-18T01:36:56.8557974Z op. For example, the "input dtype" here is not the dtype of the input 2024-12-18T01:36:56.8558350Z tensor passed to the quantized linear op. Though it can still be the 2024-12-18T01:36:56.8558686Z same as the interface dtype, this is not always the case, e.g. the 2024-12-18T01:36:56.8559108Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2024-12-18T01:36:56.8559472Z specified in the DTypeConfig would still be quint8. The semantics of 2024-12-18T01:36:56.8559827Z dtypes here are the same as the semantics of the dtypes specified in 2024-12-18T01:36:56.8559980Z the observers. 2024-12-18T01:36:56.8560109Z 2024-12-18T01:36:56.8560466Z These dtypes are matched against the ones specified in the user's 2024-12-18T01:36:56.8560826Z QConfig. If there is a match, and the QConfig satisfies the constraints 2024-12-18T01:36:56.8561200Z specified in the DTypeConfig (if any), then we will quantize the given 2024-12-18T01:36:56.8561593Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2024-12-18T01:36:56.8561805Z the pattern will not be quantized. 2024-12-18T01:36:56.8561941Z 2024-12-18T01:36:56.8562100Z Example usage:: 2024-12-18T01:36:56.8562242Z 2024-12-18T01:36:56.8562413Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:36:56.8562591Z >>> dtype_config1 = DTypeConfig( 2024-12-18T01:36:56.8562771Z ... input_dtype=torch.quint8, 2024-12-18T01:36:56.8562951Z ... output_dtype=torch.quint8, 2024-12-18T01:36:56.8563248Z ... weight_dtype=torch.qint8, 2024-12-18T01:36:56.8563415Z ... bias_dtype=torch.float) 2024-12-18T01:36:56.8563546Z 2024-12-18T01:36:56.8563732Z >>> dtype_config2 = DTypeConfig( 2024-12-18T01:36:56.8563943Z ... input_dtype=DTypeWithConstraints( 2024-12-18T01:36:56.8564113Z ... dtype=torch.quint8, 2024-12-18T01:36:56.8564283Z ... quant_min_lower_bound=0, 2024-12-18T01:36:56.8564477Z ... quant_max_upper_bound=255, 2024-12-18T01:36:56.8564614Z ... ), 2024-12-18T01:36:56.8564826Z ... output_dtype=DTypeWithConstraints( 2024-12-18T01:36:56.8565032Z ... dtype=torch.quint8, 2024-12-18T01:36:56.8565209Z ... quant_min_lower_bound=0, 2024-12-18T01:36:56.8565396Z ... quant_max_upper_bound=255, 2024-12-18T01:36:56.8565530Z ... ), 2024-12-18T01:36:56.8565740Z ... weight_dtype=DTypeWithConstraints( 2024-12-18T01:36:56.8565916Z ... dtype=torch.qint8, 2024-12-18T01:36:56.8566097Z ... quant_min_lower_bound=-128, 2024-12-18T01:36:56.8566283Z ... quant_max_upper_bound=127, 2024-12-18T01:36:56.8566416Z ... ), 2024-12-18T01:36:56.8566595Z ... bias_dtype=torch.float) 2024-12-18T01:36:56.8566724Z 2024-12-18T01:36:56.8566902Z >>> dtype_config1.input_dtype 2024-12-18T01:36:56.8567057Z torch.quint8 2024-12-18T01:36:56.8567185Z 2024-12-18T01:36:56.8567377Z >>> dtype_config2.input_dtype 2024-12-18T01:36:56.8567524Z torch.quint8 2024-12-18T01:36:56.8567650Z 2024-12-18T01:36:56.8567883Z >>> dtype_config2.input_dtype_with_constraints 2024-12-18T01:36:56.8568842Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2024-12-18T01:36:56.8568983Z 2024-12-18T01:36:56.8569424Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8569554Z 2024-12-18T01:36:56.8569719Z warnings.warn(msg) 2024-12-18T01:36:56.8569852Z 2024-12-18T01:36:56.8570174Z --- Parse Warning: 31 / 105 --- 2024-12-18T01:36:56.8572461Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2024-12-18T01:36:56.8572935Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8573103Z 2024-12-18T01:36:56.8573581Z Takes in optional filter values and generates two tables with desired information. 2024-12-18T01:36:56.8573708Z 2024-12-18T01:36:56.8574069Z The generated tables are presented in both a list-of-lists format 2024-12-18T01:36:56.8574212Z 2024-12-18T01:36:56.8574564Z The reason for the two tables are that they handle different things: 2024-12-18T01:36:56.8574844Z 1.) the first table handles all tensor level information 2024-12-18T01:36:56.8575216Z 2.) the second table handles and displays all channel based information 2024-12-18T01:36:56.8575348Z 2024-12-18T01:36:56.8575917Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:36:56.8576497Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:36:56.8577166Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:36:56.8577295Z 2024-12-18T01:36:56.8577462Z Tensor table columns: 2024-12-18T01:36:56.8577782Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:56.8578031Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:36:56.8578172Z 2024-12-18T01:36:56.8578372Z Per-Channel table columns: 2024-12-18T01:36:56.8578761Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:56.8579029Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:36:56.8579168Z 2024-12-18T01:36:56.8579300Z Args: 2024-12-18T01:36:56.8579755Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:36:56.8579946Z contain this filter substring 2024-12-18T01:36:56.8580214Z Default = "", results in all the features being printed 2024-12-18T01:36:56.8580706Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:56.8581129Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:56.8581269Z 2024-12-18T01:36:56.8581450Z Returns a dictionary with two keys: 2024-12-18T01:36:56.8581737Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2024-12-18T01:36:56.8581945Z "tensor_level_info", "channel_level_info" 2024-12-18T01:36:56.8582120Z Each key maps to a tuple with: 2024-12-18T01:36:56.8582316Z A list of the headers of each table 2024-12-18T01:36:56.8582614Z A list of lists containing the table information row by row 2024-12-18T01:36:56.8582897Z The 0th index row will contain the headers of the columns 2024-12-18T01:36:56.8583106Z The rest of the rows will contain data 2024-12-18T01:36:56.8583237Z 2024-12-18T01:36:56.8583391Z Example Use: 2024-12-18T01:36:56.8583598Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8583856Z >>> mod_report_visualizer.generate_filtered_tables( 2024-12-18T01:36:56.8584051Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:56.8584225Z ... module_fqn_filter = "block1" 2024-12-18T01:36:56.8584711Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2024-12-18T01:36:56.8584838Z 2024-12-18T01:36:56.8585282Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8585410Z 2024-12-18T01:36:56.8585568Z warnings.warn(msg) 2024-12-18T01:36:56.8585705Z 2024-12-18T01:36:56.8586011Z --- Parse Warning: 32 / 105 --- 2024-12-18T01:36:56.8588341Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2024-12-18T01:36:56.8588852Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8588991Z 2024-12-18T01:36:56.8589470Z Takes in optional filter values and prints out formatted tables of the information. 2024-12-18T01:36:56.8589608Z 2024-12-18T01:36:56.8590209Z The reason for the two tables printed out instead of one large one are that they handle different things: 2024-12-18T01:36:56.8590482Z 1.) the first table handles all tensor level information 2024-12-18T01:36:56.8590861Z 2.) the second table handles and displays all channel based information 2024-12-18T01:36:56.8590991Z 2024-12-18T01:36:56.8591568Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2024-12-18T01:36:56.8592175Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2024-12-18T01:36:56.8592814Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2024-12-18T01:36:56.8592941Z 2024-12-18T01:36:56.8593096Z Tensor table columns: 2024-12-18T01:36:56.8593430Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:56.8593702Z ---- --------- --------- --------- --------- --------- 2024-12-18T01:36:56.8593841Z 2024-12-18T01:36:56.8594013Z Per-Channel table columns: 2024-12-18T01:36:56.8594143Z 2024-12-18T01:36:56.8594523Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2024-12-18T01:36:56.8594785Z ---- --------- ------- --------- --------- --------- --------- 2024-12-18T01:36:56.8594924Z 2024-12-18T01:36:56.8595061Z Args: 2024-12-18T01:36:56.8595524Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:36:56.8595816Z contain this filter substring 2024-12-18T01:36:56.8596095Z Default = "", results in all the features being printed 2024-12-18T01:36:56.8596558Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:56.8596986Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:56.8597134Z 2024-12-18T01:36:56.8597279Z Example Use: 2024-12-18T01:36:56.8597499Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8597772Z >>> mod_report_visualizer.generate_table_visualization( 2024-12-18T01:36:56.8598155Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:56.8598347Z ... module_fqn_filter = "block1" 2024-12-18T01:36:56.8598482Z ... ) 2024-12-18T01:36:56.8598824Z >>> # prints out neatly formatted table with per_channel_min info 2024-12-18T01:36:56.8599025Z >>> # for all modules in block 1 of the model 2024-12-18T01:36:56.8599159Z 2024-12-18T01:36:56.8599605Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8599734Z 2024-12-18T01:36:56.8599906Z warnings.warn(msg) 2024-12-18T01:36:56.8600034Z 2024-12-18T01:36:56.8600353Z --- Parse Warning: 33 / 105 --- 2024-12-18T01:36:56.8602890Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566. 2024-12-18T01:36:56.8603349Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8603493Z 2024-12-18T01:36:56.8603901Z Takes in a feature and optional module_filter and plots of the desired data. 2024-12-18T01:36:56.8604036Z 2024-12-18T01:36:56.8604514Z For per channel features, it averages the value across the channels and plots a point 2024-12-18T01:36:56.8605052Z per module. The reason for this is that for models with hundreds of channels, it can 2024-12-18T01:36:56.8605531Z be hard to differentiate one channel line from another, and so the point of generating 2024-12-18T01:36:56.8605996Z a single average point per module is to give a sense of general trends that encourage 2024-12-18T01:36:56.8606164Z further deep dives. 2024-12-18T01:36:56.8606294Z 2024-12-18T01:36:56.8606436Z Note: 2024-12-18T01:36:56.8606890Z Only features in the report that have tensor value data are plottable by this class 2024-12-18T01:36:56.8607175Z When the tensor information is plotted, it will plot: 2024-12-18T01:36:56.8607390Z idx as the x val, feature value as the y_val 2024-12-18T01:36:56.8607711Z When the channel information is plotted, it will plot: 2024-12-18T01:36:56.8608182Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2024-12-18T01:36:56.8608575Z The reason for this is that we want to be able to compare values across the 2024-12-18T01:36:56.8608989Z channels for same layer, and it will be hard if values are staggered by idx 2024-12-18T01:36:56.8609265Z This means each module is represented by only 1 x value 2024-12-18T01:36:56.8609453Z Args: 2024-12-18T01:36:56.8609834Z feature_filter (str): Filters the features presented to only those that 2024-12-18T01:36:56.8610011Z contain this filter substring 2024-12-18T01:36:56.8610477Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:56.8610902Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:56.8611047Z 2024-12-18T01:36:56.8611189Z Example Use: 2024-12-18T01:36:56.8611406Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8611709Z >>> mod_report_visualizer.generate_plot_visualization( 2024-12-18T01:36:56.8611910Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:56.8612091Z ... module_fqn_filter = "block1" 2024-12-18T01:36:56.8612223Z ... ) 2024-12-18T01:36:56.8612534Z >>> # outputs line plot of per_channel_min information for all 2024-12-18T01:36:56.8612839Z >>> # modules in block1 of model each channel gets it's own line, 2024-12-18T01:36:56.8613129Z >>> # and it's plotted across the in-order modules on the x-axis 2024-12-18T01:36:56.8613271Z 2024-12-18T01:36:56.8613701Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8613842Z 2024-12-18T01:36:56.8613995Z warnings.warn(msg) 2024-12-18T01:36:56.8614140Z 2024-12-18T01:36:56.8614450Z --- Parse Warning: 34 / 105 --- 2024-12-18T01:36:56.8616800Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646. 2024-12-18T01:36:56.8617275Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8617400Z 2024-12-18T01:36:56.8617888Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2024-12-18T01:36:56.8618014Z 2024-12-18T01:36:56.8618160Z Note: 2024-12-18T01:36:56.8618617Z Only features in the report that have tensor value data can be viewed as a histogram 2024-12-18T01:36:56.8619068Z If you want to plot a histogram from all the channel values of a specific feature for 2024-12-18T01:36:56.8619504Z a specific model, make sure to specify both the model and the feature properly 2024-12-18T01:36:56.8619925Z in the filters and you should be able to see a distribution of the channel data 2024-12-18T01:36:56.8620105Z 2024-12-18T01:36:56.8620237Z Args: 2024-12-18T01:36:56.8620700Z feature_filter (str, optional): Filters the features presented to only those that 2024-12-18T01:36:56.8620879Z contain this filter substring 2024-12-18T01:36:56.8621147Z Default = "", results in all the features being printed 2024-12-18T01:36:56.8621606Z module_fqn_filter (str, optional): Only includes modules that contains this string 2024-12-18T01:36:56.8622023Z Default = "", results in all the modules in the reports to be visible in the table 2024-12-18T01:36:56.8622407Z num_bins (int, optional): The number of bins to create the histogram with 2024-12-18T01:36:56.8622712Z Default = 10, the values will be split into 10 equal sized bins 2024-12-18T01:36:56.8622887Z 2024-12-18T01:36:56.8623026Z Example Use: 2024-12-18T01:36:56.8623181Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8623721Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2024-12-18T01:36:56.8623921Z ... feature_filter = "per_channel_min", 2024-12-18T01:36:56.8624107Z ... module_fqn_filter = "block1" 2024-12-18T01:36:56.8624238Z ... ) 2024-12-18T01:36:56.8624749Z # outputs histogram of per_channel_min information for all modules in block1 of model 2024-12-18T01:36:56.8625209Z information is gathered across all channels for all modules in block 1 for the 2024-12-18T01:36:56.8625578Z per_channel_min and is displayed in a histogram of equally sized bins 2024-12-18T01:36:56.8625719Z 2024-12-18T01:36:56.8626156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8626299Z 2024-12-18T01:36:56.8626454Z warnings.warn(msg) 2024-12-18T01:36:56.8626589Z 2024-12-18T01:36:56.8626909Z --- Parse Warning: 35 / 105 --- 2024-12-18T01:36:56.8628667Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=660. 2024-12-18T01:36:56.8629115Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2024-12-18T01:36:56.8629244Z 2024-12-18T01:36:56.8629726Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2024-12-18T01:36:56.8630192Z The submesh created consists of the dimensions and the communicators indicated by 2024-12-18T01:36:56.8630338Z ``mesh_dim_names`` 2024-12-18T01:36:56.8630482Z 2024-12-18T01:36:56.8630616Z Args: 2024-12-18T01:36:56.8631026Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2024-12-18T01:36:56.8631336Z mesh dimension of the DeviceMesh to create the submesh for. 2024-12-18T01:36:56.8631483Z Returns: 2024-12-18T01:36:56.8631658Z A :class:`DeviceMesh` object 2024-12-18T01:36:56.8631791Z 2024-12-18T01:36:56.8632308Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2024-12-18T01:36:56.8632461Z In the first example: 2024-12-18T01:36:56.8632898Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2024-12-18T01:36:56.8633321Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2024-12-18T01:36:56.8633718Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2024-12-18T01:36:56.8634098Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2024-12-18T01:36:56.8634479Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2024-12-18T01:36:56.8634865Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2024-12-18T01:36:56.8634993Z 2024-12-18T01:36:56.8635168Z In the second example: 2024-12-18T01:36:56.8635741Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2024-12-18T01:36:56.8636207Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2024-12-18T01:36:56.8636662Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2024-12-18T01:36:56.8637107Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2024-12-18T01:36:56.8637256Z 2024-12-18T01:36:56.8637409Z Example:: 2024-12-18T01:36:56.8637593Z >>> # xdoctest: +SKIP("no rank") 2024-12-18T01:36:56.8637874Z >>> from torch.distributed.device_mesh import DeviceMesh 2024-12-18T01:36:56.8638063Z >>> 2024-12-18T01:36:56.8638401Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2024-12-18T01:36:56.8638628Z >>> # of cross-host(dim 0), and within-host (dim 1). 2024-12-18T01:36:56.8639067Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:36:56.8639227Z >>> tp_mesh = mesh_2d["tp"] 2024-12-18T01:36:56.8639402Z >>> dp_mesh = mesh_2d["dp"] 2024-12-18T01:36:56.8639532Z >>> 2024-12-18T01:36:56.8639725Z >>> # Initialize a 3D mesh. 2024-12-18T01:36:56.8640210Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2024-12-18T01:36:56.8640739Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2024-12-18T01:36:56.8640928Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2024-12-18T01:36:56.8641107Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2024-12-18T01:36:56.8641248Z 2024-12-18T01:36:56.8642506Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2024-12-18T01:36:56.8642636Z 2024-12-18T01:36:56.8643068Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2024-12-18T01:36:56.8643250Z ^ 2024-12-18T01:36:56.8643421Z warnings.warn(msg) 2024-12-18T01:36:56.8643545Z 2024-12-18T01:36:56.8643871Z --- Parse Warning: 36 / 105 --- 2024-12-18T01:36:56.8645595Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3063. 2024-12-18T01:36:56.8646058Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8646191Z 2024-12-18T01:36:56.8646569Z Gathers picklable objects from the whole group in a single process. 2024-12-18T01:36:56.8646712Z 2024-12-18T01:36:56.8647115Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2024-12-18T01:36:56.8647375Z object must be picklable in order to be gathered. 2024-12-18T01:36:56.8647505Z 2024-12-18T01:36:56.8647636Z Args: 2024-12-18T01:36:56.8647856Z obj (Any): Input object. Must be picklable. 2024-12-18T01:36:56.8648208Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2024-12-18T01:36:56.8648533Z should be correctly sized as the size of the group for this 2024-12-18T01:36:56.8648900Z collective and will contain the output. Must be ``None`` on non-dst 2024-12-18T01:36:56.8649087Z ranks. (default is ``None``) 2024-12-18T01:36:56.8649644Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2024-12-18T01:36:56.8649965Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2024-12-18T01:36:56.8650382Z group: (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:36:56.8650741Z the default process group will be used. Default is ``None``. 2024-12-18T01:36:56.8651354Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2024-12-18T01:36:56.8651486Z 2024-12-18T01:36:56.8651634Z Returns: 2024-12-18T01:36:56.8651940Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2024-12-18T01:36:56.8652103Z output of the collective. 2024-12-18T01:36:56.8652245Z 2024-12-18T01:36:56.8652610Z .. note:: Note that this API differs slightly from the gather collective 2024-12-18T01:36:56.8652996Z since it does not provide an async_op handle and thus will be a blocking 2024-12-18T01:36:56.8653169Z call. 2024-12-18T01:36:56.8653306Z 2024-12-18T01:36:56.8653701Z .. note:: For NCCL-based processed groups, internal tensor representations 2024-12-18T01:36:56.8654065Z of objects must be moved to the GPU device before communication takes 2024-12-18T01:36:56.8654315Z place. In this case, the device used is given by 2024-12-18T01:36:56.8654686Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2024-12-18T01:36:56.8655084Z ensure that this is set so that each rank has an individual GPU, via 2024-12-18T01:36:56.8655260Z ``torch.cuda.set_device()``. 2024-12-18T01:36:56.8655415Z 2024-12-18T01:36:56.8655559Z .. warning:: 2024-12-18T01:36:56.8655893Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2024-12-18T01:36:56.8656280Z known to be insecure. It is possible to construct malicious pickle data 2024-12-18T01:36:56.8656648Z which will execute arbitrary code during unpickling. Only call this 2024-12-18T01:36:56.8656842Z function with data you trust. 2024-12-18T01:36:56.8656973Z 2024-12-18T01:36:56.8657098Z .. warning:: 2024-12-18T01:36:56.8657376Z Calling :func:`gather_object` with GPU tensors is not well supported 2024-12-18T01:36:56.8657604Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2024-12-18T01:36:56.8657791Z pickled. Please consider using :func:`gather` instead. 2024-12-18T01:36:56.8657879Z 2024-12-18T01:36:56.8657994Z Example:: 2024-12-18T01:36:56.8658141Z >>> # xdoctest: +SKIP("need process group init") 2024-12-18T01:36:56.8658325Z >>> # Note: Process group initialization omitted on each rank. 2024-12-18T01:36:56.8658466Z >>> import torch.distributed as dist 2024-12-18T01:36:56.8658580Z >>> # Assumes world_size of 3. 2024-12-18T01:36:56.8658772Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2024-12-18T01:36:56.8658901Z >>> output = [None for _ in gather_objects] 2024-12-18T01:36:56.8659023Z >>> dist.gather_object( 2024-12-18T01:36:56.8659150Z ... gather_objects[dist.get_rank()], 2024-12-18T01:36:56.8659293Z ... output if dist.get_rank() == 0 else None, 2024-12-18T01:36:56.8659404Z ... dst=0 2024-12-18T01:36:56.8659493Z ... ) 2024-12-18T01:36:56.8659602Z >>> # On rank 0 2024-12-18T01:36:56.8659691Z >>> output 2024-12-18T01:36:56.8659790Z ['foo', 12, {1: 2}] 2024-12-18T01:36:56.8659894Z 2024-12-18T01:36:56.8660150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8660247Z 2024-12-18T01:36:56.8660351Z warnings.warn(msg) 2024-12-18T01:36:56.8660435Z 2024-12-18T01:36:56.8660655Z --- Parse Warning: 37 / 105 --- 2024-12-18T01:36:56.8661482Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2024-12-18T01:36:56.8661761Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8661880Z 2024-12-18T01:36:56.8662018Z Module ``torch.distributed.launch``. 2024-12-18T01:36:56.8662105Z 2024-12-18T01:36:56.8662356Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2024-12-18T01:36:56.8662524Z training processes on each of the training nodes. 2024-12-18T01:36:56.8662614Z 2024-12-18T01:36:56.8662721Z .. warning:: 2024-12-18T01:36:56.8662810Z 2024-12-18T01:36:56.8663063Z This module is going to be deprecated in favor of :ref:`torchrun `. 2024-12-18T01:36:56.8663162Z 2024-12-18T01:36:56.8663401Z The utility can be used for single-node distributed training, in which one or 2024-12-18T01:36:56.8663646Z more processes per node will be spawned. The utility can be used for either 2024-12-18T01:36:56.8663891Z CPU training or GPU training. If the utility is used for GPU training, 2024-12-18T01:36:56.8664152Z each distributed process will be operating on a single GPU. This can achieve 2024-12-18T01:36:56.8664387Z well-improved single-node training performance. It can also be used in 2024-12-18T01:36:56.8664651Z multi-node distributed training, by spawning up multiple processes on each node 2024-12-18T01:36:56.8664894Z for well-improved multi-node distributed training performance as well. 2024-12-18T01:36:56.8665149Z This will especially be beneficial for systems with multiple Infiniband 2024-12-18T01:36:56.8665413Z interfaces that have direct-GPU support, since all of them can be utilized for 2024-12-18T01:36:56.8665534Z aggregated communication bandwidth. 2024-12-18T01:36:56.8665635Z 2024-12-18T01:36:56.8665869Z In both cases of single-node distributed training or multi-node distributed 2024-12-18T01:36:56.8666104Z training, this utility will launch the given number of processes per node 2024-12-18T01:36:56.8666343Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2024-12-18T01:36:56.8666590Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2024-12-18T01:36:56.8666806Z and each process will be operating on a single GPU from *GPU 0 to 2024-12-18T01:36:56.8666918Z GPU (nproc_per_node - 1)*. 2024-12-18T01:36:56.8667016Z 2024-12-18T01:36:56.8667123Z **How to use this module:** 2024-12-18T01:36:56.8667214Z 2024-12-18T01:36:56.8667387Z 1. Single-Node multi-process distributed training 2024-12-18T01:36:56.8667475Z 2024-12-18T01:36:56.8667581Z :: 2024-12-18T01:36:56.8667670Z 2024-12-18T01:36:56.8667910Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:36:56.8668117Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2024-12-18T01:36:56.8668247Z arguments of your training script) 2024-12-18T01:36:56.8668350Z 2024-12-18T01:36:56.8668561Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2024-12-18T01:36:56.8668660Z 2024-12-18T01:36:56.8668753Z 2024-12-18T01:36:56.8668903Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2024-12-18T01:36:56.8669002Z 2024-12-18T01:36:56.8669091Z :: 2024-12-18T01:36:56.8669186Z 2024-12-18T01:36:56.8669421Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:36:56.8669585Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2024-12-18T01:36:56.8669807Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:36:56.8669964Z and all other arguments of your training script) 2024-12-18T01:36:56.8670065Z 2024-12-18T01:36:56.8670156Z Node 2: 2024-12-18T01:36:56.8670255Z 2024-12-18T01:36:56.8670345Z :: 2024-12-18T01:36:56.8670431Z 2024-12-18T01:36:56.8670678Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2024-12-18T01:36:56.8670844Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2024-12-18T01:36:56.8671097Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2024-12-18T01:36:56.8671251Z and all other arguments of your training script) 2024-12-18T01:36:56.8671338Z 2024-12-18T01:36:56.8671517Z 3. To look up what optional arguments this module offers: 2024-12-18T01:36:56.8671605Z 2024-12-18T01:36:56.8671708Z :: 2024-12-18T01:36:56.8671794Z 2024-12-18T01:36:56.8671939Z python -m torch.distributed.launch --help 2024-12-18T01:36:56.8672036Z 2024-12-18T01:36:56.8672123Z 2024-12-18T01:36:56.8672241Z **Important Notices:** 2024-12-18T01:36:56.8672330Z 2024-12-18T01:36:56.8672538Z 1. This utility and multi-process distributed (single-node or 2024-12-18T01:36:56.8672791Z multi-node) GPU training currently only achieves the best performance using 2024-12-18T01:36:56.8673069Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2024-12-18T01:36:56.8673184Z use for GPU training. 2024-12-18T01:36:56.8673278Z 2024-12-18T01:36:56.8673507Z 2. In your training program, you must parse the command-line argument: 2024-12-18T01:36:56.8673737Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2024-12-18T01:36:56.8674004Z If your training program uses GPUs, you should ensure that your code only 2024-12-18T01:36:56.8674202Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2024-12-18T01:36:56.8674288Z 2024-12-18T01:36:56.8674413Z Parsing the local_rank argument 2024-12-18T01:36:56.8674500Z 2024-12-18T01:36:56.8674601Z :: 2024-12-18T01:36:56.8674687Z 2024-12-18T01:36:56.8674790Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8674904Z >>> import argparse 2024-12-18T01:36:56.8675037Z >>> parser = argparse.ArgumentParser() 2024-12-18T01:36:56.8675247Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2024-12-18T01:36:56.8675389Z >>> args = parser.parse_args() 2024-12-18T01:36:56.8675479Z 2024-12-18T01:36:56.8675705Z Set your device to local rank using either 2024-12-18T01:36:56.8675797Z 2024-12-18T01:36:56.8675904Z :: 2024-12-18T01:36:56.8675993Z 2024-12-18T01:36:56.8676197Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2024-12-18T01:36:56.8676297Z 2024-12-18T01:36:56.8676389Z or 2024-12-18T01:36:56.8676487Z 2024-12-18T01:36:56.8676575Z :: 2024-12-18T01:36:56.8676677Z 2024-12-18T01:36:56.8676814Z >>> with torch.cuda.device(args.local_rank): 2024-12-18T01:36:56.8676918Z >>> # your code to run 2024-12-18T01:36:56.8677021Z >>> ... 2024-12-18T01:36:56.8677107Z 2024-12-18T01:36:56.8677228Z .. versionchanged:: 2.0.0 2024-12-18T01:36:56.8677314Z 2024-12-18T01:36:56.8677562Z The launcher will passes the ``--local-rank=`` argument to your script. 2024-12-18T01:36:56.8677812Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2024-12-18T01:36:56.8677960Z previously used underscored ``--local_rank``. 2024-12-18T01:36:56.8678058Z 2024-12-18T01:36:56.8678297Z For backward compatibility, it may be necessary for users to handle both 2024-12-18T01:36:56.8678577Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2024-12-18T01:36:56.8678795Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2024-12-18T01:36:56.8679046Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2024-12-18T01:36:56.8679290Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2024-12-18T01:36:56.8679444Z including ``"--local-rank"`` should be sufficient. 2024-12-18T01:36:56.8679545Z 2024-12-18T01:36:56.8679781Z 3. In your training program, you are supposed to call the following function 2024-12-18T01:36:56.8680035Z at the beginning to start the distributed backend. It is strongly recommended 2024-12-18T01:36:56.8680292Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2024-12-18T01:36:56.8680487Z but ``env://`` is the one that is officially supported by this module. 2024-12-18T01:36:56.8680586Z 2024-12-18T01:36:56.8680675Z :: 2024-12-18T01:36:56.8680777Z 2024-12-18T01:36:56.8680987Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2024-12-18T01:36:56.8681127Z >>> init_method='env://') 2024-12-18T01:36:56.8681228Z 2024-12-18T01:36:56.8681469Z 4. In your training program, you can either use regular distributed functions 2024-12-18T01:36:56.8681726Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2024-12-18T01:36:56.8681939Z training program uses GPUs for training and you would like to use 2024-12-18T01:36:56.8682181Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2024-12-18T01:36:56.8682296Z here is how to configure it. 2024-12-18T01:36:56.8682384Z 2024-12-18T01:36:56.8682484Z :: 2024-12-18T01:36:56.8682571Z 2024-12-18T01:36:56.8682785Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2024-12-18T01:36:56.8682928Z >>> device_ids=[args.local_rank], 2024-12-18T01:36:56.8683125Z >>> output_device=args.local_rank) 2024-12-18T01:36:56.8683212Z 2024-12-18T01:36:56.8683454Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2024-12-18T01:36:56.8683695Z that your code will be operating on. This is generally the local rank of the 2024-12-18T01:36:56.8683933Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2024-12-18T01:36:56.8684171Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2024-12-18T01:36:56.8684262Z utility 2024-12-18T01:36:56.8684379Z 2024-12-18T01:36:56.8684640Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2024-12-18T01:36:56.8684861Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2024-12-18T01:36:56.8685094Z ``--use-env=True``. You must adjust the subprocess example above to replace 2024-12-18T01:36:56.8685295Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2024-12-18T01:36:56.8685483Z will not pass ``--local-rank`` when you specify this flag. 2024-12-18T01:36:56.8685573Z 2024-12-18T01:36:56.8685670Z .. warning:: 2024-12-18T01:36:56.8685772Z 2024-12-18T01:36:56.8685977Z ``local_rank`` is NOT globally unique: it is only unique per process 2024-12-18T01:36:56.8686188Z on a machine. Thus, don't use it to decide if you should, e.g., 2024-12-18T01:36:56.8686317Z write to a networked filesystem. See 2024-12-18T01:36:56.8686550Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2024-12-18T01:36:56.8686724Z how things can go wrong if you don't do this correctly. 2024-12-18T01:36:56.8686811Z 2024-12-18T01:36:56.8686911Z 2024-12-18T01:36:56.8687001Z 2024-12-18T01:36:56.8687104Z 2024-12-18T01:36:56.8687356Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8687447Z 2024-12-18T01:36:56.8687566Z warnings.warn(msg) 2024-12-18T01:36:56.8687656Z 2024-12-18T01:36:56.8687886Z --- Parse Warning: 38 / 105 --- 2024-12-18T01:36:56.8688899Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2024-12-18T01:36:56.8689177Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8689267Z 2024-12-18T01:36:56.8689513Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2024-12-18T01:36:56.8689711Z Needs to be called on all ranks in an SPMD fashion. 2024-12-18T01:36:56.8689806Z 2024-12-18T01:36:56.8689913Z Args: 2024-12-18T01:36:56.8690190Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2024-12-18T01:36:56.8690375Z of shards that represent the local shards on this rank. 2024-12-18T01:36:56.8690604Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2024-12-18T01:36:56.8690728Z shape of the overall sharded tensor. 2024-12-18T01:36:56.8690830Z 2024-12-18T01:36:56.8690930Z Keyword args: 2024-12-18T01:36:56.8691205Z process_group (ProcessGroup, optional): The process group to work on. If None, 2024-12-18T01:36:56.8691364Z the default process group will be used. 2024-12-18T01:36:56.8691540Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:36:56.8691771Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:36:56.8691963Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:36:56.8692078Z Default: ``False``. 2024-12-18T01:36:56.8692166Z 2024-12-18T01:36:56.8692273Z Returns: 2024-12-18T01:36:56.8692460Z A :class:`ShardedTensor` object handle on this rank 2024-12-18T01:36:56.8692555Z 2024-12-18T01:36:56.8692657Z 2024-12-18T01:36:56.8692753Z Examples: 2024-12-18T01:36:56.8693013Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2024-12-18T01:36:56.8693198Z each shard have a (5, 5) local tensor, we can do it like below: 2024-12-18T01:36:56.8693285Z 2024-12-18T01:36:56.8693388Z on rank 0: 2024-12-18T01:36:56.8693515Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:36:56.8693656Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:36:56.8693806Z >>> shard_offsets=[0, 0], 2024-12-18T01:36:56.8693927Z >>> shard_lengths=[5, 5], 2024-12-18T01:36:56.8694041Z >>> placement="rank:0/cuda:0" 2024-12-18T01:36:56.8694130Z >>> ) 2024-12-18T01:36:56.8694340Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:36:56.8694538Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:36:56.8694639Z 2024-12-18T01:36:56.8694729Z on rank 1: 2024-12-18T01:36:56.8694852Z >>> # xdoctest: +SKIP("not distributed") 2024-12-18T01:36:56.8694992Z >>> local_shard_metadata = ShardMetadata( 2024-12-18T01:36:56.8695097Z >>> shard_offsets=[5, 0], 2024-12-18T01:36:56.8695219Z >>> shard_lengths=[5, 5], 2024-12-18T01:36:56.8695334Z >>> placement="rank:1/cuda:1" 2024-12-18T01:36:56.8695439Z >>> ) 2024-12-18T01:36:56.8695638Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2024-12-18T01:36:56.8695835Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2024-12-18T01:36:56.8695934Z 2024-12-18T01:36:56.8696182Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8696281Z 2024-12-18T01:36:56.8696384Z warnings.warn(msg) 2024-12-18T01:36:56.8696472Z 2024-12-18T01:36:56.8696684Z --- Parse Warning: 39 / 105 --- 2024-12-18T01:36:56.8697741Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784. 2024-12-18T01:36:56.8698203Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8698294Z 2024-12-18T01:36:56.8698563Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2024-12-18T01:36:56.8698685Z size and sharding spec on each rank. 2024-12-18T01:36:56.8698841Z 2024-12-18T01:36:56.8698942Z Args: 2024-12-18T01:36:56.8699171Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2024-12-18T01:36:56.8699448Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2024-12-18T01:36:56.8699622Z The specification describing how to shard the Tensor. 2024-12-18T01:36:56.8699808Z global_size (Sequence[int]): Size of the sharded tensor. 2024-12-18T01:36:56.8700058Z process_group (ProcessGroup, optional): The process group to aggregate on. 2024-12-18T01:36:56.8700157Z Default: None 2024-12-18T01:36:56.8700348Z init_rrefs (bool, optional): Whether or not to initialize 2024-12-18T01:36:56.8700559Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2024-12-18T01:36:56.8700803Z Need to initialize the RPC Framework if specified as ``True``. 2024-12-18T01:36:56.8700910Z Default: ``False``. 2024-12-18T01:36:56.8701011Z 2024-12-18T01:36:56.8701103Z Returns: 2024-12-18T01:36:56.8701345Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2024-12-18T01:36:56.8701481Z tensor stored in the current rank. 2024-12-18T01:36:56.8701569Z 2024-12-18T01:36:56.8701710Z Examples: 2024-12-18T01:36:56.8701815Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8701957Z >>> # All tensors below are of torch.int64 type. 2024-12-18T01:36:56.8702090Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:36:56.8702270Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:36:56.8702486Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2024-12-18T01:36:56.8702823Z >>> local_tensor 2024-12-18T01:36:56.8702949Z tensor([[1, 2, 3, 4]]) # Rank 0 2024-12-18T01:36:56.8703057Z tensor([[3, 4, 5, 6]]) # Rank 1 2024-12-18T01:36:56.8703200Z >>> sharding_dim = 0 2024-12-18T01:36:56.8703350Z >>> sharding_spec = ChunkShardingSpec( 2024-12-18T01:36:56.8703455Z dim=sharding_dim, 2024-12-18T01:36:56.8703573Z placements=[ 2024-12-18T01:36:56.8703675Z "rank:0/cuda:0", 2024-12-18T01:36:56.8703776Z "rank:1/cuda:1", 2024-12-18T01:36:56.8703878Z ], 2024-12-18T01:36:56.8703969Z ) 2024-12-18T01:36:56.8704238Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2024-12-18T01:36:56.8704332Z >>> st 2024-12-18T01:36:56.8704432Z ShardedTensor( 2024-12-18T01:36:56.8704559Z ShardedTensorMetadata( 2024-12-18T01:36:56.8704663Z shards_metadata=[ 2024-12-18T01:36:56.8704941Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2024-12-18T01:36:56.8705206Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2024-12-18T01:36:56.8705312Z ], 2024-12-18T01:36:56.8705421Z size=torch.Size([2, 4]) 2024-12-18T01:36:56.8705508Z ) 2024-12-18T01:36:56.8705621Z >>> st.local_tensor() 2024-12-18T01:36:56.8705723Z tensor([1, 2, 3, 4]) # Rank 0 2024-12-18T01:36:56.8705839Z tensor([3, 4, 5, 6]) # Rank 1 2024-12-18T01:36:56.8705926Z 2024-12-18T01:36:56.8706192Z Warning: This API is experimental and subject to change. It lacks of a fully across 2024-12-18T01:36:56.8706440Z rank validations, and we only validate the local shard on the current rank. 2024-12-18T01:36:56.8706657Z We fully rely on the user to ensure local tensor is sharded based on the 2024-12-18T01:36:56.8706769Z sharding spec. 2024-12-18T01:36:56.8706856Z 2024-12-18T01:36:56.8707118Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8707203Z 2024-12-18T01:36:56.8707337Z warnings.warn(msg) 2024-12-18T01:36:56.8707436Z 2024-12-18T01:36:56.8707647Z --- Parse Warning: 40 / 105 --- 2024-12-18T01:36:56.8708674Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023. 2024-12-18T01:36:56.8708933Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8709032Z 2024-12-18T01:36:56.8709283Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2024-12-18T01:36:56.8709382Z single local shard. 2024-12-18T01:36:56.8709482Z 2024-12-18T01:36:56.8709703Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2024-12-18T01:36:56.8709988Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2024-12-18T01:36:56.8710102Z we swap local shards directly. 2024-12-18T01:36:56.8710376Z For more generic cases, we merge different shards across different ranks and split 2024-12-18T01:36:56.8710626Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2024-12-18T01:36:56.8710714Z 2024-12-18T01:36:56.8710817Z Args: 2024-12-18T01:36:56.8711132Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2024-12-18T01:36:56.8711314Z specification describing how the tensor is sharded. 2024-12-18T01:36:56.8711402Z 2024-12-18T01:36:56.8711494Z Returns: 2024-12-18T01:36:56.8711712Z A :class:`ShardedTensor` object whose local shards are resharded. 2024-12-18T01:36:56.8711800Z 2024-12-18T01:36:56.8711909Z Examples: 2024-12-18T01:36:56.8712016Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8712155Z >>> # We have 2 process groups, 2 ranks. 2024-12-18T01:36:56.8712360Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2024-12-18T01:36:56.8712491Z >>> tensor = torch.stack([tensor, tensor]) 2024-12-18T01:36:56.8712596Z >>> tensor 2024-12-18T01:36:56.8712722Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2024-12-18T01:36:56.8712858Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2024-12-18T01:36:56.8712981Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2024-12-18T01:36:56.8713123Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2024-12-18T01:36:56.8713225Z >>> sharding_dim = 0 2024-12-18T01:36:56.8713342Z >>> spec = ChunkShardingSpec( 2024-12-18T01:36:56.8713456Z dim=sharding_dim, 2024-12-18T01:36:56.8713558Z placements=[ 2024-12-18T01:36:56.8713671Z "rank:0/cuda:0", 2024-12-18T01:36:56.8713771Z "rank:1/cuda:1", 2024-12-18T01:36:56.8713873Z "rank:2/cuda:2", 2024-12-18T01:36:56.8713984Z "rank:3/cuda:3", 2024-12-18T01:36:56.8714075Z ], 2024-12-18T01:36:56.8714181Z ) 2024-12-18T01:36:56.8714289Z >>> current_offsets = [0] * 2 2024-12-18T01:36:56.8714403Z >>> current_offsets[0] = rank * 2 2024-12-18T01:36:56.8714537Z >>> shard_metadata = ShardMetadata( 2024-12-18T01:36:56.8714696Z shard_offsets=copy.deepcopy(current_offsets), 2024-12-18T01:36:56.8714832Z shard_sizes=tensor.size(), 2024-12-18T01:36:56.8714962Z placement=spec.placements[rank], 2024-12-18T01:36:56.8715063Z ) 2024-12-18T01:36:56.8715165Z >>> local_shards = [ 2024-12-18T01:36:56.8715259Z Shard( 2024-12-18T01:36:56.8715376Z tensor=tensor, 2024-12-18T01:36:56.8715493Z metadata=shard_metadata, 2024-12-18T01:36:56.8715598Z ) 2024-12-18T01:36:56.8715762Z ] 2024-12-18T01:36:56.8716002Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2024-12-18T01:36:56.8716155Z >>> sharding_dim = 1 2024-12-18T01:36:56.8716287Z >>> resharding_spec = ChunkShardingSpec( 2024-12-18T01:36:56.8716406Z dim=sharding_dim, 2024-12-18T01:36:56.8716510Z placements=[ 2024-12-18T01:36:56.8716626Z "rank:0/cuda:0", 2024-12-18T01:36:56.8716730Z "rank:1/cuda:1", 2024-12-18T01:36:56.8716833Z "rank:2/cuda:2", 2024-12-18T01:36:56.8716947Z "rank:3/cuda:3", 2024-12-18T01:36:56.8717041Z ], 2024-12-18T01:36:56.8717147Z ) 2024-12-18T01:36:56.8717267Z >>> st.reshard(resharding_spec) 2024-12-18T01:36:56.8717391Z >>> tensor = st.local_shards()[0].tensor 2024-12-18T01:36:56.8717500Z >>> tensor 2024-12-18T01:36:56.8717648Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2024-12-18T01:36:56.8717851Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2024-12-18T01:36:56.8717996Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2024-12-18T01:36:56.8718162Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2024-12-18T01:36:56.8718254Z 2024-12-18T01:36:56.8718508Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8718612Z 2024-12-18T01:36:56.8718742Z warnings.warn(msg) 2024-12-18T01:36:56.8718844Z 2024-12-18T01:36:56.8719054Z --- Parse Warning: 41 / 105 --- 2024-12-18T01:36:56.8720011Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2024-12-18T01:36:56.8720289Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8720380Z 2024-12-18T01:36:56.8720645Z Representation of a sharding plan, describes how to shard a module 2024-12-18T01:36:56.8720921Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2024-12-18T01:36:56.8721218Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2024-12-18T01:36:56.8721469Z layout of a module with a spec, and when to convert back to data parallel fashion. 2024-12-18T01:36:56.8721561Z 2024-12-18T01:36:56.8721665Z Args: 2024-12-18T01:36:56.8721936Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2024-12-18T01:36:56.8722122Z :class:`torch.distributed._shard.sharder.Sharder`]): 2024-12-18T01:36:56.8722394Z a dict describes how to shard a module, there're currently two ways to shard a module: 2024-12-18T01:36:56.8722662Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2024-12-18T01:36:56.8722791Z a parameter to a `ShardingSpec`. 2024-12-18T01:36:56.8723049Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2024-12-18T01:36:56.8723172Z to a `Sharder` object. 2024-12-18T01:36:56.8723496Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2024-12-18T01:36:56.8723769Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2024-12-18T01:36:56.8724004Z keyed by the name of module to ShardingSpec("" in key means the root module). 2024-12-18T01:36:56.8724120Z Default: `None` 2024-12-18T01:36:56.8724372Z return_local_tensor (List[str], optional): a list of string, each element enables 2024-12-18T01:36:56.8724623Z a module's sharded output to be returned as a Tensor from its local shards to 2024-12-18T01:36:56.8724873Z ensure further processing in a data parallel fashion. ("" in list means the 2024-12-18T01:36:56.8724969Z root module). 2024-12-18T01:36:56.8725080Z Default: None 2024-12-18T01:36:56.8725201Z Example: 2024-12-18T01:36:56.8725500Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2024-12-18T01:36:56.8725781Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2024-12-18T01:36:56.8725869Z 2024-12-18T01:36:56.8726061Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2024-12-18T01:36:56.8726176Z >>> class MyModule(nn.Module): 2024-12-18T01:36:56.8726308Z >>> def __init__(self) -> None: 2024-12-18T01:36:56.8726414Z >>> super().__init__() 2024-12-18T01:36:56.8726541Z >>> self.fc1 = nn.Linear() 2024-12-18T01:36:56.8726653Z >>> self.gelu = nn.GELU() 2024-12-18T01:36:56.8726761Z >>> self.fc2 = nn.Linear() 2024-12-18T01:36:56.8726913Z >>> self.relu = nn.Linear() 2024-12-18T01:36:56.8727007Z >>> 2024-12-18T01:36:56.8727135Z >>> def forward(self, input): 2024-12-18T01:36:56.8727317Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2024-12-18T01:36:56.8727403Z 2024-12-18T01:36:56.8727503Z 2024-12-18T01:36:56.8727638Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2024-12-18T01:36:56.8727767Z >>> sharding_plan = ShardingPlan( 2024-12-18T01:36:56.8727887Z >>> plan={ 2024-12-18T01:36:56.8728009Z >>> "fc1.weight": spec1, 2024-12-18T01:36:56.8728115Z >>> "fc2.weight": spec2 2024-12-18T01:36:56.8728203Z >>> }, 2024-12-18T01:36:56.8728318Z >>> output_plan={ 2024-12-18T01:36:56.8728422Z >>> "fc2": output_spec 2024-12-18T01:36:56.8728523Z >>> }, 2024-12-18T01:36:56.8728637Z >>> return_local_tensor=["fc2"] 2024-12-18T01:36:56.8728727Z >>> ) 2024-12-18T01:36:56.8728827Z 2024-12-18T01:36:56.8729106Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8729209Z 2024-12-18T01:36:56.8729311Z warnings.warn(msg) 2024-12-18T01:36:56.8729399Z 2024-12-18T01:36:56.8729610Z --- Parse Warning: 42 / 105 --- 2024-12-18T01:36:56.8730682Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2024-12-18T01:36:56.8730959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8731048Z 2024-12-18T01:36:56.8731181Z Run post-localSGD algorithm. 2024-12-18T01:36:56.8731269Z 2024-12-18T01:36:56.8731508Z This DDP communication hook is used for running post-localSGD algorithm, 2024-12-18T01:36:56.8731687Z by combining with a model averaging component (e.g., 2024-12-18T01:36:56.8732030Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2024-12-18T01:36:56.8732169Z that runs after the optimizer step. 2024-12-18T01:36:56.8732258Z 2024-12-18T01:36:56.8732361Z Args: 2024-12-18T01:36:56.8732587Z state (PostLocalSGDState): State information to run post-localSGD. 2024-12-18T01:36:56.8732862Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2024-12-18T01:36:56.8733288Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:36:56.8733536Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:36:56.8733704Z only exactly one tensor is stored in this bucket. 2024-12-18T01:36:56.8733792Z 2024-12-18T01:36:56.8733897Z Returns: 2024-12-18T01:36:56.8734141Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:36:56.8734227Z 2024-12-18T01:36:56.8734344Z Example:: 2024-12-18T01:36:56.8734475Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8734739Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2024-12-18T01:36:56.8734863Z start_localSGD_iter=10) 2024-12-18T01:36:56.8735055Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:56.8735386Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2024-12-18T01:36:56.8735732Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2024-12-18T01:36:56.8735840Z 2024-12-18T01:36:56.8736092Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8736194Z 2024-12-18T01:36:56.8736327Z warnings.warn(msg) 2024-12-18T01:36:56.8736415Z 2024-12-18T01:36:56.8736621Z --- Parse Warning: 43 / 105 --- 2024-12-18T01:36:56.8737657Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=343. 2024-12-18T01:36:56.8737959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8738046Z 2024-12-18T01:36:56.8738173Z Implement PowerSGD algorithm. 2024-12-18T01:36:56.8738261Z 2024-12-18T01:36:56.8738486Z This DDP communication hook implements PowerSGD gradient compression 2024-12-18T01:36:56.8738736Z algorithm described in the `paper `_. 2024-12-18T01:36:56.8738975Z Once gradient tensors are aggregated across all workers, this hook applies 2024-12-18T01:36:56.8739097Z compression as follows: 2024-12-18T01:36:56.8739185Z 2024-12-18T01:36:56.8739654Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2024-12-18T01:36:56.8739743Z 2024-12-18T01:36:56.8740155Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2024-12-18T01:36:56.8740254Z 2024-12-18T01:36:56.8740654Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2024-12-18T01:36:56.8740751Z 2024-12-18T01:36:56.8740865Z 2. Handles uncompressed tensors: 2024-12-18T01:36:56.8740965Z 2024-12-18T01:36:56.8741463Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2024-12-18T01:36:56.8741550Z 2024-12-18T01:36:56.8741893Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2024-12-18T01:36:56.8741980Z 2024-12-18T01:36:56.8742227Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2024-12-18T01:36:56.8742315Z 2024-12-18T01:36:56.8742564Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2024-12-18T01:36:56.8742872Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2024-12-18T01:36:56.8742959Z 2024-12-18T01:36:56.8743125Z 3.2. Computes each P in Ps, which is equal to MQ; 2024-12-18T01:36:56.8743213Z 2024-12-18T01:36:56.8743339Z 3.3. Allreduces Ps as a batch; 2024-12-18T01:36:56.8743426Z 2024-12-18T01:36:56.8743546Z 3.4. Orthogonalizes each P in Ps; 2024-12-18T01:36:56.8743647Z 2024-12-18T01:36:56.8743845Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2024-12-18T01:36:56.8743947Z 2024-12-18T01:36:56.8744062Z 3.6. Allreduces Qs as a batch; 2024-12-18T01:36:56.8744149Z 2024-12-18T01:36:56.8744459Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2024-12-18T01:36:56.8744572Z 2024-12-18T01:36:56.8744990Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2024-12-18T01:36:56.8745271Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2024-12-18T01:36:56.8745710Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2024-12-18T01:36:56.8745800Z 2024-12-18T01:36:56.8745889Z Args: 2024-12-18T01:36:56.8746324Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2024-12-18T01:36:56.8746674Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2024-12-18T01:36:56.8746904Z and ``min_compression_rate``. 2024-12-18T01:36:56.8747314Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2024-12-18T01:36:56.8747580Z Note that since DDP comm hook only supports single process single device mode, 2024-12-18T01:36:56.8747736Z only exactly one tensor is stored in this bucket. 2024-12-18T01:36:56.8747837Z 2024-12-18T01:36:56.8747953Z Returns: 2024-12-18T01:36:56.8748197Z Future handler of the communication, which updates the gradients in place. 2024-12-18T01:36:56.8748300Z 2024-12-18T01:36:56.8748401Z Example:: 2024-12-18T01:36:56.8748518Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8748792Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2024-12-18T01:36:56.8748958Z start_powerSGD_iter=10, min_compression_rate=0.5) 2024-12-18T01:36:56.8749136Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2024-12-18T01:36:56.8749226Z 2024-12-18T01:36:56.8749518Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8749608Z 2024-12-18T01:36:56.8749726Z warnings.warn(msg) 2024-12-18T01:36:56.8749814Z 2024-12-18T01:36:56.8750011Z --- Parse Warning: 44 / 105 --- 2024-12-18T01:36:56.8751103Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=37. 2024-12-18T01:36:56.8751369Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8751472Z 2024-12-18T01:36:56.8751664Z Averages parameters periodically after the warm-up stage. 2024-12-18T01:36:56.8751766Z 2024-12-18T01:36:56.8752026Z This can be used for running `post-local SGD `_, 2024-12-18T01:36:56.8752230Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2024-12-18T01:36:56.8752489Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2024-12-18T01:36:56.8752578Z 2024-12-18T01:36:56.8752688Z Args: 2024-12-18T01:36:56.8752860Z period (int): The number of steps per model averaging. 2024-12-18T01:36:56.8753132Z Usually the period should be greater than ``1`` to reduce the communication cost. 2024-12-18T01:36:56.8753284Z Otherwise, only DDP needs to be used. 2024-12-18T01:36:56.8753493Z warmup_steps (int): The number of warm-up steps. During this stage, 2024-12-18T01:36:56.8753633Z model averaging is skipped. 2024-12-18T01:36:56.8753823Z process_group: The process group to be used for all-reduce. 2024-12-18T01:36:56.8753986Z If ``None``, the default process group, which 2024-12-18T01:36:56.8754184Z is created by :func:`torch.distributed.init_process_group`, 2024-12-18T01:36:56.8754355Z will be used. (default: ``None``) 2024-12-18T01:36:56.8754455Z 2024-12-18T01:36:56.8754552Z Example:: 2024-12-18T01:36:56.8754651Z 2024-12-18T01:36:56.8754788Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8754899Z >>> import torch 2024-12-18T01:36:56.8755027Z >>> import torch.distributed as dist 2024-12-18T01:36:56.8755346Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2024-12-18T01:36:56.8755717Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:36:56.8755827Z >>> import torch.nn as nn 2024-12-18T01:36:56.8755935Z >>> 2024-12-18T01:36:56.8756117Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:36:56.8756275Z >>> torch.cuda.set_device(rank) 2024-12-18T01:36:56.8756409Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2024-12-18T01:36:56.8756575Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:36:56.8756736Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:36:56.8756826Z >>> ) 2024-12-18T01:36:56.8756989Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:36:56.8757305Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:36:56.8757471Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:56.8757571Z >>> 2024-12-18T01:36:56.8757836Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:36:56.8758006Z >>> # After 100 steps, run model averaging every 4 steps. 2024-12-18T01:36:56.8758319Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:36:56.8758612Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:36:56.8758725Z >>> for step in range(0, 200): 2024-12-18T01:36:56.8758836Z >>> optimizer.zero_grad() 2024-12-18T01:36:56.8758967Z >>> loss = loss_fn(output, labels) 2024-12-18T01:36:56.8759070Z >>> loss.backward() 2024-12-18T01:36:56.8759187Z >>> optimizer.step() 2024-12-18T01:36:56.8759385Z >>> # Will average model parameters globally every 4 steps. Thus, 2024-12-18T01:36:56.8759602Z >>> # inter-node communication only occurs every 4 iterations after 2024-12-18T01:36:56.8759731Z >>> # the initial ``warmup_steps`` period. 2024-12-18T01:36:56.8759895Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:36:56.8759997Z 2024-12-18T01:36:56.8760248Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8760352Z 2024-12-18T01:36:56.8760454Z warnings.warn(msg) 2024-12-18T01:36:56.8760545Z 2024-12-18T01:36:56.8760765Z --- Parse Warning: 45 / 105 --- 2024-12-18T01:36:56.8761944Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18. 2024-12-18T01:36:56.8762221Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8762310Z 2024-12-18T01:36:56.8762663Z Runs hierarchical model averaging (`hierarchical SGD `_). 2024-12-18T01:36:56.8762750Z 2024-12-18T01:36:56.8763069Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2024-12-18T01:36:56.8763273Z by using different periods concurrently after the warm-up stage. 2024-12-18T01:36:56.8763684Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2024-12-18T01:36:56.8764061Z that supports `post-local SGD `_, which essentially only supports 2024-12-18T01:36:56.8764366Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2024-12-18T01:36:56.8764735Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2024-12-18T01:36:56.8765031Z Similarly, the process groups within this class do not have such an intra-machine process 2024-12-18T01:36:56.8765315Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2024-12-18T01:36:56.8765404Z 2024-12-18T01:36:56.8765495Z Args: 2024-12-18T01:36:56.8765767Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2024-12-18T01:36:56.8766013Z process group size, used for initializing process groups of 2024-12-18T01:36:56.8766257Z different sizes in a hierarchy to average parameters concurrently. 2024-12-18T01:36:56.8766473Z Particularly, at each iteration, there will be at most a single 2024-12-18T01:36:56.8766718Z process group that runs averaging -- the period of such group should 2024-12-18T01:36:56.8766961Z have the largest period which the current step can be divided by. 2024-12-18T01:36:56.8767145Z For example, if the dict has three keys: 2, 4, and 8, 2024-12-18T01:36:56.8767355Z then this means totally three process groups will be created to 2024-12-18T01:36:56.8767565Z average parameters every 2, 4, and 8 iterations, respectively. 2024-12-18T01:36:56.8767768Z At the 4th iteration, only the second process group will run 2024-12-18T01:36:56.8767976Z averaging, because the first process group should be a 2024-12-18T01:36:56.8768217Z subset of the second process group, and no need to execute the first 2024-12-18T01:36:56.8768346Z process group redundantly. 2024-12-18T01:36:56.8768567Z On the other hand, the third process group can only be triggered 2024-12-18T01:36:56.8768794Z every 8 iterations, so it will not be triggered at the 4th iteration. 2024-12-18T01:36:56.8769108Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2024-12-18T01:36:56.8769542Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2024-12-18T01:36:56.8769719Z If ``None``, the default process group, which is created 2024-12-18T01:36:56.8769950Z by :func:`torch.distributed.init_process_group`, will be used. 2024-12-18T01:36:56.8770079Z (default: ``None``) 2024-12-18T01:36:56.8770180Z 2024-12-18T01:36:56.8770281Z Example:: 2024-12-18T01:36:56.8770419Z >>> # xdoctest: +SKIP('undefined rank') 2024-12-18T01:36:56.8770545Z >>> from collections import OrderedDict 2024-12-18T01:36:56.8770647Z >>> import torch 2024-12-18T01:36:56.8770783Z >>> import torch.distributed as dist 2024-12-18T01:36:56.8771056Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:36:56.8771177Z >>> PostLocalSGDState, 2024-12-18T01:36:56.8771282Z >>> post_localSGD_hook, 2024-12-18T01:36:56.8771371Z >>> ) 2024-12-18T01:36:56.8771755Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2024-12-18T01:36:56.8771863Z >>> import torch.nn as nn 2024-12-18T01:36:56.8771965Z >>> 2024-12-18T01:36:56.8772173Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2024-12-18T01:36:56.8772300Z >>> torch.cuda.set_device(rank) 2024-12-18T01:36:56.8772442Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:36:56.8772604Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:36:56.8772762Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:36:56.8772850Z >>> ) 2024-12-18T01:36:56.8773013Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:36:56.8773288Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2024-12-18T01:36:56.8773423Z >>> subgroup, _ = dist.new_subgroups() 2024-12-18T01:36:56.8773731Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2024-12-18T01:36:56.8773919Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:56.8774022Z >>> 2024-12-18T01:36:56.8774304Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2024-12-18T01:36:56.8774440Z >>> # the 16 processes every 16 iterations. 2024-12-18T01:36:56.8774632Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2024-12-18T01:36:56.8774902Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2024-12-18T01:36:56.8775213Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:36:56.8775476Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2024-12-18T01:36:56.8775645Z >>> # After 100 steps, run model averaging at two levels. 2024-12-18T01:36:56.8775754Z >>> for step in range(0, 200): 2024-12-18T01:36:56.8775877Z >>> optimizer.zero_grad() 2024-12-18T01:36:56.8775996Z >>> loss = loss_fn(output, labels) 2024-12-18T01:36:56.8776138Z >>> loss.backward() 2024-12-18T01:36:56.8776246Z >>> optimizer.step() 2024-12-18T01:36:56.8776404Z >>> # Average parameters after ``optimizer.step()``. 2024-12-18T01:36:56.8776701Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2024-12-18T01:36:56.8776866Z >>> averager.average_parameters(model.parameters()) 2024-12-18T01:36:56.8776965Z 2024-12-18T01:36:56.8777062Z .. warning :: 2024-12-18T01:36:56.8777313Z The last group size in the dict must be the size of the provided ``process_group``, 2024-12-18T01:36:56.8777558Z which indicates model averaging at the highest level of the hierarchy. 2024-12-18T01:36:56.8777858Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2024-12-18T01:36:56.8777961Z 2024-12-18T01:36:56.8778057Z .. warning :: 2024-12-18T01:36:56.8778305Z `HierarchicalModelAverager` is experimental and subject to change. 2024-12-18T01:36:56.8778395Z 2024-12-18T01:36:56.8778644Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8778745Z 2024-12-18T01:36:56.8778848Z warnings.warn(msg) 2024-12-18T01:36:56.8778952Z 2024-12-18T01:36:56.8779156Z --- Parse Warning: 46 / 105 --- 2024-12-18T01:36:56.8780214Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2024-12-18T01:36:56.8780479Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8780566Z 2024-12-18T01:36:56.8780870Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2024-12-18T01:36:56.8781124Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2024-12-18T01:36:56.8781254Z 2024-12-18T01:36:56.8781421Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2024-12-18T01:36:56.8781524Z 2024-12-18T01:36:56.8781624Z .. warning:: 2024-12-18T01:36:56.8781801Z Current implementation only supports loading Tensors. 2024-12-18T01:36:56.8781900Z 2024-12-18T01:36:56.8782022Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8782134Z >>> sd = {"mode": model} 2024-12-18T01:36:56.8782228Z >>> dcp.load( 2024-12-18T01:36:56.8782321Z >>> sd, 2024-12-18T01:36:56.8782493Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:36:56.8782622Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:36:56.8782758Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:36:56.8782850Z >>> ) 2024-12-18T01:36:56.8782968Z 2024-12-18T01:36:56.8783236Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8783323Z 2024-12-18T01:36:56.8783451Z warnings.warn(msg) 2024-12-18T01:36:56.8783539Z 2024-12-18T01:36:56.8783747Z --- Parse Warning: 47 / 105 --- 2024-12-18T01:36:56.8784789Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2024-12-18T01:36:56.8785054Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8785155Z 2024-12-18T01:36:56.8785516Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2024-12-18T01:36:56.8785850Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2024-12-18T01:36:56.8785974Z metadata file, like Torch Save files. 2024-12-18T01:36:56.8786076Z 2024-12-18T01:36:56.8786303Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2024-12-18T01:36:56.8786394Z 2024-12-18T01:36:56.8786504Z .. warning:: 2024-12-18T01:36:56.8786682Z Current implementation only supports loading Tensors. 2024-12-18T01:36:56.8786784Z 2024-12-18T01:36:56.8786906Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8787010Z >>> sd = {"mode": model} 2024-12-18T01:36:56.8787122Z >>> dcp.load( 2024-12-18T01:36:56.8787215Z >>> sd, 2024-12-18T01:36:56.8787387Z >>> storage_reader=BroadcastingTorchSaveReader(), 2024-12-18T01:36:56.8787519Z >>> planner=DynamicMetaLoadPlanner(), 2024-12-18T01:36:56.8787656Z >>> checkpoint_id="path_to_model.pt" 2024-12-18T01:36:56.8787747Z >>> ) 2024-12-18T01:36:56.8787833Z 2024-12-18T01:36:56.8788097Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8788189Z 2024-12-18T01:36:56.8788305Z warnings.warn(msg) 2024-12-18T01:36:56.8788396Z 2024-12-18T01:36:56.8788587Z --- Parse Warning: 48 / 105 --- 2024-12-18T01:36:56.8789624Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=220. 2024-12-18T01:36:56.8789889Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8789986Z 2024-12-18T01:36:56.8790194Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2024-12-18T01:36:56.8790295Z 2024-12-18T01:36:56.8790462Z This is the current recommended way to checkpoint FSDP. 2024-12-18T01:36:56.8790564Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8790735Z >>> import torch.distributed.checkpoint as dist_cp 2024-12-18T01:36:56.8790830Z >>> # Save 2024-12-18T01:36:56.8790951Z >>> model: torch.nn.Model 2024-12-18T01:36:56.8791078Z >>> optim_params = model.parameters() 2024-12-18T01:36:56.8791266Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2024-12-18T01:36:56.8791354Z >>> # Save 2024-12-18T01:36:56.8791573Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:36:56.8791682Z >>> state_dict = { 2024-12-18T01:36:56.8791843Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2024-12-18T01:36:56.8791967Z >>> "model": model.state_dict() 2024-12-18T01:36:56.8792055Z >>> } 2024-12-18T01:36:56.8792160Z >>> dist_cp.save_state_dict( 2024-12-18T01:36:56.8792279Z >>> state_dict=optim_state, 2024-12-18T01:36:56.8792461Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2024-12-18T01:36:56.8792611Z >>> planner=dist_cp.DefaultSavePlanner(), 2024-12-18T01:36:56.8792730Z >>> ) 2024-12-18T01:36:56.8792829Z >>> 2024-12-18T01:36:56.8792920Z >>> # Load 2024-12-18T01:36:56.8793149Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2024-12-18T01:36:56.8793298Z >>> model_state_dict = model_tp.state_dict() 2024-12-18T01:36:56.8793399Z >>> checkpoint = { 2024-12-18T01:36:56.8793522Z >>> "model": model_state_dict 2024-12-18T01:36:56.8793611Z >>> } 2024-12-18T01:36:56.8793720Z >>> dist_cp.load_state_dict( 2024-12-18T01:36:56.8793869Z >>> state_dict=checkpoint, 2024-12-18T01:36:56.8794062Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2024-12-18T01:36:56.8794212Z >>> planner=dist_cp.DefaultLoadPlanner(), 2024-12-18T01:36:56.8794301Z >>> ) 2024-12-18T01:36:56.8794458Z >>> model.load_state_dict(checkpoint["model_state"]) 2024-12-18T01:36:56.8794561Z >>> 2024-12-18T01:36:56.8794737Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2024-12-18T01:36:56.8794857Z >>> model_state_dict, 2024-12-18T01:36:56.8794975Z >>> optimizer_key="optimizer", 2024-12-18T01:36:56.8795194Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2024-12-18T01:36:56.8795287Z >>> ) 2024-12-18T01:36:56.8795376Z >>> 2024-12-18T01:36:56.8795539Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2024-12-18T01:36:56.8795758Z >>> model, optim, optim_state["optimizer"] 2024-12-18T01:36:56.8795867Z >>> ) 2024-12-18T01:36:56.8795954Z >>> 2024-12-18T01:36:56.8796080Z >>> optim.load_state_dict(flattened_osd) 2024-12-18T01:36:56.8796183Z 2024-12-18T01:36:56.8796435Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8796536Z 2024-12-18T01:36:56.8796640Z warnings.warn(msg) 2024-12-18T01:36:56.8796740Z 2024-12-18T01:36:56.8796937Z --- Parse Warning: 49 / 105 --- 2024-12-18T01:36:56.8798033Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=110. 2024-12-18T01:36:56.8798313Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8798399Z 2024-12-18T01:36:56.8798696Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2024-12-18T01:36:56.8798788Z 2024-12-18T01:36:56.8799094Z SavePlanners are stateful objects that can be used to customize the whole save process. 2024-12-18T01:36:56.8799182Z 2024-12-18T01:36:56.8799459Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:36:56.8799593Z will be visible to the whole process. 2024-12-18T01:36:56.8799681Z 2024-12-18T01:36:56.8799968Z A planner subclass can expect the following sequence of calls during save_state_dict: 2024-12-18T01:36:56.8800059Z 2024-12-18T01:36:56.8800185Z 1) set_up_planner - called on all ranks. 2024-12-18T01:36:56.8800330Z Signals the start of a checkpoint save. 2024-12-18T01:36:56.8800495Z 2024-12-18T01:36:56.8800640Z 2) create_local_plan - called on all ranks. 2024-12-18T01:36:56.8800925Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2024-12-18T01:36:56.8801027Z 2024-12-18T01:36:56.8801212Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:36:56.8801413Z Takes the SavePlan from all ranks and make any global decision. 2024-12-18T01:36:56.8801516Z 2024-12-18T01:36:56.8801635Z 4) finish_plan - called on all ranks. 2024-12-18T01:36:56.8801869Z This gives each rank a chance to adjust to global planning decisions. 2024-12-18T01:36:56.8801957Z 2024-12-18T01:36:56.8802115Z 5) resolve_data - called multiple times on each rank 2024-12-18T01:36:56.8802376Z Lookups a value on the `state_dict` for the storage layer to write. 2024-12-18T01:36:56.8802462Z 2024-12-18T01:36:56.8803007Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2024-12-18T01:36:56.8803198Z most changes can be expressed by changes in a single method. 2024-12-18T01:36:56.8803300Z 2024-12-18T01:36:56.8803429Z There are 3 usual patterns of extension: 2024-12-18T01:36:56.8803515Z 2024-12-18T01:36:56.8803823Z Rewriting state_dict. This is the simplest way to extend the save process as it 2024-12-18T01:36:56.8804050Z doesn't requite understanding the intrincacies of how SavePlan works: 2024-12-18T01:36:56.8804153Z 2024-12-18T01:36:56.8804275Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8804412Z >>> class RenamePlanner(DefaultSavePlanner): 2024-12-18T01:36:56.8804529Z >>> def set_up_planner( 2024-12-18T01:36:56.8804620Z >>> self, 2024-12-18T01:36:56.8804751Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:36:56.8804884Z >>> storage_meta: Optional[StorageMeta], 2024-12-18T01:36:56.8805041Z >>> is_coordinator: bool, 2024-12-18T01:36:56.8805137Z >>> ) -> None: 2024-12-18T01:36:56.8805256Z >>> # prefix all keys with `foo_`` 2024-12-18T01:36:56.8805559Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2024-12-18T01:36:56.8805649Z 2024-12-18T01:36:56.8805993Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2024-12-18T01:36:56.8806081Z 2024-12-18T01:36:56.8806213Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8806347Z >>> class FP16Planner(DefaultSavePlanner): 2024-12-18T01:36:56.8806460Z >>> def create_local_plan(self): 2024-12-18T01:36:56.8806594Z >>> plan = super().create_local_plan() 2024-12-18T01:36:56.8806694Z >>> for p in plan: 2024-12-18T01:36:56.8806827Z >>> if p.tensor_data is not None: 2024-12-18T01:36:56.8806995Z >>> p.tensor_data.properties.dtype = torch.float16 2024-12-18T01:36:56.8807095Z >>> return plan 2024-12-18T01:36:56.8807196Z >>> 2024-12-18T01:36:56.8807317Z >>> def resolve_data(self, write_item): 2024-12-18T01:36:56.8807457Z >>> item = super().resolve_data(write_item) 2024-12-18T01:36:56.8807732Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2024-12-18T01:36:56.8807831Z 2024-12-18T01:36:56.8808165Z Using the global planning step to make central decisions that can't be made individually by each rank 2024-12-18T01:36:56.8808254Z 2024-12-18T01:36:56.8808384Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8808502Z >>> from itertools import zip_longest 2024-12-18T01:36:56.8808632Z >>> from dataclasses import replace 2024-12-18T01:36:56.8808808Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2024-12-18T01:36:56.8809088Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2024-12-18T01:36:56.8809282Z >>> # This sample doesn't handle ShardedTensors 2024-12-18T01:36:56.8809417Z >>> def create_global_plan(self, all_plans): 2024-12-18T01:36:56.8809589Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2024-12-18T01:36:56.8809694Z >>> items_per_rank = [ 2024-12-18T01:36:56.8809852Z >>> [item for item in items if item is not None] 2024-12-18T01:36:56.8810017Z >>> for items in zip(*zip_longest(*iters), strict=True) 2024-12-18T01:36:56.8810110Z >>> ] 2024-12-18T01:36:56.8810231Z >>> all_plans = [ 2024-12-18T01:36:56.8810351Z >>> replace(plan, items=items) 2024-12-18T01:36:56.8810560Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2024-12-18T01:36:56.8810684Z >>> ] 2024-12-18T01:36:56.8810844Z >>> return super().create_global_plan(all_plans) 2024-12-18T01:36:56.8810933Z 2024-12-18T01:36:56.8811203Z Finally, some planners need to save additional metadata in the checkpoint, this is 2024-12-18T01:36:56.8811486Z accomplished by having each rank contribute their data items in the local plan and 2024-12-18T01:36:56.8811606Z the global planner aggregate them: 2024-12-18T01:36:56.8811705Z 2024-12-18T01:36:56.8811852Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8812016Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2024-12-18T01:36:56.8812161Z >>> def create_local_plan(self) -> SavePlan: 2024-12-18T01:36:56.8812286Z >>> plan = super().create_local_plan() 2024-12-18T01:36:56.8812464Z >>> return replace(plan, planner_data="per-rank-data") 2024-12-18T01:36:56.8812557Z >>> 2024-12-18T01:36:56.8812869Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2024-12-18T01:36:56.8813070Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2024-12-18T01:36:56.8813260Z >>> merged_data = [p.planner_data for p in global_plan] 2024-12-18T01:36:56.8813452Z >>> metadata = replace(metadata, planner_data=merged_data) 2024-12-18T01:36:56.8813573Z >>> return global_plan, metadata 2024-12-18T01:36:56.8813677Z 2024-12-18T01:36:56.8813930Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8814032Z 2024-12-18T01:36:56.8814135Z warnings.warn(msg) 2024-12-18T01:36:56.8814226Z 2024-12-18T01:36:56.8814460Z --- Parse Warning: 50 / 105 --- 2024-12-18T01:36:56.8815392Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=272. 2024-12-18T01:36:56.8815672Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8815761Z 2024-12-18T01:36:56.8816061Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2024-12-18T01:36:56.8816155Z 2024-12-18T01:36:56.8816437Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2024-12-18T01:36:56.8816539Z 2024-12-18T01:36:56.8816817Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2024-12-18T01:36:56.8816956Z will be visible to the whole process. 2024-12-18T01:36:56.8817044Z 2024-12-18T01:36:56.8817320Z A planner subclass can expect the following sequence of calls during load_state_dict: 2024-12-18T01:36:56.8817422Z 2024-12-18T01:36:56.8817548Z 1) set_up_planner - called on all ranks. 2024-12-18T01:36:56.8817698Z Signals the start of loading a checkpoint. 2024-12-18T01:36:56.8817789Z 2024-12-18T01:36:56.8817937Z 2) create_local_plan - called on all ranks. 2024-12-18T01:36:56.8818220Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2024-12-18T01:36:56.8818337Z 2024-12-18T01:36:56.8818537Z 3) create_global_plan - called on the coordinator rank only. 2024-12-18T01:36:56.8818737Z Takes the LoadPlan from all ranks and make any global decision. 2024-12-18T01:36:56.8818842Z 2024-12-18T01:36:56.8818998Z 4) load_bytes - called multiple times on each rank 2024-12-18T01:36:56.8819172Z This is called once per non-tensor value in state_dict. 2024-12-18T01:36:56.8819272Z 2024-12-18T01:36:56.8819495Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2024-12-18T01:36:56.8819691Z They are called in pair for each Tensor value in state_dict. 2024-12-18T01:36:56.8819779Z 2024-12-18T01:36:56.8820095Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2024-12-18T01:36:56.8820310Z most changes can be expressed by changes in a single method. 2024-12-18T01:36:56.8820396Z 2024-12-18T01:36:56.8820547Z There are two usual patterns of extension: 2024-12-18T01:36:56.8820638Z 2024-12-18T01:36:56.8820904Z Rewriting state_dict. This is the simplest way to extend the load process as it 2024-12-18T01:36:56.8821160Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2024-12-18T01:36:56.8821472Z to keep a reference to the original state_dict as load happens in place so 2024-12-18T01:36:56.8821601Z we need to be able to perform it in place 2024-12-18T01:36:56.8821689Z 2024-12-18T01:36:56.8821819Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8821958Z >>> class RenamePlanner(DefaultLoadPlanner): 2024-12-18T01:36:56.8822075Z >>> def set_up_planner( 2024-12-18T01:36:56.8822166Z >>> self, 2024-12-18T01:36:56.8822284Z >>> state_dict: STATE_DICT_TYPE, 2024-12-18T01:36:56.8822404Z >>> metadata: Metadata, 2024-12-18T01:36:56.8822513Z >>> is_coordinator: bool, 2024-12-18T01:36:56.8822651Z >>> ) -> None: 2024-12-18T01:36:56.8822788Z >>> self.original_state_dict = state_dict 2024-12-18T01:36:56.8822977Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2024-12-18T01:36:56.8823066Z >>> 2024-12-18T01:36:56.8823193Z >>> if self.flatten_sharded_tensors: 2024-12-18T01:36:56.8823361Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2024-12-18T01:36:56.8823449Z >>> 2024-12-18T01:36:56.8823578Z >>> if self.flatten_state_dict: 2024-12-18T01:36:56.8823764Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2024-12-18T01:36:56.8823851Z >>> 2024-12-18T01:36:56.8823980Z >>> self.state_dict = state_dict 2024-12-18T01:36:56.8824091Z >>> self.metadata = metadata 2024-12-18T01:36:56.8824239Z >>> self.is_coordinator = is_coordinator 2024-12-18T01:36:56.8824328Z >>> 2024-12-18T01:36:56.8824468Z >>> def load_bytes(self, read_item, value): 2024-12-18T01:36:56.8824583Z >>> # Remove the "foo_" prefix 2024-12-18T01:36:56.8824899Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2024-12-18T01:36:56.8825000Z 2024-12-18T01:36:56.8825088Z 2024-12-18T01:36:56.8825365Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2024-12-18T01:36:56.8825452Z 2024-12-18T01:36:56.8825571Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.8825744Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2024-12-18T01:36:56.8825866Z >>> def resolve_tensor(self, read_item): 2024-12-18T01:36:56.8826015Z >>> tensor = super().resolve_tensor(read_item) 2024-12-18T01:36:56.8826167Z >>> return torch.empty_like(tensor, device="cpu") 2024-12-18T01:36:56.8826269Z >>> 2024-12-18T01:36:56.8826403Z >>> def commit_tensor(self, read_item, tensor): 2024-12-18T01:36:56.8826563Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2024-12-18T01:36:56.8826690Z 2024-12-18T01:36:56.8826941Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8827040Z 2024-12-18T01:36:56.8827143Z warnings.warn(msg) 2024-12-18T01:36:56.8827290Z 2024-12-18T01:36:56.8827551Z --- Parse Warning: 51 / 105 --- 2024-12-18T01:36:56.8842292Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2024-12-18T01:36:56.8842578Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8842682Z 2024-12-18T01:36:56.8842835Z Load a distributed ``state_dict`` in SPMD style. 2024-12-18T01:36:56.8843040Z 2024-12-18T01:36:56.8843229Z Each rank will try to read the least amount of data necessary 2024-12-18T01:36:56.8843478Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2024-12-18T01:36:56.8843745Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2024-12-18T01:36:56.8843833Z 2024-12-18T01:36:56.8844106Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:36:56.8844399Z load will first call ``state_dict`` before attempting deserialization, followed by 2024-12-18T01:36:56.8844587Z ``load_state_dict`` once the deserialization is complete. 2024-12-18T01:36:56.8844845Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2024-12-18T01:36:56.8845002Z it in the ``state_dict`` with the deserialized object. 2024-12-18T01:36:56.8845104Z 2024-12-18T01:36:56.8845216Z .. warning:: 2024-12-18T01:36:56.8845408Z All tensors in ``state_dict`` must be allocated on their 2024-12-18T01:36:56.8845612Z destination device *prior to* calling this function. 2024-12-18T01:36:56.8845720Z 2024-12-18T01:36:56.8845950Z All non-tensor data is loaded using `torch.load()` and modified in place 2024-12-18T01:36:56.8846050Z on state_dict. 2024-12-18T01:36:56.8846152Z 2024-12-18T01:36:56.8846250Z .. warning:: 2024-12-18T01:36:56.8846472Z Users must call `load_state_dict` on the root module to ensure load 2024-12-18T01:36:56.8846659Z pos-processing and non-tensor data properly propagates. 2024-12-18T01:36:56.8846747Z 2024-12-18T01:36:56.8846852Z .. note: 2024-12-18T01:36:56.8847080Z If no process group is initialized, this function will assume the intent 2024-12-18T01:36:56.8847322Z is to load a checkpoint into the local process. This can be useful in the 2024-12-18T01:36:56.8847568Z case of local inference, and when using regular Tensors (as opposed to DTensor 2024-12-18T01:36:56.8847693Z or ShardedTensor) 2024-12-18T01:36:56.8847780Z 2024-12-18T01:36:56.8847872Z .. note: 2024-12-18T01:36:56.8848031Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:36:56.8848120Z 2024-12-18T01:36:56.8848221Z Args: 2024-12-18T01:36:56.8848382Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:36:56.8848532Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:36:56.8848760Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:36:56.8848967Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:36:56.8849147Z It can also be a key if the storage is a key-value store. 2024-12-18T01:36:56.8849250Z (Default: ``None``) 2024-12-18T01:36:56.8849400Z storage_reader (Optional[StorageReader]): 2024-12-18T01:36:56.8849608Z Instance of StorageWriter used to perform reads. If this is not 2024-12-18T01:36:56.8849817Z specified, DCP will automatically infer the reader based on the 2024-12-18T01:36:56.8850033Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:36:56.8850185Z be raised. (Default: ``None``) 2024-12-18T01:36:56.8850319Z planner (Optional[LoadPlanner]): 2024-12-18T01:36:56.8850521Z Instance of LoadPlanner. If this is not specificed, the default 2024-12-18T01:36:56.8850669Z planner will be used. (Default: ``None``) 2024-12-18T01:36:56.8850807Z process_group (Optional[ProcessGroup]): 2024-12-18T01:36:56.8850993Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:36:56.8851108Z (Default: ``None``) 2024-12-18T01:36:56.8851196Z 2024-12-18T01:36:56.8851301Z Returns: 2024-12-18T01:36:56.8851394Z None. 2024-12-18T01:36:56.8851483Z 2024-12-18T01:36:56.8851589Z Examples 2024-12-18T01:36:56.8851720Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8851841Z >>> my_model = MyModule() 2024-12-18T01:36:56.8851987Z >>> optimizer = Adagrad(my_model.parameters()) 2024-12-18T01:36:56.8852138Z >>> model_state_dict = my_model.state_dict() 2024-12-18T01:36:56.8852443Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2024-12-18T01:36:56.8852534Z 2024-12-18T01:36:56.8852709Z >>> torch.distributed.checkpoint.load_state_dict( 2024-12-18T01:36:56.8852856Z >>> state_dict=model_state_dict, 2024-12-18T01:36:56.8852998Z >>> storage_reader=fs_storage_reader, 2024-12-18T01:36:56.8853092Z >>> ) 2024-12-18T01:36:56.8853181Z 2024-12-18T01:36:56.8853395Z >>> # module.load_state_dict() function might have customized steps 2024-12-18T01:36:56.8853527Z >>> # to flush the state_dict, must call it to 2024-12-18T01:36:56.8853654Z >>> # ensure correct behavior. 2024-12-18T01:36:56.8853792Z >>> my_model.load_state_dict(model_state_dict) 2024-12-18T01:36:56.8853896Z 2024-12-18T01:36:56.8853992Z .. note:: 2024-12-18T01:36:56.8854230Z load_state_dict uses collectives to coordinate reads across ranks. 2024-12-18T01:36:56.8854460Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:36:56.8854690Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:36:56.8854933Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:36:56.8855162Z and it is the user's responsibility to ensure that this is set so that each 2024-12-18T01:36:56.8855363Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:36:56.8855450Z 2024-12-18T01:36:56.8855698Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8855802Z 2024-12-18T01:36:56.8855906Z warnings.warn(msg) 2024-12-18T01:36:56.8856010Z 2024-12-18T01:36:56.8856263Z --- Parse Warning: 52 / 105 --- 2024-12-18T01:36:56.8857197Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2024-12-18T01:36:56.8857473Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8857561Z 2024-12-18T01:36:56.8857701Z Save a distributed model in SPMD style. 2024-12-18T01:36:56.8857788Z 2024-12-18T01:36:56.8857993Z This function is different from ``torch.save()`` as it handles 2024-12-18T01:36:56.8858248Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2024-12-18T01:36:56.8858337Z 2024-12-18T01:36:56.8858605Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2024-12-18T01:36:56.8858764Z save will call ``state_dict`` before serialization. 2024-12-18T01:36:56.8858865Z 2024-12-18T01:36:56.8858962Z .. warning:: 2024-12-18T01:36:56.8859214Z There is no guarantees of Backwards Compatibility across PyTorch versions 2024-12-18T01:36:56.8859374Z for saved state_dicts. 2024-12-18T01:36:56.8859461Z 2024-12-18T01:36:56.8859572Z .. warning:: 2024-12-18T01:36:56.8859782Z If using the `process_group` argument, make sure that only its ranks 2024-12-18T01:36:56.8860006Z call `save_state_dict` and that all data in state_dict belong to it. 2024-12-18T01:36:56.8860093Z 2024-12-18T01:36:56.8860185Z .. note:: 2024-12-18T01:36:56.8860461Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2024-12-18T01:36:56.8860719Z the shard_group should be calling `save_state_dict` and the corresponding process 2024-12-18T01:36:56.8860850Z group needs to be passed in. 2024-12-18T01:36:56.8860940Z 2024-12-18T01:36:56.8861078Z .. note:: 2024-12-18T01:36:56.8861345Z If no process group is available, this function assumes the intention is to save the 2024-12-18T01:36:56.8861466Z state_dict in the local process. 2024-12-18T01:36:56.8861571Z 2024-12-18T01:36:56.8861661Z .. note: 2024-12-18T01:36:56.8861816Z Rank 0 is assumed to be the coordinator rank. 2024-12-18T01:36:56.8861905Z 2024-12-18T01:36:56.8861994Z 2024-12-18T01:36:56.8862097Z Args: 2024-12-18T01:36:56.8862281Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:36:56.8862446Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:36:56.8862660Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:36:56.8862878Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:36:56.8863046Z It can also be a key if the storage is a key-value store. 2024-12-18T01:36:56.8863151Z (Default: ``None``) 2024-12-18T01:36:56.8863303Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:36:56.8863537Z Instance of StorageWriter used to perform writes. If this is not 2024-12-18T01:36:56.8863759Z specified, DCP will automatically infer the writer based on the 2024-12-18T01:36:56.8863962Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:36:56.8864089Z be raised. (Default: ``None``) 2024-12-18T01:36:56.8864217Z planner (Optional[SavePlanner]): 2024-12-18T01:36:56.8864420Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:36:56.8864567Z planner will be used. (Default: ``None``) 2024-12-18T01:36:56.8864704Z process_group (Optional[ProcessGroup]): 2024-12-18T01:36:56.8864902Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:36:56.8865005Z (Default: ``None``) 2024-12-18T01:36:56.8865095Z 2024-12-18T01:36:56.8865204Z Returns: 2024-12-18T01:36:56.8865368Z Metadata: Metadata object for the saved checkpoint. 2024-12-18T01:36:56.8865472Z 2024-12-18T01:36:56.8865567Z Example: 2024-12-18T01:36:56.8865674Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8865796Z >>> my_model = MyModule() 2024-12-18T01:36:56.8865884Z 2024-12-18T01:36:56.8866016Z >>> state_dict = {"model": my_model} 2024-12-18T01:36:56.8866104Z 2024-12-18T01:36:56.8866427Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:36:56.8866570Z >>> torch.distributed.checkpoint.save( 2024-12-18T01:36:56.8866681Z >>> state_dict=state_dict, 2024-12-18T01:36:56.8866819Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:36:56.8866908Z >>> ) 2024-12-18T01:36:56.8867011Z 2024-12-18T01:36:56.8867103Z .. note:: 2024-12-18T01:36:56.8867320Z save_state_dict uses collectives to coordinate writes across ranks. 2024-12-18T01:36:56.8867552Z For NCCL-based process groups, internal tensor representations of 2024-12-18T01:36:56.8867785Z objects must be moved to the GPU device before communication takes place. 2024-12-18T01:36:56.8868049Z In this case, the device used is given by ``torch.cuda.current_device()`` 2024-12-18T01:36:56.8868257Z and it is the user's responsibility to ensure that this is set so that 2024-12-18T01:36:56.8868465Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2024-12-18T01:36:56.8868559Z 2024-12-18T01:36:56.8868823Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8868911Z 2024-12-18T01:36:56.8869013Z warnings.warn(msg) 2024-12-18T01:36:56.8869111Z 2024-12-18T01:36:56.8869321Z --- Parse Warning: 53 / 105 --- 2024-12-18T01:36:56.8870298Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2024-12-18T01:36:56.8870593Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8870872Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2024-12-18T01:36:56.8871159Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2024-12-18T01:36:56.8871248Z 2024-12-18T01:36:56.8871385Z .. warning:: 2024-12-18T01:36:56.8871549Z This feature is experimental and subject to change. 2024-12-18T01:36:56.8871650Z 2024-12-18T01:36:56.8871741Z Args: 2024-12-18T01:36:56.8871912Z state_dict (Dict[str, Any]): The state_dict to save. 2024-12-18T01:36:56.8872061Z checkpoint_id (Union[str, os.PathLike, None]): 2024-12-18T01:36:56.8872274Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2024-12-18T01:36:56.8872491Z depends on the storage. It can be a path to a folder or to a file. 2024-12-18T01:36:56.8872687Z It can also be a key if the storage is a key-value store. 2024-12-18T01:36:56.8872806Z (Default: ``None``) 2024-12-18T01:36:56.8872945Z storage_writer (Optional[StorageWriter]): 2024-12-18T01:36:56.8873169Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2024-12-18T01:36:56.8873409Z this is not specified, DCP will automatically infer the writer based on the 2024-12-18T01:36:56.8873617Z checkpoint_id. If checkpoint_id is also None, an exception will 2024-12-18T01:36:56.8873747Z be raised. (Default: ``None``) 2024-12-18T01:36:56.8873870Z planner (Optional[SavePlanner]): 2024-12-18T01:36:56.8874087Z Instance of SavePlanner. If this is not specificed, the default 2024-12-18T01:36:56.8874226Z planner will be used. (Default: ``None``) 2024-12-18T01:36:56.8874377Z process_group (Optional[ProcessGroup]): 2024-12-18T01:36:56.8874564Z ProcessGroup to be used for cross-rank synchronization. 2024-12-18T01:36:56.8874669Z (Default: ``None``) 2024-12-18T01:36:56.8874769Z 2024-12-18T01:36:56.8874862Z Returns: 2024-12-18T01:36:56.8875083Z Future: A future holding the resultant Metadata object from `save`. 2024-12-18T01:36:56.8875170Z 2024-12-18T01:36:56.8875267Z Example: 2024-12-18T01:36:56.8875382Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.8875489Z >>> my_model = MyModule() 2024-12-18T01:36:56.8875591Z 2024-12-18T01:36:56.8875809Z >>> state_dict = {"model": my_model} 2024-12-18T01:36:56.8875900Z 2024-12-18T01:36:56.8876222Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2024-12-18T01:36:56.8876437Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2024-12-18T01:36:56.8876566Z >>> state_dict=state_dict, 2024-12-18T01:36:56.8876696Z >>> storage_writer=fs_storage_writer, 2024-12-18T01:36:56.8876835Z >>> ) 2024-12-18T01:36:56.8876930Z >>> 2024-12-18T01:36:56.8877034Z >>> # ... do some work ... 2024-12-18T01:36:56.8877137Z >>> 2024-12-18T01:36:56.8877256Z >>> checkpoint_future.result() 2024-12-18T01:36:56.8877357Z 2024-12-18T01:36:56.8877451Z 2024-12-18T01:36:56.8877703Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8877807Z 2024-12-18T01:36:56.8877909Z warnings.warn(msg) 2024-12-18T01:36:56.8878015Z 2024-12-18T01:36:56.8878227Z --- Parse Warning: 54 / 105 --- 2024-12-18T01:36:56.8879256Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2024-12-18T01:36:56.8879552Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8879642Z 2024-12-18T01:36:56.8879858Z Initialize rendezvous event object and record its operations. 2024-12-18T01:36:56.8879946Z 2024-12-18T01:36:56.8880051Z Args: 2024-12-18T01:36:56.8880183Z run_id (str): The run id of the rendezvous. 2024-12-18T01:36:56.8880376Z message (str): The message describing the event. 2024-12-18T01:36:56.8880635Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2024-12-18T01:36:56.8880826Z name (str): Event name. (E.g. Current action being performed). 2024-12-18T01:36:56.8880959Z hostname (str): Hostname of the node. 2024-12-18T01:36:56.8881105Z pid (Optional[int]): The process id of the node. 2024-12-18T01:36:56.8881363Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2024-12-18T01:36:56.8881655Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2024-12-18T01:36:56.8881831Z rank (Optional[int]): The rank of the node, if known. 2024-12-18T01:36:56.8881923Z Returns: 2024-12-18T01:36:56.8882013Z None 2024-12-18T01:36:56.8882117Z Example: 2024-12-18T01:36:56.8882248Z >>> # See DynamicRendezvousHandler class 2024-12-18T01:36:56.8882359Z >>> def _record( 2024-12-18T01:36:56.8882450Z ... self, 2024-12-18T01:36:56.8882549Z ... message: str, 2024-12-18T01:36:56.8882709Z ... node_state: NodeState = NodeState.RUNNING, 2024-12-18T01:36:56.8882823Z ... rank: Optional[int] = None, 2024-12-18T01:36:56.8882932Z ... ) -> None: 2024-12-18T01:36:56.8883056Z ... construct_and_record_rdzv_event( 2024-12-18T01:36:56.8883239Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2024-12-18T01:36:56.8883364Z ... run_id=self._settings.run_id, 2024-12-18T01:36:56.8883472Z ... message=message, 2024-12-18T01:36:56.8883598Z ... node_state=node_state, 2024-12-18T01:36:56.8883724Z ... hostname=self._this_node.addr, 2024-12-18T01:36:56.8883852Z ... pid=self._this_node.pid, 2024-12-18T01:36:56.8883981Z ... local_id=self._this_node.local_id, 2024-12-18T01:36:56.8884081Z ... rank=rank, 2024-12-18T01:36:56.8884183Z ... ) 2024-12-18T01:36:56.8884270Z 2024-12-18T01:36:56.8884532Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8884619Z 2024-12-18T01:36:56.8884722Z warnings.warn(msg) 2024-12-18T01:36:56.8884824Z 2024-12-18T01:36:56.8885014Z --- Parse Warning: 55 / 105 --- 2024-12-18T01:36:56.8885919Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=113. 2024-12-18T01:36:56.8886208Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8886305Z 2024-12-18T01:36:56.8886475Z This configures FSDP-native mixed precision training. 2024-12-18T01:36:56.8886581Z 2024-12-18T01:36:56.8886676Z Attributes: 2024-12-18T01:36:56.8886914Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2024-12-18T01:36:56.8887127Z parameters during forward and backward and thus the dtype for 2024-12-18T01:36:56.8887346Z forward and backward computation. Outside forward and backward, the 2024-12-18T01:36:56.8887555Z *sharded* parameters are kept in full precision (e.g. for the 2024-12-18T01:36:56.8887764Z optimizer step), and for model checkpointing, the parameters are 2024-12-18T01:36:56.8887968Z always saved in full precision. (Default: ``None``) 2024-12-18T01:36:56.8888184Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:36:56.8888405Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2024-12-18T01:36:56.8888598Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2024-12-18T01:36:56.8888800Z the ``param_dtype`` value, still running gradient reduction in low 2024-12-18T01:36:56.8889049Z precision. This is permitted to differ from ``param_dtype``, e.g. 2024-12-18T01:36:56.8889253Z to force gradient reduction to run in full precision. (Default: 2024-12-18T01:36:56.8889359Z ``None``) 2024-12-18T01:36:56.8889571Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2024-12-18T01:36:56.8889775Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2024-12-18T01:36:56.8889989Z ``buffer_dtype`` in the first forward pass and keeps them in that 2024-12-18T01:36:56.8890311Z dtype thereafter. For model checkpointing, the buffers are saved 2024-12-18T01:36:56.8890512Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2024-12-18T01:36:56.8890606Z ``None``) 2024-12-18T01:36:56.8890815Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2024-12-18T01:36:56.8891036Z gradients to full precision after the backward pass in preparation 2024-12-18T01:36:56.8891241Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2024-12-18T01:36:56.8891455Z in the dtype used for gradient reduction, which can save memory if 2024-12-18T01:36:56.8891663Z using a custom optimizer that supports running in low precision. 2024-12-18T01:36:56.8891780Z (Default: ``False``) 2024-12-18T01:36:56.8891991Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2024-12-18T01:36:56.8892209Z its forward args and kwargs to ``param_dtype``. This is to ensure 2024-12-18T01:36:56.8892423Z that parameter and input dtypes match for forward computation, as 2024-12-18T01:36:56.8892640Z required by many ops. This may need to be set to ``True`` when only 2024-12-18T01:36:56.8892853Z applying mixed precision to some but not all FSDP modules, in which 2024-12-18T01:36:56.8893066Z case a mixed-precision FSDP submodule needs to recast its inputs. 2024-12-18T01:36:56.8893177Z (Default: ``False``) 2024-12-18T01:36:56.8893393Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2024-12-18T01:36:56.8893604Z casts its forward args and kwargs to ``param_dtype``, overriding 2024-12-18T01:36:56.8893792Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2024-12-18T01:36:56.8893952Z this does not do anything. (Default: ``True``) 2024-12-18T01:36:56.8894170Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2024-12-18T01:36:56.8894357Z module classes to ignore for mixed precision when using an 2024-12-18T01:36:56.8894576Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2024-12-18T01:36:56.8894784Z applied to them separately with mixed precision disabled (meaning 2024-12-18T01:36:56.8895005Z that the final FSDP construction would deviate from the specified 2024-12-18T01:36:56.8895196Z policy). If ``auto_wrap_policy`` is not specified, then this does 2024-12-18T01:36:56.8895405Z not do anything. This API is experimental and subject to change. 2024-12-18T01:36:56.8895515Z (Default: ``(_BatchNorm,)``) 2024-12-18T01:36:56.8895600Z 2024-12-18T01:36:56.8895786Z .. note:: This API is experimental and subject to change. 2024-12-18T01:36:56.8895873Z 2024-12-18T01:36:56.8896102Z .. note:: Only floating point tensors are cast to their specified dtypes. 2024-12-18T01:36:56.8896216Z 2024-12-18T01:36:56.8896413Z .. note:: In ``summon_full_params``, parameters are forced to full 2024-12-18T01:36:56.8896530Z precision, but buffers are not. 2024-12-18T01:36:56.8896619Z 2024-12-18T01:36:56.8896833Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2024-12-18T01:36:56.8897042Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2024-12-18T01:36:56.8897309Z Disabling FSDP's mixed precision for those norm modules only means that 2024-12-18T01:36:56.8897517Z the affine parameters are kept in ``float32``. However, this incurs 2024-12-18T01:36:56.8897763Z separate all-gathers and reduce-scatters for those norm modules, which 2024-12-18T01:36:56.8898171Z may be inefficient, so if the workload permits, the user should prefer 2024-12-18T01:36:56.8898325Z to still apply mixed precision to those modules. 2024-12-18T01:36:56.8898429Z 2024-12-18T01:36:56.8898634Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2024-12-18T01:36:56.8898924Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2024-12-18T01:36:56.8899153Z modules will have FSDP applied to them separately with mixed precision 2024-12-18T01:36:56.8899343Z disabled. See the ``_module_classes_to_ignore`` argument. 2024-12-18T01:36:56.8899429Z 2024-12-18T01:36:56.8899640Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2024-12-18T01:36:56.8899868Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2024-12-18T01:36:56.8900043Z its ``cast_root_forward_inputs`` takes precedence over its 2024-12-18T01:36:56.8900234Z ``cast_forward_inputs``. For non-root FSDP instances, their 2024-12-18T01:36:56.8900447Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2024-12-18T01:36:56.8900686Z sufficient for the typical case where each FSDP instance has the same 2024-12-18T01:36:56.8900914Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2024-12-18T01:36:56.8901098Z ``param_dtype`` at the beginning of the model's forward pass. 2024-12-18T01:36:56.8901200Z 2024-12-18T01:36:56.8901409Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2024-12-18T01:36:56.8901655Z configurations, we recommend setting individual ``cast_forward_inputs`` 2024-12-18T01:36:56.8901859Z values to configure casting inputs or not before each instance's 2024-12-18T01:36:56.8902069Z forward. In such a case, since the casts happen before each FSDP 2024-12-18T01:36:56.8902283Z instance's forward, a parent FSDP instance should have its non-FSDP 2024-12-18T01:36:56.8902786Z submodules run before its FSDP submodules to avoid the activation dtype 2024-12-18T01:36:56.8903013Z being changed due to a different ``MixedPrecision`` configuration. 2024-12-18T01:36:56.8903102Z 2024-12-18T01:36:56.8903213Z Example:: 2024-12-18T01:36:56.8903300Z 2024-12-18T01:36:56.8903439Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8903688Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2024-12-18T01:36:56.8903791Z >>> model[1] = FSDP( 2024-12-18T01:36:56.8903904Z >>> model[1], 2024-12-18T01:36:56.8904210Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2024-12-18T01:36:56.8904316Z >>> ) 2024-12-18T01:36:56.8904417Z >>> model = FSDP( 2024-12-18T01:36:56.8904513Z >>> model, 2024-12-18T01:36:56.8904836Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2024-12-18T01:36:56.8904928Z >>> ) 2024-12-18T01:36:56.8905030Z 2024-12-18T01:36:56.8905245Z The above shows a working example. On the other hand, if ``model[1]`` 2024-12-18T01:36:56.8905496Z were replaced with ``model[0]``, meaning that the submodule using 2024-12-18T01:36:56.8905722Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2024-12-18T01:36:56.8905945Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2024-12-18T01:36:56.8906053Z ones. 2024-12-18T01:36:56.8906140Z 2024-12-18T01:36:56.8906242Z 2024-12-18T01:36:56.8906528Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8906615Z 2024-12-18T01:36:56.8906731Z warnings.warn(msg) 2024-12-18T01:36:56.8906816Z 2024-12-18T01:36:56.8907059Z --- Parse Warning: 56 / 105 --- 2024-12-18T01:36:56.8908207Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=649. 2024-12-18T01:36:56.8908516Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8908763Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:36:56.8908851Z 2024-12-18T01:36:56.8909122Z Also takes (optional) configuration for the model's and optimizer's state dict. 2024-12-18T01:36:56.8909331Z The target module does not have to be a FSDP module. If the target 2024-12-18T01:36:56.8909553Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2024-12-18T01:36:56.8909640Z 2024-12-18T01:36:56.8909849Z .. note:: This API should be called for only the top-level (root) 2024-12-18T01:36:56.8909940Z module. 2024-12-18T01:36:56.8910029Z 2024-12-18T01:36:56.8910251Z .. note:: This API enables users to transparently use the conventional 2024-12-18T01:36:56.8910448Z ``state_dict`` API to take model checkpoints in cases where the 2024-12-18T01:36:56.8910670Z root FSDP module is wrapped by another ``nn.Module``. For example, 2024-12-18T01:36:56.8910878Z the following will ensure ``state_dict`` is called on all non-FSDP 2024-12-18T01:36:56.8911122Z instances, while dispatching into `sharded_state_dict` implementation 2024-12-18T01:36:56.8911217Z for FSDP: 2024-12-18T01:36:56.8911304Z 2024-12-18T01:36:56.8911414Z Example:: 2024-12-18T01:36:56.8911502Z 2024-12-18T01:36:56.8911648Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8911761Z >>> model = DDP(FSDP(...)) 2024-12-18T01:36:56.8911888Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:56.8911983Z >>> model, 2024-12-18T01:36:56.8912119Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:36:56.8912347Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2024-12-18T01:36:56.8912576Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2024-12-18T01:36:56.8912703Z >>> ) 2024-12-18T01:36:56.8912833Z >>> param_state_dict = model.state_dict() 2024-12-18T01:36:56.8913017Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:36:56.8913103Z 2024-12-18T01:36:56.8913193Z Args: 2024-12-18T01:36:56.8913336Z module (torch.nn.Module): Root module. 2024-12-18T01:36:56.8913568Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:36:56.8913813Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2024-12-18T01:36:56.8913932Z target ``state_dict_type``. 2024-12-18T01:36:56.8914182Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2024-12-18T01:36:56.8914340Z for the optimizer state dict. 2024-12-18T01:36:56.8914428Z 2024-12-18T01:36:56.8914535Z Returns: 2024-12-18T01:36:56.8914762Z A StateDictSettings that include the previous state_dict type and 2024-12-18T01:36:56.8914893Z configuration for the module. 2024-12-18T01:36:56.8914981Z 2024-12-18T01:36:56.8915233Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8915368Z 2024-12-18T01:36:56.8915474Z warnings.warn(msg) 2024-12-18T01:36:56.8915574Z 2024-12-18T01:36:56.8915860Z --- Parse Warning: 57 / 105 --- 2024-12-18T01:36:56.8917006Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=805. 2024-12-18T01:36:56.8917267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8917547Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2024-12-18T01:36:56.8917653Z 2024-12-18T01:36:56.8917966Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2024-12-18T01:36:56.8918117Z :meth:`set_state_dict_type` for the detail. 2024-12-18T01:36:56.8918204Z 2024-12-18T01:36:56.8918317Z Example:: 2024-12-18T01:36:56.8918404Z 2024-12-18T01:36:56.8918543Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8918666Z >>> model = DDP(FSDP(...)) 2024-12-18T01:36:56.8918784Z >>> with FSDP.state_dict_type( 2024-12-18T01:36:56.8918892Z >>> model, 2024-12-18T01:36:56.8919028Z >>> StateDictType.SHARDED_STATE_DICT, 2024-12-18T01:36:56.8919123Z >>> ): 2024-12-18T01:36:56.8919261Z >>> checkpoint = model.state_dict() 2024-12-18T01:36:56.8919350Z 2024-12-18T01:36:56.8919452Z Args: 2024-12-18T01:36:56.8919584Z module (torch.nn.Module): Root module. 2024-12-18T01:36:56.8919831Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2024-12-18T01:36:56.8920061Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2024-12-18T01:36:56.8920226Z configuration for the target ``state_dict_type``. 2024-12-18T01:36:56.8920474Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2024-12-18T01:36:56.8920671Z ``state_dict`` configuration for the target ``state_dict_type``. 2024-12-18T01:36:56.8920774Z 2024-12-18T01:36:56.8921026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8921132Z 2024-12-18T01:36:56.8921234Z warnings.warn(msg) 2024-12-18T01:36:56.8921321Z 2024-12-18T01:36:56.8921528Z --- Parse Warning: 58 / 105 --- 2024-12-18T01:36:56.8923285Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1818. 2024-12-18T01:36:56.8923561Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8923650Z 2024-12-18T01:36:56.8923902Z Transform the state-dict of an optimizer corresponding to a sharded model. 2024-12-18T01:36:56.8923990Z 2024-12-18T01:36:56.8924184Z The given state-dict can be transformed to one of three types: 2024-12-18T01:36:56.8924492Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2024-12-18T01:36:56.8924607Z 2024-12-18T01:36:56.8924851Z For full optimizer state_dict, all states are unflattened and not sharded. 2024-12-18T01:36:56.8925066Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2024-12-18T01:36:56.8925171Z avoid OOM. 2024-12-18T01:36:56.8925262Z 2024-12-18T01:36:56.8925495Z For sharded optimizer state_dict, all states are unflattened but sharded. 2024-12-18T01:36:56.8925712Z CPU only can be specified via :meth:`state_dict_type` to further save 2024-12-18T01:36:56.8925850Z memory. 2024-12-18T01:36:56.8925948Z 2024-12-18T01:36:56.8926164Z For local state_dict, no transformation will be performed. But a state 2024-12-18T01:36:56.8926401Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2024-12-18T01:36:56.8926533Z nature (this is not supported yet). 2024-12-18T01:36:56.8926620Z 2024-12-18T01:36:56.8926725Z Example:: 2024-12-18T01:36:56.8926812Z 2024-12-18T01:36:56.8926955Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8927197Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:36:56.8927387Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:36:56.8927587Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:36:56.8927791Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:36:56.8927904Z >>> # Save a checkpoint 2024-12-18T01:36:56.8928009Z >>> model, optim = ... 2024-12-18T01:36:56.8928118Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:56.8928222Z >>> model, 2024-12-18T01:36:56.8928347Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:56.8928491Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8928642Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8928740Z >>> ) 2024-12-18T01:36:56.8928856Z >>> state_dict = model.state_dict() 2024-12-18T01:36:56.8929029Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2024-12-18T01:36:56.8929190Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:36:56.8929295Z >>> # Load a checkpoint 2024-12-18T01:36:56.8929408Z >>> model, optim = ... 2024-12-18T01:36:56.8929564Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:36:56.8929674Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:56.8929783Z >>> model, 2024-12-18T01:36:56.8929911Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:56.8930060Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8930210Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8930311Z >>> ) 2024-12-18T01:36:56.8930433Z >>> model.load_state_dict(state_dict) 2024-12-18T01:36:56.8930584Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:36:56.8930717Z >>> model, optim, optim_state_dict 2024-12-18T01:36:56.8930811Z >>> ) 2024-12-18T01:36:56.8930953Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:36:56.8931072Z 2024-12-18T01:36:56.8931157Z Args: 2024-12-18T01:36:56.8931368Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:36:56.8931572Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:36:56.8931722Z were passed into the optimizer ``optim``. 2024-12-18T01:36:56.8931908Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:36:56.8932019Z parameters. 2024-12-18T01:36:56.8932235Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2024-12-18T01:36:56.8932444Z transform. If the value is None, optim.state_dict() will be used. ( 2024-12-18T01:36:56.8932558Z Default: ``None``) 2024-12-18T01:36:56.8932794Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:36:56.8933024Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:36:56.8933124Z Default: ``None``) 2024-12-18T01:36:56.8933224Z 2024-12-18T01:36:56.8933318Z Returns: 2024-12-18T01:36:56.8933511Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2024-12-18T01:36:56.8933692Z ``model``. The sharding of the optimizer state is based on 2024-12-18T01:36:56.8933793Z ``state_dict_type``. 2024-12-18T01:36:56.8933921Z 2024-12-18T01:36:56.8934174Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8934262Z 2024-12-18T01:36:56.8934380Z warnings.warn(msg) 2024-12-18T01:36:56.8934466Z 2024-12-18T01:36:56.8934680Z --- Parse Warning: 59 / 105 --- 2024-12-18T01:36:56.8935875Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1916. 2024-12-18T01:36:56.8936155Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8936244Z 2024-12-18T01:36:56.8936593Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2024-12-18T01:36:56.8936698Z 2024-12-18T01:36:56.8936867Z Given a ``optim_state_dict`` that is transformed through 2024-12-18T01:36:56.8937096Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2024-12-18T01:36:56.8937306Z state_dict that can be loaded to ``optim`` which is the optimizer for 2024-12-18T01:36:56.8937513Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2024-12-18T01:36:56.8937600Z 2024-12-18T01:36:56.8937735Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.8937993Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2024-12-18T01:36:56.8938155Z >>> from torch.distributed.fsdp import StateDictType 2024-12-18T01:36:56.8938351Z >>> from torch.distributed.fsdp import FullStateDictConfig 2024-12-18T01:36:56.8938556Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2024-12-18T01:36:56.8938671Z >>> # Save a checkpoint 2024-12-18T01:36:56.8938771Z >>> model, optim = ... 2024-12-18T01:36:56.8938885Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:56.8938987Z >>> model, 2024-12-18T01:36:56.8939111Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:56.8939260Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8939410Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8939498Z >>> ) 2024-12-18T01:36:56.8939626Z >>> state_dict = model.state_dict() 2024-12-18T01:36:56.8939748Z >>> original_osd = optim.state_dict() 2024-12-18T01:36:56.8939899Z >>> optim_state_dict = FSDP.optim_state_dict( 2024-12-18T01:36:56.8939990Z >>> model, 2024-12-18T01:36:56.8940126Z >>> optim, 2024-12-18T01:36:56.8940243Z >>> optim_state_dict=original_osd 2024-12-18T01:36:56.8940331Z >>> ) 2024-12-18T01:36:56.8940495Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2024-12-18T01:36:56.8940598Z >>> # Load a checkpoint 2024-12-18T01:36:56.8940716Z >>> model, optim = ... 2024-12-18T01:36:56.8940870Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2024-12-18T01:36:56.8940980Z >>> FSDP.set_state_dict_type( 2024-12-18T01:36:56.8941082Z >>> model, 2024-12-18T01:36:56.8941205Z >>> StateDictType.FULL_STATE_DICT, 2024-12-18T01:36:56.8941350Z >>> FullStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8941499Z >>> FullOptimStateDictConfig(rank0_only=False), 2024-12-18T01:36:56.8941628Z >>> ) 2024-12-18T01:36:56.8941744Z >>> model.load_state_dict(state_dict) 2024-12-18T01:36:56.8941898Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2024-12-18T01:36:56.8942031Z >>> model, optim, optim_state_dict 2024-12-18T01:36:56.8942120Z >>> ) 2024-12-18T01:36:56.8942259Z >>> optim.load_state_dict(optim_state_dict) 2024-12-18T01:36:56.8942344Z 2024-12-18T01:36:56.8942433Z Args: 2024-12-18T01:36:56.8942667Z model (torch.nn.Module): Root module (which may or may not be a 2024-12-18T01:36:56.8942871Z :class:`FullyShardedDataParallel` instance) whose parameters 2024-12-18T01:36:56.8943017Z were passed into the optimizer ``optim``. 2024-12-18T01:36:56.8943202Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2024-12-18T01:36:56.8943311Z parameters. 2024-12-18T01:36:56.8943521Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2024-12-18T01:36:56.8943724Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2024-12-18T01:36:56.8943954Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2024-12-18T01:36:56.8944136Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2024-12-18T01:36:56.8944339Z load_directly (bool): If this is set to True, this API will also 2024-12-18T01:36:56.8944543Z call optim.load_state_dict(result) before returning the result. 2024-12-18T01:36:56.8944778Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2024-12-18T01:36:56.8944882Z (Default: ``False``) 2024-12-18T01:36:56.8945121Z group (dist.ProcessGroup): Model's process group across which parameters 2024-12-18T01:36:56.8945319Z are sharded or ``None`` if using the default process group. ( 2024-12-18T01:36:56.8945420Z Default: ``None``) 2024-12-18T01:36:56.8945519Z 2024-12-18T01:36:56.8945773Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8945875Z 2024-12-18T01:36:56.8945977Z warnings.warn(msg) 2024-12-18T01:36:56.8946067Z 2024-12-18T01:36:56.8946274Z --- Parse Warning: 60 / 105 --- 2024-12-18T01:36:56.8947247Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=137. 2024-12-18T01:36:56.8947526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8947614Z 2024-12-18T01:36:56.8947853Z RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:36:56.8947943Z 2024-12-18T01:36:56.8948140Z It creates a user-specified module on a specified remote node. 2024-12-18T01:36:56.8948381Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:36:56.8948495Z executed on the remote node. 2024-12-18T01:36:56.8948745Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:36:56.8948932Z gradients back to the corresponding remote module. 2024-12-18T01:36:56.8949291Z It can be shared across processors using `RPC framework `__, 2024-12-18T01:36:56.8949504Z without incurring any overheads of copying the actual module, 2024-12-18T01:36:56.8949711Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2024-12-18T01:36:56.8949835Z pointing to the remote module. 2024-12-18T01:36:56.8949925Z 2024-12-18T01:36:56.8950140Z The arguments of ``forward_async`` and ``forward`` are the same as 2024-12-18T01:36:56.8950343Z the ``forward`` method of the module returned by the ``module_cls``. 2024-12-18T01:36:56.8950431Z 2024-12-18T01:36:56.8950746Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2024-12-18T01:36:56.8950867Z 2024-12-18T01:36:56.8951134Z Particularly, to create a hybrid model, typically the local modules should be 2024-12-18T01:36:56.8951500Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2024-12-18T01:36:56.8951610Z Hybrid Example: 2024-12-18T01:36:56.8951734Z >>> class HybridModel(nn.Module): 2024-12-18T01:36:56.8951853Z >>> def __init__(self) -> None: 2024-12-18T01:36:56.8952014Z >>> nn.Module.__init__(self) 2024-12-18T01:36:56.8952165Z >>> self.remote_embedding = RemoteModule(...) 2024-12-18T01:36:56.8952312Z >>> self.local_linear = nn.Linear(...) 2024-12-18T01:36:56.8952401Z 2024-12-18T01:36:56.8952617Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:36:56.8952868Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:36:56.8953080Z the generated ``RemoteModule`` will have 2 methods in signature of 2024-12-18T01:36:56.8953258Z ``def forward(input: Tensor) -> Tensor:`` and 2024-12-18T01:36:56.8953431Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2024-12-18T01:36:56.8953532Z 2024-12-18T01:36:56.8953630Z .. note:: 2024-12-18T01:36:56.8953782Z If the remote module is placed on a cuda device, 2024-12-18T01:36:56.8954035Z any input CPU tensors will be automatically moved to the same cuda device, 2024-12-18T01:36:56.8954426Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2024-12-18T01:36:56.8954529Z 2024-12-18T01:36:56.8954621Z Args: 2024-12-18T01:36:56.8954930Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:36:56.8955221Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:36:56.8955317Z formats: 2024-12-18T01:36:56.8955423Z 2024-12-18T01:36:56.8955573Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:36:56.8955865Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:36:56.8955956Z 2024-12-18T01:36:56.8956219Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:36:56.8956340Z module_cls (nn.Module): For example, 2024-12-18T01:36:56.8956459Z >>> class MyModule(nn.Module): 2024-12-18T01:36:56.8956580Z >>> def forward(input): 2024-12-18T01:36:56.8956689Z >>> return input + 1 2024-12-18T01:36:56.8956796Z >>> 2024-12-18T01:36:56.8956900Z >>> module_cls = MyModule 2024-12-18T01:36:56.8957101Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:36:56.8957309Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:36:56.8957593Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:36:56.8957841Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:36:56.8958099Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:36:56.8958344Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:36:56.8958429Z 2024-12-18T01:36:56.8958524Z Returns: 2024-12-18T01:36:56.8958777Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:36:56.8959007Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:36:56.8959285Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:36:56.8959432Z on the user-provided module on the remote side. 2024-12-18T01:36:56.8959576Z 2024-12-18T01:36:56.8959673Z Example:: 2024-12-18T01:36:56.8959828Z Run the following code in two different processes: 2024-12-18T01:36:56.8959932Z 2024-12-18T01:36:56.8960058Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.8960170Z >>> # On worker 0: 2024-12-18T01:36:56.8960268Z >>> import torch 2024-12-18T01:36:56.8960402Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:56.8960532Z >>> from torch import nn, Tensor 2024-12-18T01:36:56.8960781Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:36:56.8960882Z >>> 2024-12-18T01:36:56.8961027Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:36:56.8961169Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:36:56.8961299Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:36:56.8961388Z >>> ) 2024-12-18T01:36:56.8961514Z >>> input = torch.randn(128, 20) 2024-12-18T01:36:56.8961675Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:36:56.8961793Z >>> ret = ret_fut.wait() 2024-12-18T01:36:56.8961923Z >>> rpc.shutdown() 2024-12-18T01:36:56.8962015Z 2024-12-18T01:36:56.8962128Z >>> # On worker 1: 2024-12-18T01:36:56.8962226Z >>> import torch 2024-12-18T01:36:56.8962374Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:56.8962464Z >>> 2024-12-18T01:36:56.8962627Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:36:56.8962728Z >>> rpc.shutdown() 2024-12-18T01:36:56.8962816Z 2024-12-18T01:36:56.8963081Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8963166Z 2024-12-18T01:36:56.8963279Z warnings.warn(msg) 2024-12-18T01:36:56.8963365Z 2024-12-18T01:36:56.8963579Z --- Parse Warning: 61 / 105 --- 2024-12-18T01:36:56.8964631Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=514. 2024-12-18T01:36:56.8964896Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8964996Z 2024-12-18T01:36:56.8965316Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2024-12-18T01:36:56.8965417Z 2024-12-18T01:36:56.8965736Z This alternate initialization method can be particularly useful if we want to create multiple 2024-12-18T01:36:56.8966046Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2024-12-18T01:36:56.8966149Z 2024-12-18T01:36:56.8966426Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2024-12-18T01:36:56.8966619Z which is not supported. The recommended way is as follows: 2024-12-18T01:36:56.8966710Z 2024-12-18T01:36:56.8966847Z 1. the sender creates a RemoteModule; 2024-12-18T01:36:56.8967000Z 2. the sender sends its ``module_rref`` over RPC; 2024-12-18T01:36:56.8967361Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2024-12-18T01:36:56.8967466Z 2024-12-18T01:36:56.8967563Z Example:: 2024-12-18T01:36:56.8967733Z Run the following code in two different processes: 2024-12-18T01:36:56.8967822Z 2024-12-18T01:36:56.8967945Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.8968056Z >>> # On worker 0: 2024-12-18T01:36:56.8968153Z >>> import torch 2024-12-18T01:36:56.8968296Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:56.8968408Z >>> from torch import nn, Tensor 2024-12-18T01:36:56.8968649Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:36:56.8968741Z >>> 2024-12-18T01:36:56.8968918Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:36:56.8969047Z >>> remote_module = RemoteModule( 2024-12-18T01:36:56.8969181Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:36:56.8969287Z >>> ) 2024-12-18T01:36:56.8969375Z >>> 2024-12-18T01:36:56.8969490Z >>> remote_module1 = rpc.rpc_sync( 2024-12-18T01:36:56.8969603Z >>> "worker1/cpu", 2024-12-18T01:36:56.8969733Z >>> RemoteModule.init_from_module_rref, 2024-12-18T01:36:56.8969930Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2024-12-18T01:36:56.8970021Z >>> ) 2024-12-18T01:36:56.8970134Z >>> rpc.shutdown() 2024-12-18T01:36:56.8970221Z 2024-12-18T01:36:56.8970315Z >>> # On worker 1: 2024-12-18T01:36:56.8970426Z >>> import torch 2024-12-18T01:36:56.8970556Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:56.8970656Z >>> 2024-12-18T01:36:56.8970798Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:36:56.8970898Z >>> rpc.shutdown() 2024-12-18T01:36:56.8970999Z 2024-12-18T01:36:56.8971115Z Args: 2024-12-18T01:36:56.8971422Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:36:56.8971707Z The device can be a local device or a remote device specified by one of the following remote 2024-12-18T01:36:56.8971813Z formats: 2024-12-18T01:36:56.8971899Z 2024-12-18T01:36:56.8972045Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2024-12-18T01:36:56.8972214Z 2. "/" (ex: "trainer0/cuda:0"). 2024-12-18T01:36:56.8972301Z 2024-12-18T01:36:56.8972553Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:36:56.8972804Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2024-12-18T01:36:56.8972915Z the created remote module. 2024-12-18T01:36:56.8973204Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2024-12-18T01:36:56.8973438Z to be created. The type object should be decorated by @torch.jit.interface. 2024-12-18T01:36:56.8973669Z If not provided, the generated RemoteModule is not torchscript-able. 2024-12-18T01:36:56.8973902Z Warning, this is an experimental API and susceptible to frequent changes. 2024-12-18T01:36:56.8974002Z 2024-12-18T01:36:56.8974094Z Returns: 2024-12-18T01:36:56.8974335Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:36:56.8974578Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2024-12-18T01:36:56.8974844Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:36:56.8975001Z on the user-provided module on the remote side. 2024-12-18T01:36:56.8975090Z 2024-12-18T01:36:56.8975354Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8975443Z 2024-12-18T01:36:56.8975549Z warnings.warn(msg) 2024-12-18T01:36:56.8975675Z 2024-12-18T01:36:56.8975875Z --- Parse Warning: 62 / 105 --- 2024-12-18T01:36:56.8976833Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=606. 2024-12-18T01:36:56.8977094Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8977198Z 2024-12-18T01:36:56.8977428Z A RemoteModule instance can only be created after RPC initialization. 2024-12-18T01:36:56.8977517Z 2024-12-18T01:36:56.8977727Z It creates a user-specified module on a specified remote node. 2024-12-18T01:36:56.8977964Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2024-12-18T01:36:56.8978119Z executed on the remote node. 2024-12-18T01:36:56.8978359Z It takes care of autograd recording to ensure the backward pass propagates 2024-12-18T01:36:56.8978534Z gradients back to the corresponding remote module. 2024-12-18T01:36:56.8978626Z 2024-12-18T01:36:56.8978845Z It generates two methods ``forward_async`` and ``forward`` based on the 2024-12-18T01:36:56.8979097Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2024-12-18T01:36:56.8979349Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2024-12-18T01:36:56.8979561Z and ``forward`` are the same as the ``forward`` method of the module 2024-12-18T01:36:56.8979675Z returned by the ``module_cls``. 2024-12-18T01:36:56.8979776Z 2024-12-18T01:36:56.8979976Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2024-12-18T01:36:56.8980226Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2024-12-18T01:36:56.8980494Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2024-12-18T01:36:56.8980586Z 2024-12-18T01:36:56.8980735Z | ``def forward(input: Tensor) -> Tensor:`` 2024-12-18T01:36:56.8980903Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2024-12-18T01:36:56.8980992Z 2024-12-18T01:36:56.8981094Z Args: 2024-12-18T01:36:56.8981390Z remote_device (str): Device on the destination worker where we'd like to place this module. 2024-12-18T01:36:56.8981739Z The format should be "/", where the device field can be parsed as torch.device type. 2024-12-18T01:36:56.8981885Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2024-12-18T01:36:56.8982142Z In addition, the device field can be optional and the default value is "cpu". 2024-12-18T01:36:56.8982389Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2024-12-18T01:36:56.8982478Z 2024-12-18T01:36:56.8982609Z >>> class MyModule(nn.Module): 2024-12-18T01:36:56.8982719Z >>> def forward(input): 2024-12-18T01:36:56.8982841Z >>> return input + 1 2024-12-18T01:36:56.8982932Z >>> 2024-12-18T01:36:56.8983057Z >>> module_cls = MyModule 2024-12-18T01:36:56.8983147Z 2024-12-18T01:36:56.8983350Z args (Sequence, optional): args to be passed to ``module_cls``. 2024-12-18T01:36:56.8983558Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2024-12-18T01:36:56.8983647Z 2024-12-18T01:36:56.8983755Z Returns: 2024-12-18T01:36:56.8983998Z A remote module instance which wraps the :class:`~nn.Module` created by the 2024-12-18T01:36:56.8984230Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2024-12-18T01:36:56.8984514Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2024-12-18T01:36:56.8984665Z on the user-provided module on the remote side. 2024-12-18T01:36:56.8984770Z 2024-12-18T01:36:56.8984898Z Example:: 2024-12-18T01:36:56.8985073Z Run the following code in two different processes: 2024-12-18T01:36:56.8985165Z 2024-12-18T01:36:56.8985289Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.8985405Z >>> # On worker 0: 2024-12-18T01:36:56.8985505Z >>> import torch 2024-12-18T01:36:56.8985655Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:56.8985770Z >>> from torch import nn, Tensor 2024-12-18T01:36:56.8985998Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2024-12-18T01:36:56.8986104Z >>> 2024-12-18T01:36:56.8986248Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2024-12-18T01:36:56.8986388Z >>> remote_linear_module = RemoteModule( 2024-12-18T01:36:56.8986551Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2024-12-18T01:36:56.8986657Z >>> ) 2024-12-18T01:36:56.8986774Z >>> input = torch.randn(128, 20) 2024-12-18T01:36:56.8986936Z >>> ret_fut = remote_linear_module.forward_async(input) 2024-12-18T01:36:56.8987054Z >>> ret = ret_fut.wait() 2024-12-18T01:36:56.8987157Z >>> rpc.shutdown() 2024-12-18T01:36:56.8987257Z 2024-12-18T01:36:56.8987352Z >>> # On worker 1: 2024-12-18T01:36:56.8987451Z >>> import torch 2024-12-18T01:36:56.8987623Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:56.8987714Z >>> 2024-12-18T01:36:56.8987871Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2024-12-18T01:36:56.8987974Z >>> rpc.shutdown() 2024-12-18T01:36:56.8988059Z 2024-12-18T01:36:56.8988264Z Furthermore, a more practical example that is combined with 2024-12-18T01:36:56.8988741Z `DistributedDataParallel `__ (DDP) 2024-12-18T01:36:56.8989115Z can be found in this `tutorial `__. 2024-12-18T01:36:56.8989205Z 2024-12-18T01:36:56.8989465Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.8989551Z 2024-12-18T01:36:56.8989657Z warnings.warn(msg) 2024-12-18T01:36:56.8989758Z 2024-12-18T01:36:56.8989958Z --- Parse Warning: 63 / 105 --- 2024-12-18T01:36:56.8990938Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130. 2024-12-18T01:36:56.8991202Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.8991299Z 2024-12-18T01:36:56.8991542Z DistributedOptimizer takes remote references to parameters scattered 2024-12-18T01:36:56.8991782Z across workers and applies the given optimizer locally for each parameter. 2024-12-18T01:36:56.8991883Z 2024-12-18T01:36:56.8992123Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2024-12-18T01:36:56.8992292Z to retrieve the gradients for specific parameters. 2024-12-18T01:36:56.8992382Z 2024-12-18T01:36:56.8992496Z Concurrent calls to 2024-12-18T01:36:56.8992717Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2024-12-18T01:36:56.8992865Z either from the same or different clients, will 2024-12-18T01:36:56.8993102Z be serialized on each worker -- as each worker's optimizer can only work 2024-12-18T01:36:56.8993309Z on one set of gradients at a time. However, there is no guarantee that 2024-12-18T01:36:56.8993570Z the full forward-backward-optimizer sequence will execute for one client 2024-12-18T01:36:56.8993790Z at a time. This means that the gradients being applied may not correspond 2024-12-18T01:36:56.8994032Z to the latest forward pass executed on a given worker. Also, there is no 2024-12-18T01:36:56.8994157Z guaranteed ordering across workers. 2024-12-18T01:36:56.8994294Z 2024-12-18T01:36:56.8994570Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2024-12-18T01:36:56.8994798Z by default, so that optimizer updates are not blocked by the Python Global 2024-12-18T01:36:56.8995058Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2024-12-18T01:36:56.8995302Z Model Parallel). This feature is currently enabled for most optimizers. You 2024-12-18T01:36:56.8995567Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2024-12-18T01:36:56.8995779Z for your own custom optimizers. 2024-12-18T01:36:56.8995870Z 2024-12-18T01:36:56.8995975Z Args: 2024-12-18T01:36:56.8996172Z optimizer_class (optim.Optimizer): the class of optimizer to 2024-12-18T01:36:56.8996336Z instantiate on each worker. 2024-12-18T01:36:56.8996548Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2024-12-18T01:36:56.8996649Z to optimize. 2024-12-18T01:36:56.8996875Z args: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:36:56.8997095Z kwargs: arguments to pass to the optimizer constructor on each worker. 2024-12-18T01:36:56.8997195Z 2024-12-18T01:36:56.8997293Z Example:: 2024-12-18T01:36:56.8997456Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.8997626Z >>> import torch.distributed.autograd as dist_autograd 2024-12-18T01:36:56.8997757Z >>> import torch.distributed.rpc as rpc 2024-12-18T01:36:56.8998056Z >>> from torch import optim 2024-12-18T01:36:56.8998256Z >>> from torch.distributed.optim import DistributedOptimizer 2024-12-18T01:36:56.8998361Z >>> 2024-12-18T01:36:56.8998501Z >>> with dist_autograd.context() as context_id: 2024-12-18T01:36:56.8998609Z >>> # Forward pass. 2024-12-18T01:36:56.8998903Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2024-12-18T01:36:56.8999105Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2024-12-18T01:36:56.8999249Z >>> loss = rref1.to_here() + rref2.to_here() 2024-12-18T01:36:56.8999340Z >>> 2024-12-18T01:36:56.8999460Z >>> # Backward pass. 2024-12-18T01:36:56.8999618Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2024-12-18T01:36:56.8999710Z >>> 2024-12-18T01:36:56.8999825Z >>> # Optimizer. 2024-12-18T01:36:56.8999957Z >>> dist_optim = DistributedOptimizer( 2024-12-18T01:36:56.9000071Z >>> optim.SGD, 2024-12-18T01:36:56.9000174Z >>> [rref1, rref2], 2024-12-18T01:36:56.9000269Z >>> lr=0.05, 2024-12-18T01:36:56.9000375Z >>> ) 2024-12-18T01:36:56.9000490Z >>> dist_optim.step(context_id) 2024-12-18T01:36:56.9000594Z 2024-12-18T01:36:56.9000752Z __ https://github.com/pytorch/tutorials/pull/1465 2024-12-18T01:36:56.9000854Z 2024-12-18T01:36:56.9001110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9001197Z 2024-12-18T01:36:56.9001311Z warnings.warn(msg) 2024-12-18T01:36:56.9001399Z 2024-12-18T01:36:56.9001628Z --- Parse Warning: 64 / 105 --- 2024-12-18T01:36:56.9002896Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2024-12-18T01:36:56.9003176Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9003264Z 2024-12-18T01:36:56.9003651Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2024-12-18T01:36:56.9003823Z This optimizer runs local optimizer at every step. 2024-12-18T01:36:56.9004152Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2024-12-18T01:36:56.9004313Z 2024-12-18T01:36:56.9004403Z Args: 2024-12-18T01:36:56.9004514Z optim: The local optimizer. 2024-12-18T01:36:56.9004747Z averager: A model averager instance to run post-localSGD algorithm. 2024-12-18T01:36:56.9004835Z 2024-12-18T01:36:56.9004953Z Example:: 2024-12-18T01:36:56.9005041Z 2024-12-18T01:36:56.9005190Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.9005290Z >>> import torch 2024-12-18T01:36:56.9005414Z >>> import torch.distributed as dist 2024-12-18T01:36:56.9005701Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2024-12-18T01:36:56.9005807Z >>> import torch.nn as nn 2024-12-18T01:36:56.9006059Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2024-12-18T01:36:56.9006336Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2024-12-18T01:36:56.9006447Z >>> PostLocalSGDState, 2024-12-18T01:36:56.9006563Z >>> post_localSGD_hook, 2024-12-18T01:36:56.9006651Z >>> ) 2024-12-18T01:36:56.9006751Z >>> 2024-12-18T01:36:56.9006914Z >>> model = nn.parallel.DistributedDataParallel( 2024-12-18T01:36:56.9007107Z >>> module, device_ids=[rank], output_device=rank 2024-12-18T01:36:56.9007196Z >>> ) 2024-12-18T01:36:56.9007284Z >>> 2024-12-18T01:36:56.9007448Z >>> # Register a post-localSGD communication hook. 2024-12-18T01:36:56.9007744Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2024-12-18T01:36:56.9007923Z >>> model.register_comm_hook(state, post_localSGD_hook) 2024-12-18T01:36:56.9008013Z >>> 2024-12-18T01:36:56.9008222Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2024-12-18T01:36:56.9008513Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2024-12-18T01:36:56.9008688Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2024-12-18T01:36:56.9008913Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2024-12-18T01:36:56.9009037Z >>> opt = PostLocalSGDOptimizer( 2024-12-18T01:36:56.9009158Z >>> optim=local_optim, 2024-12-18T01:36:56.9009411Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2024-12-18T01:36:56.9009504Z >>> ) 2024-12-18T01:36:56.9009609Z >>> 2024-12-18T01:36:56.9009834Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2024-12-18T01:36:56.9010146Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2024-12-18T01:36:56.9010521Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2024-12-18T01:36:56.9010656Z >>> for step in range(0, 200): 2024-12-18T01:36:56.9010763Z >>> opt.zero_grad() 2024-12-18T01:36:56.9010886Z >>> loss = loss_fn(output, labels) 2024-12-18T01:36:56.9011005Z >>> loss.backward() 2024-12-18T01:36:56.9011103Z >>> opt.step() 2024-12-18T01:36:56.9011204Z 2024-12-18T01:36:56.9011460Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9011559Z 2024-12-18T01:36:56.9011665Z warnings.warn(msg) 2024-12-18T01:36:56.9011751Z 2024-12-18T01:36:56.9011971Z --- Parse Warning: 65 / 105 --- 2024-12-18T01:36:56.9013035Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282. 2024-12-18T01:36:56.9013317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9013438Z 2024-12-18T01:36:56.9013852Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2024-12-18T01:36:56.9013940Z 2024-12-18T01:36:56.9014072Z The sharing is done as described by ZeRO_. 2024-12-18T01:36:56.9014175Z 2024-12-18T01:36:56.9014331Z The local optimizer instance in each rank is only 2024-12-18T01:36:56.9014596Z responsible for updating approximately ``1 / world_size`` parameters and 2024-12-18T01:36:56.9014802Z hence only needs to keep ``1 / world_size`` optimizer states. After 2024-12-18T01:36:56.9015047Z parameters are updated locally, each rank will broadcast its parameters to 2024-12-18T01:36:56.9015252Z all other peers to keep all model replicas in the same state. 2024-12-18T01:36:56.9015456Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2024-12-18T01:36:56.9015762Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2024-12-18T01:36:56.9015870Z memory consumption. 2024-12-18T01:36:56.9015972Z 2024-12-18T01:36:56.9016230Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2024-12-18T01:36:56.9016463Z of parameters at each rank. Each parameter belongs to a single rank and is 2024-12-18T01:36:56.9016742Z not divided among ranks. The partition is arbitrary and might not match the 2024-12-18T01:36:56.9016879Z the parameter registration or usage order. 2024-12-18T01:36:56.9016984Z 2024-12-18T01:36:56.9017080Z Arguments: 2024-12-18T01:36:56.9017292Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2024-12-18T01:36:56.9017484Z or :class:`dict` s giving all parameters, which will be sharded 2024-12-18T01:36:56.9017584Z across ranks. 2024-12-18T01:36:56.9017691Z 2024-12-18T01:36:56.9017789Z Keyword Args: 2024-12-18T01:36:56.9018028Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2024-12-18T01:36:56.9018161Z optimizer. 2024-12-18T01:36:56.9018376Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2024-12-18T01:36:56.9018592Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2024-12-18T01:36:56.9018747Z :meth:`torch.distributed.init_process_group`). 2024-12-18T01:36:56.9018992Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2024-12-18T01:36:56.9019209Z packed into buckets to speed up communication, and ``param.data`` 2024-12-18T01:36:56.9019427Z fields point to bucket views at different offsets; if ``False``, 2024-12-18T01:36:56.9019636Z each individual parameter is communicated separately, and each 2024-12-18T01:36:56.9019806Z ``params.data`` stays intact (default: ``False``). 2024-12-18T01:36:56.9020000Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2024-12-18T01:36:56.9020207Z overlapped with :class:`DistributedDataParallel` 's gradient 2024-12-18T01:36:56.9020440Z synchronization; this requires (1) either a functional optimizer 2024-12-18T01:36:56.9020627Z for the ``optimizer_class`` argument or one with a functional 2024-12-18T01:36:56.9020820Z equivalent and (2) registering a DDP communication hook 2024-12-18T01:36:56.9021023Z constructed from one of the functions in ``ddp_zero_hook.py``; 2024-12-18T01:36:56.9021207Z parameters are packed into buckets matching those in 2024-12-18T01:36:56.9021370Z :class:`DistributedDataParallel`, meaning that the 2024-12-18T01:36:56.9021524Z ``parameters_as_bucket_view`` argument is ignored. 2024-12-18T01:36:56.9021725Z If ``False``, :meth:`step` runs disjointly after the backward pass 2024-12-18T01:36:56.9021825Z (per normal). 2024-12-18T01:36:56.9021943Z (default: ``False``) 2024-12-18T01:36:56.9022158Z **defaults: any trailing arguments, which are forwarded to the local 2024-12-18T01:36:56.9022280Z optimizer. 2024-12-18T01:36:56.9022379Z 2024-12-18T01:36:56.9022477Z Example:: 2024-12-18T01:36:56.9022574Z 2024-12-18T01:36:56.9022677Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9022794Z >>> import torch.nn as nn 2024-12-18T01:36:56.9023003Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2024-12-18T01:36:56.9023207Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2024-12-18T01:36:56.9023452Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2024-12-18T01:36:56.9023574Z >>> ddp = DDP(model, device_ids=[rank]) 2024-12-18T01:36:56.9023713Z >>> opt = ZeroRedundancyOptimizer( 2024-12-18T01:36:56.9023819Z >>> ddp.parameters(), 2024-12-18T01:36:56.9023975Z >>> optimizer_class=torch.optim.Adam, 2024-12-18T01:36:56.9024081Z >>> lr=0.01 2024-12-18T01:36:56.9024174Z >>> ) 2024-12-18T01:36:56.9024306Z >>> ddp(inputs).sum().backward() 2024-12-18T01:36:56.9024401Z >>> opt.step() 2024-12-18T01:36:56.9024502Z 2024-12-18T01:36:56.9024601Z .. warning:: 2024-12-18T01:36:56.9024810Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2024-12-18T01:36:56.9024999Z passed-in parameters are the same dense type. 2024-12-18T01:36:56.9025089Z 2024-12-18T01:36:56.9025198Z .. warning:: 2024-12-18T01:36:56.9025412Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2024-12-18T01:36:56.9025615Z the way that overlapping :class:`DistributedDataParallel` with 2024-12-18T01:36:56.9025865Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2024-12-18T01:36:56.9026083Z two or three training iterations do not perform parameter updates in 2024-12-18T01:36:56.9026291Z the optimizer step, depending on if ``static_graph=False`` or 2024-12-18T01:36:56.9026515Z ``static_graph=True``, respectively. This is because it needs 2024-12-18T01:36:56.9026722Z information about the gradient bucketing strategy used by 2024-12-18T01:36:56.9026942Z :class:`DistributedDataParallel`, which is not finalized until the 2024-12-18T01:36:56.9027142Z second forward pass if ``static_graph=False`` or until the third 2024-12-18T01:36:56.9027365Z forward pass if ``static_graph=True``. To adjust for this, one option 2024-12-18T01:36:56.9027478Z is to prepend dummy inputs. 2024-12-18T01:36:56.9027577Z 2024-12-18T01:36:56.9027832Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2024-12-18T01:36:56.9027933Z 2024-12-18T01:36:56.9028071Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2024-12-18T01:36:56.9028159Z 2024-12-18T01:36:56.9028259Z 2024-12-18T01:36:56.9028512Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9028611Z 2024-12-18T01:36:56.9028717Z warnings.warn(msg) 2024-12-18T01:36:56.9028804Z 2024-12-18T01:36:56.9029016Z --- Parse Warning: 66 / 105 --- 2024-12-18T01:36:56.9029977Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2024-12-18T01:36:56.9030254Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9030345Z 2024-12-18T01:36:56.9030592Z Custom reducer class that can be used to specify a custom operation that 2024-12-18T01:36:56.9030768Z reduces losses of multiple microbatches into one value. 2024-12-18T01:36:56.9030856Z 2024-12-18T01:36:56.9030964Z Example: 2024-12-18T01:36:56.9031070Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9031203Z >>> sum_reducer = _CustomReducer( 2024-12-18T01:36:56.9031308Z >>> torch.tensor(0.0), 2024-12-18T01:36:56.9031425Z >>> lambda a, b: a + b 2024-12-18T01:36:56.9031545Z >>> ) 2024-12-18T01:36:56.9031634Z 2024-12-18T01:36:56.9031901Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9031988Z 2024-12-18T01:36:56.9032103Z warnings.warn(msg) 2024-12-18T01:36:56.9032189Z 2024-12-18T01:36:56.9032378Z --- Parse Warning: 67 / 105 --- 2024-12-18T01:36:56.9033284Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2024-12-18T01:36:56.9033551Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9033652Z 2024-12-18T01:36:56.9033920Z A decorator for a function indicating that the return value of the function 2024-12-18T01:36:56.9034145Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2024-12-18T01:36:56.9034389Z function can run asynchronously on the RPC callee. More specifically, the 2024-12-18T01:36:56.9034626Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2024-12-18T01:36:56.9034871Z function and installs subsequent processing steps as a callback to that 2024-12-18T01:36:56.9035193Z :class:`~torch.futures.Future`. The installed callback will read the value 2024-12-18T01:36:56.9035419Z from the :class:`~torch.futures.Future` when completed and send the 2024-12-18T01:36:56.9035606Z value back as the RPC response. That also means the returned 2024-12-18T01:36:56.9035948Z :class:`~torch.futures.Future` only exists on the callee side and is never 2024-12-18T01:36:56.9036175Z sent through RPC. This decorator is useful when the wrapped function's 2024-12-18T01:36:56.9036377Z (``fn``) execution needs to pause and resume due to, e.g., containing 2024-12-18T01:36:56.9036650Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2024-12-18T01:36:56.9036743Z 2024-12-18T01:36:56.9036971Z .. note:: To enable asynchronous execution, applications must pass the 2024-12-18T01:36:56.9037202Z function object returned by this decorator to RPC APIs. If RPC detected 2024-12-18T01:36:56.9037437Z attributes installed by this decorator, it knows that this function 2024-12-18T01:36:56.9037625Z returns a ``Future`` object and will handle that accordingly. 2024-12-18T01:36:56.9037841Z However, this does not mean this decorator has to be outmost one when 2024-12-18T01:36:56.9038082Z defining a function. For example, when combined with ``@staticmethod`` 2024-12-18T01:36:56.9038295Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2024-12-18T01:36:56.9038532Z inner decorator to allow the target function be recognized as a static 2024-12-18T01:36:56.9038768Z or class function. This target function can still execute asynchronously 2024-12-18T01:36:56.9039012Z because, when accessed, the static or class method preserves attributes 2024-12-18T01:36:56.9039173Z installed by ``@rpc.functions.async_execution``. 2024-12-18T01:36:56.9039274Z 2024-12-18T01:36:56.9039362Z 2024-12-18T01:36:56.9039457Z Example:: 2024-12-18T01:36:56.9039674Z The returned :class:`~torch.futures.Future` object can come from 2024-12-18T01:36:56.9039815Z :meth:`~torch.distributed.rpc.rpc_async`, 2024-12-18T01:36:56.9040056Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2024-12-18T01:36:56.9040234Z constructor. The example below shows directly using the 2024-12-18T01:36:56.9040371Z :class:`~torch.futures.Future` returned by 2024-12-18T01:36:56.9040512Z :meth:`~torch.futures.Future.then`. 2024-12-18T01:36:56.9040602Z 2024-12-18T01:36:56.9040739Z >>> from torch.distributed import rpc 2024-12-18T01:36:56.9040831Z >>> 2024-12-18T01:36:56.9040954Z >>> # omitting setup and shutdown RPC 2024-12-18T01:36:56.9041086Z >>> 2024-12-18T01:36:56.9041187Z >>> # On all workers 2024-12-18T01:36:56.9041321Z >>> @rpc.functions.async_execution 2024-12-18T01:36:56.9041443Z >>> def async_add_chained(to, x, y, z): 2024-12-18T01:36:56.9041654Z >>> # This function runs on "worker1" and returns immediately when 2024-12-18T01:36:56.9041847Z >>> # the callback is installed through the `then(cb)` API. In the 2024-12-18T01:36:56.9042035Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2024-12-18T01:36:56.9042210Z >>> # When the return value of that `rpc_async` arrives at 2024-12-18T01:36:56.9042402Z >>> # "worker1", "worker1" will run the lambda function accordingly 2024-12-18T01:36:56.9042630Z >>> # and set the value for the previously returned `Future`, which 2024-12-18T01:36:56.9042816Z >>> # will then trigger RPC to send the result back to "worker0". 2024-12-18T01:36:56.9043005Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:56.9043125Z >>> lambda fut: fut.wait() + z 2024-12-18T01:36:56.9043218Z >>> ) 2024-12-18T01:36:56.9043324Z >>> 2024-12-18T01:36:56.9043421Z >>> # On worker0 2024-12-18T01:36:56.9043568Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9043674Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:56.9043771Z >>> "worker1", 2024-12-18T01:36:56.9043889Z >>> async_add_chained, 2024-12-18T01:36:56.9044015Z >>> args=("worker2", torch.ones(2), 1, 1) 2024-12-18T01:36:56.9044115Z >>> ) 2024-12-18T01:36:56.9044238Z >>> print(ret) # prints tensor([3., 3.]) 2024-12-18T01:36:56.9044335Z 2024-12-18T01:36:56.9044566Z When combined with TorchScript decorators, this decorator must be the 2024-12-18T01:36:56.9044659Z outmost one. 2024-12-18T01:36:56.9044758Z 2024-12-18T01:36:56.9044895Z >>> from torch import Tensor 2024-12-18T01:36:56.9045030Z >>> from torch.futures import Future 2024-12-18T01:36:56.9045155Z >>> from torch.distributed import rpc 2024-12-18T01:36:56.9045242Z >>> 2024-12-18T01:36:56.9045374Z >>> # omitting setup and shutdown RPC 2024-12-18T01:36:56.9045463Z >>> 2024-12-18T01:36:56.9045576Z >>> # On all workers 2024-12-18T01:36:56.9045683Z >>> @torch.jit.script 2024-12-18T01:36:56.9045844Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2024-12-18T01:36:56.9045942Z >>> return x + y 2024-12-18T01:36:56.9046031Z >>> 2024-12-18T01:36:56.9046166Z >>> @rpc.functions.async_execution 2024-12-18T01:36:56.9046271Z >>> @torch.jit.script 2024-12-18T01:36:56.9046475Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2024-12-18T01:36:56.9046624Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2024-12-18T01:36:56.9046716Z >>> 2024-12-18T01:36:56.9046827Z >>> # On worker0 2024-12-18T01:36:56.9046930Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:56.9047042Z >>> "worker1", 2024-12-18T01:36:56.9047139Z >>> async_add, 2024-12-18T01:36:56.9047263Z >>> args=("worker2", torch.ones(2), 1) 2024-12-18T01:36:56.9047366Z >>> ) 2024-12-18T01:36:56.9047494Z >>> print(ret) # prints tensor([2., 2.]) 2024-12-18T01:36:56.9047600Z 2024-12-18T01:36:56.9047824Z When combined with static or class method, this decorator must be the 2024-12-18T01:36:56.9047932Z inner one. 2024-12-18T01:36:56.9048021Z 2024-12-18T01:36:56.9048149Z >>> from torch.distributed import rpc 2024-12-18T01:36:56.9048258Z >>> 2024-12-18T01:36:56.9048382Z >>> # omitting setup and shutdown RPC 2024-12-18T01:36:56.9048489Z >>> 2024-12-18T01:36:56.9048592Z >>> # On all workers 2024-12-18T01:36:56.9048714Z >>> class AsyncExecutionClass: 2024-12-18T01:36:56.9048844Z >>> 2024-12-18T01:36:56.9048944Z >>> @staticmethod 2024-12-18T01:36:56.9049085Z >>> @rpc.functions.async_execution 2024-12-18T01:36:56.9049207Z >>> def static_async_add(to, x, y, z): 2024-12-18T01:36:56.9049381Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:56.9049516Z >>> lambda fut: fut.wait() + z 2024-12-18T01:36:56.9049606Z >>> ) 2024-12-18T01:36:56.9049712Z >>> 2024-12-18T01:36:56.9049813Z >>> @classmethod 2024-12-18T01:36:56.9049951Z >>> @rpc.functions.async_execution 2024-12-18T01:36:56.9050078Z >>> def class_async_add(cls, to, x, y, z): 2024-12-18T01:36:56.9050210Z >>> ret_fut = torch.futures.Future() 2024-12-18T01:36:56.9050405Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:56.9050563Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2024-12-18T01:36:56.9050673Z >>> ) 2024-12-18T01:36:56.9050782Z >>> return ret_fut 2024-12-18T01:36:56.9050869Z >>> 2024-12-18T01:36:56.9051005Z >>> @rpc.functions.async_execution 2024-12-18T01:36:56.9051133Z >>> def bound_async_add(self, to, x, y, z): 2024-12-18T01:36:56.9051343Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2024-12-18T01:36:56.9051462Z >>> lambda fut: fut.wait() + z 2024-12-18T01:36:56.9051565Z >>> ) 2024-12-18T01:36:56.9051654Z >>> 2024-12-18T01:36:56.9051748Z >>> # On worker0 2024-12-18T01:36:56.9051863Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:56.9051959Z >>> "worker1", 2024-12-18T01:36:56.9052111Z >>> AsyncExecutionClass.static_async_add, 2024-12-18T01:36:56.9052237Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:36:56.9052325Z >>> ) 2024-12-18T01:36:56.9052487Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:56.9052579Z >>> 2024-12-18T01:36:56.9052693Z >>> ret = rpc.rpc_sync( 2024-12-18T01:36:56.9052787Z >>> "worker1", 2024-12-18T01:36:56.9052940Z >>> AsyncExecutionClass.class_async_add, 2024-12-18T01:36:56.9053064Z >>> args=("worker2", torch.ones(2), 1, 2) 2024-12-18T01:36:56.9053154Z >>> ) 2024-12-18T01:36:56.9053291Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:56.9053379Z 2024-12-18T01:36:56.9053560Z This decorator also works with RRef helpers, i.e., . 2024-12-18T01:36:56.9053709Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2024-12-18T01:36:56.9053876Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2024-12-18T01:36:56.9054039Z :meth:`torch.distributed.rpc.RRef.remote`. 2024-12-18T01:36:56.9054128Z 2024-12-18T01:36:56.9054266Z >>> from torch.distributed import rpc 2024-12-18T01:36:56.9054357Z >>> 2024-12-18T01:36:56.9054513Z >>> # reuse the AsyncExecutionClass class above 2024-12-18T01:36:56.9054674Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:36:56.9054889Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2024-12-18T01:36:56.9055025Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:56.9055116Z >>> 2024-12-18T01:36:56.9055283Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:36:56.9055516Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2024-12-18T01:36:56.9055639Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:56.9055744Z >>> 2024-12-18T01:36:56.9055898Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2024-12-18T01:36:56.9056142Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2024-12-18T01:36:56.9056265Z >>> print(ret) # prints tensor([4., 4.]) 2024-12-18T01:36:56.9056366Z 2024-12-18T01:36:56.9056647Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9056733Z 2024-12-18T01:36:56.9056849Z warnings.warn(msg) 2024-12-18T01:36:56.9056936Z 2024-12-18T01:36:56.9057168Z --- Parse Warning: 68 / 105 --- 2024-12-18T01:36:56.9058208Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108. 2024-12-18T01:36:56.9058491Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9058584Z 2024-12-18T01:36:56.9058790Z Set device mapping between each RPC caller and callee pair. This 2024-12-18T01:36:56.9059020Z function can be called multiple times to incrementally add 2024-12-18T01:36:56.9059142Z device placement configurations. 2024-12-18T01:36:56.9059245Z 2024-12-18T01:36:56.9059335Z Args: 2024-12-18T01:36:56.9059442Z to (str): Callee name. 2024-12-18T01:36:56.9059653Z device_map (Dict of int, str, or torch.device): Device placement 2024-12-18T01:36:56.9059837Z mappings from this worker to the callee. This map must be 2024-12-18T01:36:56.9059989Z invertible. 2024-12-18T01:36:56.9060079Z 2024-12-18T01:36:56.9060184Z Example: 2024-12-18T01:36:56.9060305Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.9060401Z >>> # both workers 2024-12-18T01:36:56.9060511Z >>> def add(x, y): 2024-12-18T01:36:56.9060656Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2024-12-18T01:36:56.9060783Z >>> return x + y, (x + y).to(2) 2024-12-18T01:36:56.9060873Z >>> 2024-12-18T01:36:56.9060971Z >>> # on worker 0 2024-12-18T01:36:56.9061131Z >>> options = TensorPipeRpcBackendOptions( 2024-12-18T01:36:56.9061276Z >>> num_worker_threads=8, 2024-12-18T01:36:56.9061414Z >>> device_maps={"worker1": {0: 1}} 2024-12-18T01:36:56.9061553Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2024-12-18T01:36:56.9061655Z >>> ) 2024-12-18T01:36:56.9061790Z >>> options.set_device_map("worker1", {1: 2}) 2024-12-18T01:36:56.9061924Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2024-12-18T01:36:56.9062024Z >>> 2024-12-18T01:36:56.9062125Z >>> rpc.init_rpc( 2024-12-18T01:36:56.9062234Z >>> "worker0", 2024-12-18T01:36:56.9062327Z >>> rank=0, 2024-12-18T01:36:56.9062425Z >>> world_size=2, 2024-12-18T01:36:56.9062574Z >>> backend=rpc.BackendType.TENSORPIPE, 2024-12-18T01:36:56.9062691Z >>> rpc_backend_options=options 2024-12-18T01:36:56.9062798Z >>> ) 2024-12-18T01:36:56.9062888Z >>> 2024-12-18T01:36:56.9062987Z >>> x = torch.ones(2) 2024-12-18T01:36:56.9063168Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2024-12-18T01:36:56.9063357Z >>> # The first argument will be moved to cuda:1 on worker1. When 2024-12-18T01:36:56.9063556Z >>> # sending the return value back, it will follow the invert of 2024-12-18T01:36:56.9063732Z >>> # the device map, and hence will be moved back to cuda:0 and 2024-12-18T01:36:56.9063848Z >>> # cuda:1 on worker0 2024-12-18T01:36:56.9064001Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2024-12-18T01:36:56.9064150Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2024-12-18T01:36:56.9064249Z 2024-12-18T01:36:56.9064500Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9064602Z 2024-12-18T01:36:56.9064705Z warnings.warn(msg) 2024-12-18T01:36:56.9064805Z 2024-12-18T01:36:56.9064999Z --- Parse Warning: 69 / 105 --- 2024-12-18T01:36:56.9065965Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=32. 2024-12-18T01:36:56.9066270Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9066359Z 2024-12-18T01:36:56.9066634Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2024-12-18T01:36:56.9066909Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2024-12-18T01:36:56.9067182Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2024-12-18T01:36:56.9067346Z :class:`DTensor` according to the ``out_placements``. 2024-12-18T01:36:56.9067435Z 2024-12-18T01:36:56.9067537Z Args: 2024-12-18T01:36:56.9067771Z func (Callable): the function to be applied on each local shard of 2024-12-18T01:36:56.9067889Z :class:`DTensor` s. 2024-12-18T01:36:56.9068117Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2024-12-18T01:36:56.9068384Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2024-12-18T01:36:56.9068625Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2024-12-18T01:36:56.9068892Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2024-12-18T01:36:56.9069151Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2024-12-18T01:36:56.9069279Z mapping to the flattened ``output``. 2024-12-18T01:36:56.9069499Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2024-12-18T01:36:56.9069771Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2024-12-18T01:36:56.9069891Z should be `None`. 2024-12-18T01:36:56.9070159Z Note that the only exception is when no :class:`DTensor` argument is passed 2024-12-18T01:36:56.9070387Z in. In this case, even if `out_placements` is not `None`, the result function 2024-12-18T01:36:56.9070655Z should ignore the desired placements because the function is not running with 2024-12-18T01:36:56.9070761Z :class:`DTensor` s. 2024-12-18T01:36:56.9070950Z in_placements (Tuple[`PlacementType`, ...], optional): 2024-12-18T01:36:56.9071229Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2024-12-18T01:36:56.9071472Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2024-12-18T01:36:56.9071699Z placements of each :class:`DTensor` argument is the same as the required 2024-12-18T01:36:56.9071882Z placements or not. If the placements are not the same and 2024-12-18T01:36:56.9072141Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2024-12-18T01:36:56.9072385Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2024-12-18T01:36:56.9072654Z the required sharding placements before passing its local tensor to ``func``. 2024-12-18T01:36:56.9072879Z The only exception is when required placements are not ``None`` and the 2024-12-18T01:36:56.9073138Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2024-12-18T01:36:56.9073355Z will be skipped and the argument will be directly passed to ``func``. 2024-12-18T01:36:56.9073590Z If ``in_placements`` is ``None``, no placements examination will be performed. 2024-12-18T01:36:56.9073691Z Default: None 2024-12-18T01:36:56.9073833Z device_mesh (:class:`DeviceMesh`, optional): 2024-12-18T01:36:56.9074065Z the device mesh that all the :class:`DTensor` s are placed on. If not 2024-12-18T01:36:56.9074301Z specified, this will be inferred from the input :class:`DTensor` s' device 2024-12-18T01:36:56.9074584Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2024-12-18T01:36:56.9074695Z device mesh. Default: None. 2024-12-18T01:36:56.9074838Z redistribute_inputs (bool, optional): 2024-12-18T01:36:56.9075094Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2024-12-18T01:36:56.9075340Z their placements are different from the required input placements. If this 2024-12-18T01:36:56.9075583Z value is ``False`` and some :class:`DTensor` input has a different placement, 2024-12-18T01:36:56.9075803Z an exception will be raised. Default: False. 2024-12-18T01:36:56.9075910Z 2024-12-18T01:36:56.9076003Z Returns: 2024-12-18T01:36:56.9076308Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2024-12-18T01:36:56.9076553Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2024-12-18T01:36:56.9076643Z 2024-12-18T01:36:56.9076752Z Raises: 2024-12-18T01:36:56.9077002Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2024-12-18T01:36:56.9077257Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2024-12-18T01:36:56.9077389Z argument passed in. 2024-12-18T01:36:56.9077479Z 2024-12-18T01:36:56.9077732Z AssertionError: For any non-DTensor output, we require its corresponding 2024-12-18T01:36:56.9077992Z output placement in ``out_placements`` be None. An AssertionError will be raised 2024-12-18T01:36:56.9078113Z if this is not the case. 2024-12-18T01:36:56.9078203Z 2024-12-18T01:36:56.9078478Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2024-12-18T01:36:56.9078642Z a redistribution according to ``in_placements``. 2024-12-18T01:36:56.9078731Z 2024-12-18T01:36:56.9078867Z Example: 2024-12-18T01:36:56.9078989Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.9079145Z >>> def mm_allreduce_forward(device_mesh, W, X): 2024-12-18T01:36:56.9079277Z >>> partial_sum_tensor = torch.mm(W, X) 2024-12-18T01:36:56.9079534Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2024-12-18T01:36:56.9079644Z >>> return reduced_tensor 2024-12-18T01:36:56.9079735Z >>> 2024-12-18T01:36:56.9079883Z >>> W = torch.randn(12, 8, requires_grad=False) 2024-12-18T01:36:56.9080019Z >>> X = torch.randn(8, 16, requires_grad=False) 2024-12-18T01:36:56.9080135Z >>> Y = torch.mm(W, X) 2024-12-18T01:36:56.9080325Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2024-12-18T01:36:56.9080514Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2024-12-18T01:36:56.9080620Z >>> 2024-12-18T01:36:56.9080892Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2024-12-18T01:36:56.9081039Z >>> local_mm_allreduce_forward = local_map( 2024-12-18T01:36:56.9081149Z >>> mm_allreduce_forward, 2024-12-18T01:36:56.9081283Z >>> out_placements=[Replicate()], 2024-12-18T01:36:56.9081408Z >>> in_placements=[col_wise, row_wise], 2024-12-18T01:36:56.9081519Z >>> device_mesh=device_mesh, 2024-12-18T01:36:56.9081621Z >>> ) 2024-12-18T01:36:56.9081709Z >>> 2024-12-18T01:36:56.9081975Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2024-12-18T01:36:56.9082225Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2024-12-18T01:36:56.9082561Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2024-12-18T01:36:56.9082651Z 2024-12-18T01:36:56.9082861Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:36:56.9082990Z 2024-12-18T01:36:56.9083243Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9083341Z 2024-12-18T01:36:56.9083444Z warnings.warn(msg) 2024-12-18T01:36:56.9083531Z 2024-12-18T01:36:56.9083761Z --- Parse Warning: 70 / 105 --- 2024-12-18T01:36:56.9084807Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2024-12-18T01:36:56.9085080Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9085168Z 2024-12-18T01:36:56.9085485Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2024-12-18T01:36:56.9085725Z strategies for an operator when the tensor inputs and outputs are DTensor. 2024-12-18T01:36:56.9085991Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2024-12-18T01:36:56.9086233Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2024-12-18T01:36:56.9086532Z when users would like to overwrite default sharding strategies of existing operators. 2024-12-18T01:36:56.9086632Z 2024-12-18T01:36:56.9086722Z Args: 2024-12-18T01:36:56.9086872Z op (Union[OpOverload, List[OpOverload]]): 2024-12-18T01:36:56.9087070Z An op or a list of ops to register the customized sharding function. 2024-12-18T01:36:56.9087159Z 2024-12-18T01:36:56.9087266Z Returns: 2024-12-18T01:36:56.9087531Z A function decorator which can be used to wrap a function that defines the sharding 2024-12-18T01:36:56.9087818Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2024-12-18T01:36:56.9088118Z registered to DTensor and will override the default sharding strategy if DTensor has 2024-12-18T01:36:56.9088431Z already implemented the operator. The customized sharding function takes the same inputs 2024-12-18T01:36:56.9088672Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2024-12-18T01:36:56.9088955Z replaced by a tensor-like object that DTensor uses internally). The function should 2024-12-18T01:36:56.9089226Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2024-12-18T01:36:56.9089349Z corresponding intput placements. 2024-12-18T01:36:56.9089453Z 2024-12-18T01:36:56.9089544Z Example: 2024-12-18T01:36:56.9089677Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.9089816Z >>> @register_sharding(aten._softmax.default) 2024-12-18T01:36:56.9089978Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2024-12-18T01:36:56.9090138Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2024-12-18T01:36:56.9090257Z >>> acceptable_shardings = [] 2024-12-18T01:36:56.9090357Z >>> 2024-12-18T01:36:56.9090538Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2024-12-18T01:36:56.9090698Z >>> acceptable_shardings.append(all_replicate) 2024-12-18T01:36:56.9090791Z >>> 2024-12-18T01:36:56.9090915Z >>> for sharding_dim in range(x.ndim): 2024-12-18T01:36:56.9091050Z >>> if sharding_dim != softmax_dim: 2024-12-18T01:36:56.9091156Z >>> all_sharded = ( 2024-12-18T01:36:56.9091286Z >>> [Shard(sharding_dim)], 2024-12-18T01:36:56.9091418Z >>> [Shard(sharding_dim), None, None], 2024-12-18T01:36:56.9091515Z >>> ) 2024-12-18T01:36:56.9091676Z >>> acceptable_shardings.append(all_sharded) 2024-12-18T01:36:56.9091766Z >>> 2024-12-18T01:36:56.9091897Z >>> return acceptable_shardings 2024-12-18T01:36:56.9092009Z 2024-12-18T01:36:56.9092218Z .. note:: This API is currently experimental and subject to change 2024-12-18T01:36:56.9092307Z 2024-12-18T01:36:56.9092558Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9092657Z 2024-12-18T01:36:56.9092761Z warnings.warn(msg) 2024-12-18T01:36:56.9092861Z 2024-12-18T01:36:56.9093055Z --- Parse Warning: 71 / 105 --- 2024-12-18T01:36:56.9094041Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=378. 2024-12-18T01:36:56.9094319Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9094448Z 2024-12-18T01:36:56.9094840Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:36:56.9095160Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2024-12-18T01:36:56.9095262Z 2024-12-18T01:36:56.9095357Z Keyword Args: 2024-12-18T01:36:56.9095570Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:36:56.9095923Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2024-12-18T01:36:56.9096277Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2024-12-18T01:36:56.9096416Z as a placeholder. default: None. 2024-12-18T01:36:56.9096647Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2024-12-18T01:36:56.9097036Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:36:56.9097450Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2024-12-18T01:36:56.9097604Z input_kwarg_layouts (Dict[str, Placement]): 2024-12-18T01:36:56.9098211Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2024-12-18T01:36:56.9098316Z default: None 2024-12-18T01:36:56.9098500Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2024-12-18T01:36:56.9098866Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2024-12-18T01:36:56.9099031Z have the desired DTensor layouts. default: None. 2024-12-18T01:36:56.9099152Z use_local_output (bool, optional): 2024-12-18T01:36:56.9099518Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2024-12-18T01:36:56.9099616Z Returns: 2024-12-18T01:36:56.9099946Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2024-12-18T01:36:56.9100036Z 2024-12-18T01:36:56.9100132Z Example:: 2024-12-18T01:36:56.9100261Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:36:56.9100578Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2024-12-18T01:36:56.9100787Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:36:56.9100879Z >>> ... 2024-12-18T01:36:56.9101181Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:36:56.9101327Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:36:56.9101416Z >>> 2024-12-18T01:36:56.9101757Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2024-12-18T01:36:56.9101914Z >>> # and then redistributed to Replicated DTensor. 2024-12-18T01:36:56.9102108Z >>> parallelize_module( 2024-12-18T01:36:56.9102247Z >>> block, # this can be a submodule or module 2024-12-18T01:36:56.9102343Z >>> tp_mesh, 2024-12-18T01:36:56.9102466Z >>> parallelize_plan={ 2024-12-18T01:36:56.9102827Z >>> "attn": PrepareModuleInput( 2024-12-18T01:36:56.9102989Z >>> input_layouts=(Shard(0), None, None, ...), 2024-12-18T01:36:56.9103161Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2024-12-18T01:36:56.9103267Z >>> ), 2024-12-18T01:36:56.9103357Z >>> } 2024-12-18T01:36:56.9103445Z >>> ) 2024-12-18T01:36:56.9103546Z 2024-12-18T01:36:56.9103796Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9103943Z 2024-12-18T01:36:56.9104048Z warnings.warn(msg) 2024-12-18T01:36:56.9104134Z 2024-12-18T01:36:56.9104367Z --- Parse Warning: 72 / 105 --- 2024-12-18T01:36:56.9105363Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=533. 2024-12-18T01:36:56.9105678Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9105768Z 2024-12-18T01:36:56.9106167Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2024-12-18T01:36:56.9106494Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2024-12-18T01:36:56.9106584Z 2024-12-18T01:36:56.9106692Z Keyword Args: 2024-12-18T01:36:56.9106861Z output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:36:56.9107254Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2024-12-18T01:36:56.9107630Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2024-12-18T01:36:56.9107790Z ``None`` need to be specified as a placeholder. 2024-12-18T01:36:56.9107987Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2024-12-18T01:36:56.9108368Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2024-12-18T01:36:56.9108501Z have the desired DTensor layouts. 2024-12-18T01:36:56.9108620Z use_local_output (bool, optional): 2024-12-18T01:36:56.9108988Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2024-12-18T01:36:56.9109081Z Returns: 2024-12-18T01:36:56.9109388Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2024-12-18T01:36:56.9109474Z 2024-12-18T01:36:56.9109576Z Example:: 2024-12-18T01:36:56.9109702Z >>> # xdoctest: +SKIP(failing) 2024-12-18T01:36:56.9110017Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2024-12-18T01:36:56.9110226Z >>> from torch.distributed.device_mesh import init_device_mesh 2024-12-18T01:36:56.9110317Z >>> ... 2024-12-18T01:36:56.9110637Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2024-12-18T01:36:56.9110771Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2024-12-18T01:36:56.9110861Z >>> 2024-12-18T01:36:56.9111269Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2024-12-18T01:36:56.9111410Z >>> # and then redistributed to Sharded DTensor. 2024-12-18T01:36:56.9111537Z >>> parallelize_module( 2024-12-18T01:36:56.9111678Z >>> block, # this can be a submodule or module 2024-12-18T01:36:56.9111817Z >>> tp_mesh, 2024-12-18T01:36:56.9111962Z >>> parallelize_plan = PrepareModuleOutput( 2024-12-18T01:36:56.9112086Z >>> output_layouts=Replicate(), 2024-12-18T01:36:56.9112226Z >>> desired_output_layouts=Shard(0) 2024-12-18T01:36:56.9112317Z >>> ) 2024-12-18T01:36:56.9112425Z >>> ) 2024-12-18T01:36:56.9112514Z 2024-12-18T01:36:56.9112770Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9112874Z 2024-12-18T01:36:56.9112980Z warnings.warn(msg) 2024-12-18T01:36:56.9113087Z 2024-12-18T01:36:56.9113283Z --- Parse Warning: 73 / 105 --- 2024-12-18T01:36:56.9114266Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13. 2024-12-18T01:36:56.9114562Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9114653Z 2024-12-18T01:36:56.9114898Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2024-12-18T01:36:56.9115145Z distribution where all component are from different parameterizations of 2024-12-18T01:36:56.9115398Z the same distribution type. It is parameterized by a `Categorical` 2024-12-18T01:36:56.9115599Z "selecting distribution" (over `k` component) and a component 2024-12-18T01:36:56.9115902Z distribution, i.e., a `Distribution` with a rightmost batch shape 2024-12-18T01:36:56.9116066Z (equal to `[k]`) which indexes each (batch of) component. 2024-12-18T01:36:56.9116155Z 2024-12-18T01:36:56.9116268Z Examples:: 2024-12-18T01:36:56.9116355Z 2024-12-18T01:36:56.9116496Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.9116733Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2024-12-18T01:36:56.9116866Z >>> # weighted normal distributions 2024-12-18T01:36:56.9117003Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:36:56.9117158Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2024-12-18T01:36:56.9117302Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:36:56.9117394Z 2024-12-18T01:36:56.9117612Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2024-12-18T01:36:56.9117747Z >>> # weighted bivariate normal distributions 2024-12-18T01:36:56.9117869Z >>> mix = D.Categorical(torch.ones(5,)) 2024-12-18T01:36:56.9117999Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:36:56.9118130Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2024-12-18T01:36:56.9118269Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:36:56.9118359Z 2024-12-18T01:36:56.9118553Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2024-12-18T01:36:56.9118761Z >>> # consisting of 5 random weighted bivariate normal distributions 2024-12-18T01:36:56.9118886Z >>> mix = D.Categorical(torch.rand(3,5)) 2024-12-18T01:36:56.9119017Z >>> comp = D.Independent(D.Normal( 2024-12-18T01:36:56.9119158Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2024-12-18T01:36:56.9119299Z >>> gmm = MixtureSameFamily(mix, comp) 2024-12-18T01:36:56.9119388Z 2024-12-18T01:36:56.9119477Z Args: 2024-12-18T01:36:56.9119697Z mixture_distribution: `torch.distributions.Categorical`-like 2024-12-18T01:36:56.9119887Z instance. Manages the probability of selecting component. 2024-12-18T01:36:56.9120074Z The number of categories must match the rightmost batch 2024-12-18T01:36:56.9120264Z dimension of the `component_distribution`. Must have either 2024-12-18T01:36:56.9120425Z scalar `batch_shape` or `batch_shape` matching 2024-12-18T01:36:56.9120569Z `component_distribution.batch_shape[:-1]` 2024-12-18T01:36:56.9120820Z component_distribution: `torch.distributions.Distribution`-like 2024-12-18T01:36:56.9121013Z instance. Right-most batch dimension indexes component. 2024-12-18T01:36:56.9121100Z 2024-12-18T01:36:56.9121363Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9121457Z 2024-12-18T01:36:56.9121563Z warnings.warn(msg) 2024-12-18T01:36:56.9121668Z 2024-12-18T01:36:56.9121867Z --- Parse Warning: 74 / 105 --- 2024-12-18T01:36:56.9122841Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=111. 2024-12-18T01:36:56.9123102Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9123232Z 2024-12-18T01:36:56.9123421Z Creates a RelaxedBernoulli distribution, parametrized by 2024-12-18T01:36:56.9123630Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2024-12-18T01:36:56.9123847Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2024-12-18T01:36:56.9124034Z so the values are in (0, 1), and has reparametrizable samples. 2024-12-18T01:36:56.9124139Z 2024-12-18T01:36:56.9124265Z Example:: 2024-12-18T01:36:56.9124367Z 2024-12-18T01:36:56.9124513Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:56.9124651Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2024-12-18T01:36:56.9124795Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2024-12-18T01:36:56.9124891Z >>> m.sample() 2024-12-18T01:36:56.9125028Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2024-12-18T01:36:56.9137338Z 2024-12-18T01:36:56.9137502Z Args: 2024-12-18T01:36:56.9137667Z temperature (Tensor): relaxation temperature 2024-12-18T01:36:56.9137944Z probs (Number, Tensor): the probability of sampling `1` 2024-12-18T01:36:56.9138115Z logits (Number, Tensor): the log-odds of sampling `1` 2024-12-18T01:36:56.9138204Z 2024-12-18T01:36:56.9138454Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9138537Z 2024-12-18T01:36:56.9138639Z warnings.warn(msg) 2024-12-18T01:36:56.9138720Z 2024-12-18T01:36:56.9138969Z --- Parse Warning: 75 / 105 --- 2024-12-18T01:36:56.9139986Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=99. 2024-12-18T01:36:56.9140246Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9140334Z 2024-12-18T01:36:56.9140551Z Creates a RelaxedOneHotCategorical distribution parametrized by 2024-12-18T01:36:56.9140744Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2024-12-18T01:36:56.9140974Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2024-12-18T01:36:56.9141139Z its samples are on simplex, and are reparametrizable. 2024-12-18T01:36:56.9141219Z 2024-12-18T01:36:56.9141323Z Example:: 2024-12-18T01:36:56.9141403Z 2024-12-18T01:36:56.9141546Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:56.9141702Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2024-12-18T01:36:56.9141826Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2024-12-18T01:36:56.9141915Z >>> m.sample() 2024-12-18T01:36:56.9142029Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2024-12-18T01:36:56.9142114Z 2024-12-18T01:36:56.9142196Z Args: 2024-12-18T01:36:56.9142331Z temperature (Tensor): relaxation temperature 2024-12-18T01:36:56.9142451Z probs (Tensor): event probabilities 2024-12-18T01:36:56.9142672Z logits (Tensor): unnormalized log probability for each event 2024-12-18T01:36:56.9142755Z 2024-12-18T01:36:56.9143000Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9143101Z 2024-12-18T01:36:56.9143204Z warnings.warn(msg) 2024-12-18T01:36:56.9143295Z 2024-12-18T01:36:56.9143504Z --- Parse Warning: 76 / 105 --- 2024-12-18T01:36:56.9144492Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2024-12-18T01:36:56.9144770Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9145000Z Return a new dict with new, potentially nested, key value pair 2024-12-18T01:36:56.9145102Z 2024-12-18T01:36:56.9145206Z >>> purchase = { 2024-12-18T01:36:56.9145311Z ... "name": "Alice", 2024-12-18T01:36:56.9145506Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:36:56.9145632Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:36:56.9145737Z ... } 2024-12-18T01:36:56.9145972Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2024-12-18T01:36:56.9146088Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:36:56.9146199Z 'name': 'Alice', 2024-12-18T01:36:56.9146370Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2024-12-18T01:36:56.9146476Z 2024-12-18T01:36:56.9146726Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9146829Z 2024-12-18T01:36:56.9146934Z warnings.warn(msg) 2024-12-18T01:36:56.9147023Z 2024-12-18T01:36:56.9147226Z --- Parse Warning: 77 / 105 --- 2024-12-18T01:36:56.9148242Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2024-12-18T01:36:56.9148525Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9148680Z Update value in a (potentially) nested dictionary 2024-12-18T01:36:56.9148782Z 2024-12-18T01:36:56.9148877Z inputs: 2024-12-18T01:36:56.9149002Z d - dictionary on which to operate 2024-12-18T01:36:56.9149238Z keys - list or tuple giving the location of the value to be changed in d 2024-12-18T01:36:56.9149369Z func - function to operate on that value 2024-12-18T01:36:56.9149471Z 2024-12-18T01:36:56.9149670Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2024-12-18T01:36:56.9149906Z original dictionary with v replaced by func(v), but does not mutate the 2024-12-18T01:36:56.9150029Z original dictionary. 2024-12-18T01:36:56.9150119Z 2024-12-18T01:36:56.9150344Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2024-12-18T01:36:56.9150568Z specified by the keys, with the innermost value set to func(default). 2024-12-18T01:36:56.9150673Z 2024-12-18T01:36:56.9150779Z >>> inc = lambda x: x + 1 2024-12-18T01:36:56.9150891Z >>> update_in({"a": 0}, ["a"], inc) 2024-12-18T01:36:56.9150994Z {'a': 1} 2024-12-18T01:36:56.9151082Z 2024-12-18T01:36:56.9151197Z >>> transaction = { 2024-12-18T01:36:56.9151296Z ... "name": "Alice", 2024-12-18T01:36:56.9151488Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:36:56.9151625Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:36:56.9151715Z ... } 2024-12-18T01:36:56.9151949Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2024-12-18T01:36:56.9152097Z {'credit card': '5555-1234-1234-1234', 2024-12-18T01:36:56.9152207Z 'name': 'Alice', 2024-12-18T01:36:56.9152377Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2024-12-18T01:36:56.9152466Z 2024-12-18T01:36:56.9152605Z >>> # updating a value when k0 is not in d 2024-12-18T01:36:56.9152742Z >>> update_in({}, [1, 2, 3], str, default="bar") 2024-12-18T01:36:56.9152850Z {1: {2: {3: 'bar'}}} 2024-12-18T01:36:56.9152971Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2024-12-18T01:36:56.9153072Z {1: 'foo', 2: {3: {4: 1}}} 2024-12-18T01:36:56.9153175Z 2024-12-18T01:36:56.9153426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9153557Z 2024-12-18T01:36:56.9153662Z warnings.warn(msg) 2024-12-18T01:36:56.9153767Z 2024-12-18T01:36:56.9153958Z --- Parse Warning: 78 / 105 --- 2024-12-18T01:36:56.9154933Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2024-12-18T01:36:56.9155235Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9155407Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2024-12-18T01:36:56.9155511Z 2024-12-18T01:36:56.9155799Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2024-12-18T01:36:56.9156019Z ``no_default`` is specified, then it raises KeyError or IndexError. 2024-12-18T01:36:56.9156109Z 2024-12-18T01:36:56.9156320Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2024-12-18T01:36:56.9156474Z structures such as dictionaries and lists. 2024-12-18T01:36:56.9156562Z 2024-12-18T01:36:56.9156710Z >>> transaction = { 2024-12-18T01:36:56.9156816Z ... "name": "Alice", 2024-12-18T01:36:56.9157010Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2024-12-18T01:36:56.9157148Z ... "credit card": "5555-1234-1234-1234", 2024-12-18T01:36:56.9157240Z ... } 2024-12-18T01:36:56.9157399Z >>> get_in(["purchase", "items", 0], transaction) 2024-12-18T01:36:56.9157493Z 'Apple' 2024-12-18T01:36:56.9157620Z >>> get_in(["name"], transaction) 2024-12-18T01:36:56.9157710Z 'Alice' 2024-12-18T01:36:56.9157847Z >>> get_in(["purchase", "total"], transaction) 2024-12-18T01:36:56.9158015Z >>> get_in(["purchase", "items", "apple"], transaction) 2024-12-18T01:36:56.9158156Z >>> get_in(["purchase", "items", 10], transaction) 2024-12-18T01:36:56.9158316Z >>> get_in(["purchase", "total"], transaction, 0) 2024-12-18T01:36:56.9158408Z 0 2024-12-18T01:36:56.9158543Z >>> get_in(["y"], {}, no_default=True) 2024-12-18T01:36:56.9158665Z Traceback (most recent call last): 2024-12-18T01:36:56.9158757Z ... 2024-12-18T01:36:56.9158870Z KeyError: 'y' 2024-12-18T01:36:56.9158959Z 2024-12-18T01:36:56.9159067Z See Also: 2024-12-18T01:36:56.9159169Z itertoolz.get 2024-12-18T01:36:56.9159279Z operator.getitem 2024-12-18T01:36:56.9159384Z 2024-12-18T01:36:56.9159638Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9159742Z 2024-12-18T01:36:56.9159846Z warnings.warn(msg) 2024-12-18T01:36:56.9159934Z 2024-12-18T01:36:56.9160149Z --- Parse Warning: 79 / 105 --- 2024-12-18T01:36:56.9161137Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2024-12-18T01:36:56.9161417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9161567Z Group a collection by a key function 2024-12-18T01:36:56.9161671Z 2024-12-18T01:36:56.9161841Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2024-12-18T01:36:56.9161972Z >>> groupby(len, names) # doctest: +SKIP 2024-12-18T01:36:56.9162156Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2024-12-18T01:36:56.9162245Z 2024-12-18T01:36:56.9162373Z >>> iseven = lambda x: x % 2 == 0 2024-12-18T01:36:56.9162550Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2024-12-18T01:36:56.9162685Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2024-12-18T01:36:56.9162776Z 2024-12-18T01:36:56.9162925Z Non-callable keys imply grouping on a member. 2024-12-18T01:36:56.9163056Z 2024-12-18T01:36:56.9163154Z >>> groupby( 2024-12-18T01:36:56.9163269Z ... "gender", 2024-12-18T01:36:56.9163366Z ... [ 2024-12-18T01:36:56.9163494Z ... {"name": "Alice", "gender": "F"}, 2024-12-18T01:36:56.9163632Z ... {"name": "Bob", "gender": "M"}, 2024-12-18T01:36:56.9163760Z ... {"name": "Charlie", "gender": "M"}, 2024-12-18T01:36:56.9163867Z ... ], 2024-12-18T01:36:56.9163995Z ... ) # doctest:+SKIP 2024-12-18T01:36:56.9164119Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2024-12-18T01:36:56.9164251Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2024-12-18T01:36:56.9164370Z {'gender': 'M', 'name': 'Charlie'}]} 2024-12-18T01:36:56.9164476Z 2024-12-18T01:36:56.9164625Z Not to be confused with ``itertools.groupby`` 2024-12-18T01:36:56.9164731Z 2024-12-18T01:36:56.9164825Z See Also: 2024-12-18T01:36:56.9164918Z countby 2024-12-18T01:36:56.9165023Z 2024-12-18T01:36:56.9165303Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9165409Z 2024-12-18T01:36:56.9165513Z warnings.warn(msg) 2024-12-18T01:36:56.9165601Z 2024-12-18T01:36:56.9165802Z --- Parse Warning: 80 / 105 --- 2024-12-18T01:36:56.9166688Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601. 2024-12-18T01:36:56.9166965Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9167145Z Applies Batch Normalization over a N-Dimensional input. 2024-12-18T01:36:56.9167250Z 2024-12-18T01:36:56.9167591Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2024-12-18T01:36:56.9167824Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2024-12-18T01:36:56.9168060Z Internal Covariate Shift `__ . 2024-12-18T01:36:56.9168152Z 2024-12-18T01:36:56.9168267Z .. math:: 2024-12-18T01:36:56.9168357Z 2024-12-18T01:36:56.9168589Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2024-12-18T01:36:56.9168677Z 2024-12-18T01:36:56.9168907Z The mean and standard-deviation are calculated per-dimension over all 2024-12-18T01:36:56.9169156Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2024-12-18T01:36:56.9169397Z are learnable parameter vectors of size `C` (where `C` is the input size). 2024-12-18T01:36:56.9169589Z By default, the elements of :math:`\gamma` are sampled from 2024-12-18T01:36:56.9169790Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2024-12-18T01:36:56.9170063Z The standard-deviation is calculated via the biased estimator, equivalent to 2024-12-18T01:36:56.9170187Z `torch.var(input, unbiased=False)`. 2024-12-18T01:36:56.9170307Z 2024-12-18T01:36:56.9170554Z Also by default, during training this layer keeps running estimates of its 2024-12-18T01:36:56.9170787Z computed mean and variance, which are then used for normalization during 2024-12-18T01:36:56.9171037Z evaluation. The running estimates are kept with a default :attr:`momentum` 2024-12-18T01:36:56.9171127Z of 0.1. 2024-12-18T01:36:56.9171229Z 2024-12-18T01:36:56.9171455Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2024-12-18T01:36:56.9171688Z keep running estimates, and batch statistics are instead used during 2024-12-18T01:36:56.9171795Z evaluation time as well. 2024-12-18T01:36:56.9171894Z 2024-12-18T01:36:56.9171987Z .. note:: 2024-12-18T01:36:56.9172208Z This :attr:`momentum` argument is different from one used in optimizer 2024-12-18T01:36:56.9172491Z classes and the conventional notion of momentum. Mathematically, the 2024-12-18T01:36:56.9172631Z update rule for running statistics here is 2024-12-18T01:36:56.9172912Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2024-12-18T01:36:56.9173117Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2024-12-18T01:36:56.9173261Z new observed value. 2024-12-18T01:36:56.9173351Z 2024-12-18T01:36:56.9173657Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2024-12-18T01:36:56.9173922Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2024-12-18T01:36:56.9174105Z Normalization or Spatio-temporal Batch Normalization. 2024-12-18T01:36:56.9174205Z 2024-12-18T01:36:56.9174355Z Currently :class:`SyncBatchNorm` only supports 2024-12-18T01:36:56.9174678Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2024-12-18T01:36:56.9174898Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2024-12-18T01:36:56.9175105Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2024-12-18T01:36:56.9175217Z Network with DDP. 2024-12-18T01:36:56.9175305Z 2024-12-18T01:36:56.9175409Z Args: 2024-12-18T01:36:56.9175579Z num_features: :math:`C` from an expected input of size 2024-12-18T01:36:56.9175681Z :math:`(N, C, +)` 2024-12-18T01:36:56.9175881Z eps: a value added to the denominator for numerical stability. 2024-12-18T01:36:56.9175983Z Default: ``1e-5`` 2024-12-18T01:36:56.9176185Z momentum: the value used for the running_mean and running_var 2024-12-18T01:36:56.9176394Z computation. Can be set to ``None`` for cumulative moving average 2024-12-18T01:36:56.9176531Z (i.e. simple average). Default: 0.1 2024-12-18T01:36:56.9176734Z affine: a boolean value that when set to ``True``, this module has 2024-12-18T01:36:56.9176889Z learnable affine parameters. Default: ``True`` 2024-12-18T01:36:56.9177113Z track_running_stats: a boolean value that when set to ``True``, this 2024-12-18T01:36:56.9177341Z module tracks the running mean and variance, and when set to ``False``, 2024-12-18T01:36:56.9177577Z this module does not track such statistics, and initializes statistics 2024-12-18T01:36:56.9177778Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2024-12-18T01:36:56.9178020Z When these buffers are ``None``, this module always uses batch statistics. 2024-12-18T01:36:56.9178176Z in both training and eval modes. Default: ``True`` 2024-12-18T01:36:56.9178438Z process_group: synchronization of stats happen within each process group 2024-12-18T01:36:56.9178667Z individually. Default behavior is synchronization across the whole 2024-12-18T01:36:56.9178785Z world 2024-12-18T01:36:56.9178883Z 2024-12-18T01:36:56.9178973Z Shape: 2024-12-18T01:36:56.9179097Z - Input: :math:`(N, C, +)` 2024-12-18T01:36:56.9179249Z - Output: :math:`(N, C, +)` (same shape as input) 2024-12-18T01:36:56.9179337Z 2024-12-18T01:36:56.9179444Z .. note:: 2024-12-18T01:36:56.9179689Z Synchronization of batchnorm statistics occurs only while training, i.e. 2024-12-18T01:36:56.9179904Z synchronization is disabled when ``model.eval()`` is set or if 2024-12-18T01:36:56.9180037Z ``self.training`` is otherwise ``False``. 2024-12-18T01:36:56.9180137Z 2024-12-18T01:36:56.9180233Z Examples:: 2024-12-18T01:36:56.9180320Z 2024-12-18T01:36:56.9180438Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9180583Z >>> # With Learnable Parameters 2024-12-18T01:36:56.9180710Z >>> m = nn.SyncBatchNorm(100) 2024-12-18T01:36:56.9180842Z >>> # creating process group (optional) 2024-12-18T01:36:56.9180990Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:36:56.9181112Z >>> ranks = list(range(8)) 2024-12-18T01:36:56.9181226Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:36:56.9181413Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:36:56.9181568Z >>> # process group created, even if that rank is not 2024-12-18T01:36:56.9181684Z >>> # part of the group. 2024-12-18T01:36:56.9181934Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:36:56.9182140Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:36:56.9182273Z >>> # Without Learnable Parameters 2024-12-18T01:36:56.9182481Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2024-12-18T01:36:56.9182647Z >>> input = torch.randn(20, 100, 35, 45, 10) 2024-12-18T01:36:56.9182753Z >>> output = m(input) 2024-12-18T01:36:56.9182843Z 2024-12-18T01:36:56.9182978Z >>> # network is nn.BatchNorm layer 2024-12-18T01:36:56.9183253Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2024-12-18T01:36:56.9183433Z >>> # only single gpu per process is currently supported 2024-12-18T01:36:56.9183657Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:36:56.9183787Z >>> sync_bn_network, 2024-12-18T01:36:56.9183920Z >>> device_ids=[args.local_rank], 2024-12-18T01:36:56.9184054Z >>> output_device=args.local_rank) 2024-12-18T01:36:56.9184158Z 2024-12-18T01:36:56.9184409Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9184509Z 2024-12-18T01:36:56.9184614Z warnings.warn(msg) 2024-12-18T01:36:56.9184717Z 2024-12-18T01:36:56.9184925Z --- Parse Warning: 81 / 105 --- 2024-12-18T01:36:56.9185916Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=824. 2024-12-18T01:36:56.9186192Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9186499Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2024-12-18T01:36:56.9186605Z 2024-12-18T01:36:56.9186697Z Args: 2024-12-18T01:36:56.9186957Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2024-12-18T01:36:56.9187179Z process_group (optional): process group to scope synchronization, 2024-12-18T01:36:56.9187299Z default is the whole world 2024-12-18T01:36:56.9187427Z 2024-12-18T01:36:56.9187520Z Returns: 2024-12-18T01:36:56.9187784Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2024-12-18T01:36:56.9187998Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2024-12-18T01:36:56.9188225Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2024-12-18T01:36:56.9188321Z instead. 2024-12-18T01:36:56.9188408Z 2024-12-18T01:36:56.9188515Z Example:: 2024-12-18T01:36:56.9188603Z 2024-12-18T01:36:56.9188742Z >>> # Network with nn.BatchNorm layer 2024-12-18T01:36:56.9188886Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:56.9189108Z >>> module = torch.nn.Sequential( 2024-12-18T01:36:56.9189234Z >>> torch.nn.Linear(20, 100), 2024-12-18T01:36:56.9189364Z >>> torch.nn.BatchNorm1d(100), 2024-12-18T01:36:56.9189478Z >>> ).cuda() 2024-12-18T01:36:56.9189607Z >>> # creating process group (optional) 2024-12-18T01:36:56.9189767Z >>> # ranks is a list of int identifying rank ids. 2024-12-18T01:36:56.9189904Z >>> ranks = list(range(8)) 2024-12-18T01:36:56.9190029Z >>> r1, r2 = ranks[:4], ranks[4:] 2024-12-18T01:36:56.9190179Z >>> # Note: every rank calls into new_group for every 2024-12-18T01:36:56.9190336Z >>> # process group created, even if that rank is not 2024-12-18T01:36:56.9190453Z >>> # part of the group. 2024-12-18T01:36:56.9190579Z >>> # xdoctest: +SKIP("distributed") 2024-12-18T01:36:56.9190836Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2024-12-18T01:36:56.9191065Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2024-12-18T01:36:56.9191371Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2024-12-18T01:36:56.9191457Z 2024-12-18T01:36:56.9191549Z 2024-12-18T01:36:56.9191810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9191897Z 2024-12-18T01:36:56.9192012Z warnings.warn(msg) 2024-12-18T01:36:56.9192099Z 2024-12-18T01:36:56.9192294Z --- Parse Warning: 82 / 105 --- 2024-12-18T01:36:56.9193154Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60. 2024-12-18T01:36:56.9193417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9193516Z 2024-12-18T01:36:56.9193826Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2024-12-18T01:36:56.9193923Z 2024-12-18T01:36:56.9194191Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2024-12-18T01:36:56.9194427Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2024-12-18T01:36:56.9194514Z 2024-12-18T01:36:56.9194828Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2024-12-18T01:36:56.9195090Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2024-12-18T01:36:56.9195261Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2024-12-18T01:36:56.9195353Z 2024-12-18T01:36:56.9195445Z Shape: 2024-12-18T01:36:56.9195752Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2024-12-18T01:36:56.9196012Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2024-12-18T01:36:56.9196266Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2024-12-18T01:36:56.9196407Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2024-12-18T01:36:56.9196492Z 2024-12-18T01:36:56.9196594Z Args: 2024-12-18T01:36:56.9196747Z dim (Union[int, str]): Dimension to be unflattened 2024-12-18T01:36:56.9197090Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2024-12-18T01:36:56.9197186Z 2024-12-18T01:36:56.9197279Z Examples: 2024-12-18T01:36:56.9197396Z >>> input = torch.randn(2, 50) 2024-12-18T01:36:56.9197496Z >>> # With tuple of ints 2024-12-18T01:36:56.9197607Z >>> m = nn.Sequential( 2024-12-18T01:36:56.9197707Z >>> nn.Linear(50, 50), 2024-12-18T01:36:56.9197844Z >>> nn.Unflatten(1, (2, 5, 5)) 2024-12-18T01:36:56.9198325Z >>> ) 2024-12-18T01:36:56.9198428Z >>> output = m(input) 2024-12-18T01:36:56.9198540Z >>> output.size() 2024-12-18T01:36:56.9198643Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:36:56.9198740Z >>> # With torch.Size 2024-12-18T01:36:56.9198850Z >>> m = nn.Sequential( 2024-12-18T01:36:56.9198950Z >>> nn.Linear(50, 50), 2024-12-18T01:36:56.9199085Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2024-12-18T01:36:56.9199243Z >>> ) 2024-12-18T01:36:56.9199346Z >>> output = m(input) 2024-12-18T01:36:56.9199451Z >>> output.size() 2024-12-18T01:36:56.9199553Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:36:56.9199685Z >>> # With namedshape (tuple of tuples) 2024-12-18T01:36:56.9199836Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2024-12-18T01:36:56.9200050Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2024-12-18T01:36:56.9200164Z >>> output = unflatten(input) 2024-12-18T01:36:56.9200262Z >>> output.size() 2024-12-18T01:36:56.9200647Z torch.Size([2, 2, 5, 5]) 2024-12-18T01:36:56.9200736Z 2024-12-18T01:36:56.9200997Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9201083Z 2024-12-18T01:36:56.9201184Z warnings.warn(msg) 2024-12-18T01:36:56.9201278Z 2024-12-18T01:36:56.9201501Z --- Parse Warning: 83 / 105 --- 2024-12-18T01:36:56.9202465Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1698. 2024-12-18T01:36:56.9202981Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9203192Z Creates a criterion that measures the triplet loss given input 2024-12-18T01:36:56.9203387Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2024-12-18T01:36:56.9203601Z positive, and negative examples, respectively), and a nonnegative, 2024-12-18T01:36:56.9203860Z real-valued function ("distance function") used to compute the relationship 2024-12-18T01:36:56.9204079Z between the anchor and positive example ("positive distance") and the 2024-12-18T01:36:56.9204242Z anchor and negative example ("negative distance"). 2024-12-18T01:36:56.9204325Z 2024-12-18T01:36:56.9204543Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2024-12-18T01:36:56.9204644Z can be described as: 2024-12-18T01:36:56.9204731Z 2024-12-18T01:36:56.9204839Z .. math:: 2024-12-18T01:36:56.9204983Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2024-12-18T01:36:56.9205148Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2024-12-18T01:36:56.9205234Z 2024-12-18T01:36:56.9205489Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2024-12-18T01:36:56.9205777Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2024-12-18T01:36:56.9206087Z and :math:`margin` is a nonnegative margin representing the minimum difference 2024-12-18T01:36:56.9206342Z between the positive and negative distances that is required for the loss to 2024-12-18T01:36:56.9206572Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2024-12-18T01:36:56.9206707Z that the distance function can handle. 2024-12-18T01:36:56.9206795Z 2024-12-18T01:36:56.9206931Z If :attr:`reduction` is not ``'none'`` 2024-12-18T01:36:56.9207036Z (default ``'mean'``), then: 2024-12-18T01:36:56.9207123Z 2024-12-18T01:36:56.9207230Z .. math:: 2024-12-18T01:36:56.9207326Z \ell(x, y) = 2024-12-18T01:36:56.9207438Z \begin{cases} 2024-12-18T01:36:56.9207678Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2024-12-18T01:36:56.9207874Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2024-12-18T01:36:56.9207978Z \end{cases} 2024-12-18T01:36:56.9208066Z 2024-12-18T01:36:56.9208310Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2024-12-18T01:36:56.9208555Z loss for input tensors using the :math:`l_p` distance as the distance function. 2024-12-18T01:36:56.9208678Z 2024-12-18T01:36:56.9208767Z Args: 2024-12-18T01:36:56.9209039Z distance_function (Callable, optional): A nonnegative, real-valued function that 2024-12-18T01:36:56.9209236Z quantifies the closeness of two tensors. If not specified, 2024-12-18T01:36:56.9209412Z `nn.PairwiseDistance` will be used. Default: ``None`` 2024-12-18T01:36:56.9210005Z margin (float, optional): A nonnegative margin representing the minimum difference 2024-12-18T01:36:56.9210670Z between the positive and negative distances required for the loss to be 0. Larger 2024-12-18T01:36:56.9211365Z margins penalize cases where the negative examples are not distant enough from the 2024-12-18T01:36:56.9211933Z anchors, relative to the positives. Default: :math:`1`. 2024-12-18T01:36:56.9212466Z swap (bool, optional): Whether to use the distance swap described in the paper 2024-12-18T01:36:56.9213091Z `Learning shallow convolutional feature descriptors with triplet losses` by 2024-12-18T01:36:56.9213707Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2024-12-18T01:36:56.9214318Z negative example than the anchor is, swaps the positive example and the anchor in 2024-12-18T01:36:56.9214841Z the loss computation. Default: ``False``. 2024-12-18T01:36:56.9215368Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2024-12-18T01:36:56.9215941Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2024-12-18T01:36:56.9216429Z ``'mean'``: the sum of the output will be divided by the number of 2024-12-18T01:36:56.9216970Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2024-12-18T01:36:56.9217416Z 2024-12-18T01:36:56.9217622Z 2024-12-18T01:36:56.9217845Z Shape: 2024-12-18T01:36:56.9218221Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2024-12-18T01:36:56.9218721Z as supported by the distance function. 2024-12-18T01:36:56.9219217Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2024-12-18T01:36:56.9219689Z otherwise. 2024-12-18T01:36:56.9219945Z 2024-12-18T01:36:56.9220153Z Examples:: 2024-12-18T01:36:56.9220392Z 2024-12-18T01:36:56.9220611Z >>> # Initialize embeddings 2024-12-18T01:36:56.9220944Z >>> embedding = nn.Embedding(1000, 128) 2024-12-18T01:36:56.9221363Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:36:56.9221762Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:36:56.9222149Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2024-12-18T01:36:56.9222496Z >>> anchor = embedding(anchor_ids) 2024-12-18T01:36:56.9222849Z >>> positive = embedding(positive_ids) 2024-12-18T01:36:56.9223201Z >>> negative = embedding(negative_ids) 2024-12-18T01:36:56.9223519Z >>> 2024-12-18T01:36:56.9223757Z >>> # Built-in Distance Function 2024-12-18T01:36:56.9224066Z >>> triplet_loss = \ 2024-12-18T01:36:56.9224530Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2024-12-18T01:36:56.9225085Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:36:56.9225486Z >>> output.backward() 2024-12-18T01:36:56.9225760Z >>> 2024-12-18T01:36:56.9225991Z >>> # Custom Distance Function 2024-12-18T01:36:56.9226307Z >>> def l_infinity(x1, x2): 2024-12-18T01:36:56.9226671Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2024-12-18T01:36:56.9227031Z >>> 2024-12-18T01:36:56.9227348Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2024-12-18T01:36:56.9227743Z >>> triplet_loss = ( 2024-12-18T01:36:56.9228229Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2024-12-18T01:36:56.9228783Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:36:56.9229156Z >>> output.backward() 2024-12-18T01:36:56.9229435Z >>> 2024-12-18T01:36:56.9229675Z >>> # Custom Distance Function (Lambda) 2024-12-18T01:36:56.9230005Z >>> triplet_loss = ( 2024-12-18T01:36:56.9230322Z >>> nn.TripletMarginWithDistanceLoss( 2024-12-18T01:36:56.9230789Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2024-12-18T01:36:56.9231300Z >>> output = triplet_loss(anchor, positive, negative) 2024-12-18T01:36:56.9231665Z >>> output.backward() 2024-12-18T01:36:56.9231937Z 2024-12-18T01:36:56.9232147Z Reference: 2024-12-18T01:36:56.9232599Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2024-12-18T01:36:56.9233243Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2024-12-18T01:36:56.9233659Z 2024-12-18T01:36:56.9234039Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2024-12-18T01:36:56.9234495Z 2024-12-18T01:36:56.9234721Z warnings.warn(msg) 2024-12-18T01:36:56.9234982Z 2024-12-18T01:36:56.9235311Z --- Parse Warning: 84 / 105 --- 2024-12-18T01:36:56.9236515Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395. 2024-12-18T01:36:56.9237775Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9238309Z Computes a partial inverse of :class:`MaxPool2d`. 2024-12-18T01:36:56.9238659Z 2024-12-18T01:36:56.9239039Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2024-12-18T01:36:56.9239495Z 2024-12-18T01:36:56.9239839Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2024-12-18T01:36:56.9240417Z including the indices of the maximal values and computes a partial inverse 2024-12-18T01:36:56.9240921Z in which all non-maximal values are set to zero. 2024-12-18T01:36:56.9241262Z 2024-12-18T01:36:56.9241462Z Note: 2024-12-18T01:36:56.9241915Z This operation may behave nondeterministically when the input indices has repeat values. 2024-12-18T01:36:56.9242724Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2024-12-18T01:36:56.9243343Z 2024-12-18T01:36:56.9243703Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2024-12-18T01:36:56.9244227Z sizes. Hence, the inversion process can get ambiguous. 2024-12-18T01:36:56.9244706Z To accommodate this, you can provide the needed output size 2024-12-18T01:36:56.9245224Z as an additional argument :attr:`output_size` in the forward call. 2024-12-18T01:36:56.9245682Z See the Inputs and Example below. 2024-12-18T01:36:56.9246006Z 2024-12-18T01:36:56.9246215Z Args: 2024-12-18T01:36:56.9246522Z kernel_size (int or tuple): Size of the max pooling window. 2024-12-18T01:36:56.9246997Z stride (int or tuple): Stride of the max pooling window. 2024-12-18T01:36:56.9247456Z It is set to :attr:`kernel_size` by default. 2024-12-18T01:36:56.9247894Z padding (int or tuple): Padding that was added to the input 2024-12-18T01:36:56.9248284Z 2024-12-18T01:36:56.9248478Z Inputs: 2024-12-18T01:36:56.9248743Z - `input`: the input Tensor to invert 2024-12-18T01:36:56.9249195Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2024-12-18T01:36:56.9249712Z - `output_size` (optional): the targeted output size 2024-12-18T01:36:56.9250069Z 2024-12-18T01:36:56.9250265Z Shape: 2024-12-18T01:36:56.9250592Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2024-12-18T01:36:56.9251107Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2024-12-18T01:36:56.9251524Z 2024-12-18T01:36:56.9251741Z .. math:: 2024-12-18T01:36:56.9252165Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2024-12-18T01:36:56.9252645Z 2024-12-18T01:36:56.9252891Z .. math:: 2024-12-18T01:36:56.9253319Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2024-12-18T01:36:56.9253785Z 2024-12-18T01:36:56.9254064Z or as given by :attr:`output_size` in the call operator 2024-12-18T01:36:56.9254443Z 2024-12-18T01:36:56.9254662Z Example:: 2024-12-18T01:36:56.9254905Z 2024-12-18T01:36:56.9255198Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2024-12-18T01:36:56.9255601Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2024-12-18T01:36:56.9255989Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2024-12-18T01:36:56.9256369Z [ 5., 6., 7., 8.], 2024-12-18T01:36:56.9256722Z [ 9., 10., 11., 12.], 2024-12-18T01:36:56.9257076Z [13., 14., 15., 16.]]]]) 2024-12-18T01:36:56.9257418Z >>> output, indices = pool(input) 2024-12-18T01:36:56.9257771Z >>> unpool(output, indices) 2024-12-18T01:36:56.9258106Z tensor([[[[ 0., 0., 0., 0.], 2024-12-18T01:36:56.9258438Z [ 0., 6., 0., 8.], 2024-12-18T01:36:56.9258758Z [ 0., 0., 0., 0.], 2024-12-18T01:36:56.9259071Z [ 0., 14., 0., 16.]]]]) 2024-12-18T01:36:56.9259506Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2024-12-18T01:36:56.9259986Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2024-12-18T01:36:56.9260361Z [ 6., 7., 8., 9., 10.], 2024-12-18T01:36:56.9260711Z [11., 12., 13., 14., 15.], 2024-12-18T01:36:56.9261058Z [16., 17., 18., 19., 20.]]]]) 2024-12-18T01:36:56.9261406Z >>> output, indices = pool(input) 2024-12-18T01:36:56.9261811Z >>> # This call will not work without specifying output_size 2024-12-18T01:36:56.9262296Z >>> unpool(output, indices, output_size=input.size()) 2024-12-18T01:36:56.9262679Z tensor([[[[ 0., 0., 0., 0., 0.], 2024-12-18T01:36:56.9263007Z [ 0., 7., 0., 9., 0.], 2024-12-18T01:36:56.9263322Z [ 0., 0., 0., 0., 0.], 2024-12-18T01:36:56.9263646Z [ 0., 17., 0., 19., 0.]]]]) 2024-12-18T01:36:56.9263957Z 2024-12-18T01:36:56.9264170Z 2024-12-18T01:36:56.9264384Z 2024-12-18T01:36:56.9264755Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9265213Z 2024-12-18T01:36:56.9265433Z warnings.warn(msg) 2024-12-18T01:36:56.9265693Z 2024-12-18T01:36:56.9266029Z --- Parse Warning: 85 / 105 --- 2024-12-18T01:36:56.9267172Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270. 2024-12-18T01:36:56.9268422Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9269115Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2024-12-18T01:36:56.9269659Z 2024-12-18T01:36:56.9270104Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2024-12-18T01:36:56.9270657Z and with 2D inputs, this class 2024-12-18T01:36:56.9270953Z 2024-12-18T01:36:56.9271380Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2024-12-18T01:36:56.9272110Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2024-12-18T01:36:56.9272886Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2024-12-18T01:36:56.9273398Z 2024-12-18T01:36:56.9273871Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2024-12-18T01:36:56.9274421Z operations. 2024-12-18T01:36:56.9274667Z 2024-12-18T01:36:56.9275052Z EmbeddingBag also supports per-sample weights as an argument to the forward 2024-12-18T01:36:56.9275760Z pass. This scales the output of the Embedding before performing a weighted 2024-12-18T01:36:56.9276374Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2024-12-18T01:36:56.9276970Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2024-12-18T01:36:56.9277438Z :attr:`per_sample_weights`. 2024-12-18T01:36:56.9277744Z 2024-12-18T01:36:56.9277965Z Args: 2024-12-18T01:36:56.9278297Z num_embeddings (int): size of the dictionary of embeddings 2024-12-18T01:36:56.9278771Z embedding_dim (int): the size of each embedding vector 2024-12-18T01:36:56.9279353Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2024-12-18T01:36:56.9279940Z is renormalized to have norm :attr:`max_norm`. 2024-12-18T01:36:56.9280560Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2024-12-18T01:36:56.9281332Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2024-12-18T01:36:56.9281938Z the words in the mini-batch. Default ``False``. 2024-12-18T01:36:56.9282413Z Note: this option is not supported when ``mode="max"``. 2024-12-18T01:36:56.9282964Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2024-12-18T01:36:56.9283594Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2024-12-18T01:36:56.9284154Z into consideration. ``"mean"`` computes the average of the values 2024-12-18T01:36:56.9284679Z in the bag, ``"max"`` computes the max value over each bag. 2024-12-18T01:36:56.9285103Z Default: ``"mean"`` 2024-12-18T01:36:56.9285655Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2024-12-18T01:36:56.9286332Z Notes for more details regarding sparse gradients. Note: this option is not 2024-12-18T01:36:56.9286842Z supported when ``mode="max"``. 2024-12-18T01:36:56.9287484Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2024-12-18T01:36:56.9288202Z is equivalent to the size of `indices`. This matches the CSR format. 2024-12-18T01:36:56.9288873Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2024-12-18T01:36:56.9289621Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2024-12-18T01:36:56.9290264Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2024-12-18T01:36:56.9290886Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2024-12-18T01:36:56.9291516Z zeros, but can be updated to another value to be used as the padding vector. 2024-12-18T01:36:56.9292127Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2024-12-18T01:36:56.9292630Z reduction. 2024-12-18T01:36:56.9292950Z 2024-12-18T01:36:56.9293167Z Attributes: 2024-12-18T01:36:56.9293627Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2024-12-18T01:36:56.9294205Z initialized from :math:`\mathcal{N}(0, 1)`. 2024-12-18T01:36:56.9294560Z 2024-12-18T01:36:56.9294784Z Examples:: 2024-12-18T01:36:56.9295024Z 2024-12-18T01:36:56.9295309Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2024-12-18T01:36:56.9295771Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2024-12-18T01:36:56.9296191Z >>> # a batch of 2 samples of 4 indices each 2024-12-18T01:36:56.9296628Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:36:56.9297098Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:36:56.9297520Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2024-12-18T01:36:56.9298063Z >>> embedding_sum(input, offsets) 2024-12-18T01:36:56.9298418Z tensor([[-0.8861, -5.4350, -0.0523], 2024-12-18T01:36:56.9298760Z [ 1.1306, -2.5798, -1.0044]]) 2024-12-18T01:36:56.9299073Z 2024-12-18T01:36:56.9299324Z >>> # Example with padding_idx 2024-12-18T01:36:56.9299749Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2024-12-18T01:36:56.9300263Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2024-12-18T01:36:56.9300734Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2024-12-18T01:36:56.9301128Z >>> embedding_sum(input, offsets) 2024-12-18T01:36:56.9301471Z tensor([[ 0.0000, 0.0000, 0.0000], 2024-12-18T01:36:56.9301793Z [-0.7082, 3.2145, -2.6251]]) 2024-12-18T01:36:56.9302100Z 2024-12-18T01:36:56.9302398Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2024-12-18T01:36:56.9303130Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2024-12-18T01:36:56.9303565Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2024-12-18T01:36:56.9303941Z embedding.weight, 2024-12-18T01:36:56.9304294Z padding_idx=embedding.padding_idx, 2024-12-18T01:36:56.9304643Z mode='sum') 2024-12-18T01:36:56.9304917Z 2024-12-18T01:36:56.9305293Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9305740Z 2024-12-18T01:36:56.9305971Z warnings.warn(msg) 2024-12-18T01:36:56.9306233Z 2024-12-18T01:36:56.9306582Z --- Parse Warning: 86 / 105 --- 2024-12-18T01:36:56.9307819Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1748. 2024-12-18T01:36:56.9309238Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9309693Z 2024-12-18T01:36:56.9310054Z Context manager for training with uneven inputs across processes in DDP. 2024-12-18T01:36:56.9310495Z 2024-12-18T01:36:56.9310891Z This context manager will keep track of already-joined DDP processes, 2024-12-18T01:36:56.9311446Z and "shadow" the forward and backward passes by inserting collective 2024-12-18T01:36:56.9312000Z communication operations to match with the ones created by non-joined 2024-12-18T01:36:56.9312587Z DDP processes. This will ensure each collective call has a corresponding 2024-12-18T01:36:56.9313163Z call by already-joined DDP processes, preventing hangs or errors that 2024-12-18T01:36:56.9313707Z would otherwise happen when training with uneven inputs across 2024-12-18T01:36:56.9314299Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2024-12-18T01:36:56.9314868Z specified to be ``True``, all trainers will throw an error once one rank 2024-12-18T01:36:56.9315387Z runs out of inputs, allowing these errors to be caught and handled 2024-12-18T01:36:56.9315886Z according to application logic. 2024-12-18T01:36:56.9316199Z 2024-12-18T01:36:56.9316549Z Once all DDP processes have joined, the context manager will broadcast 2024-12-18T01:36:56.9317119Z the model corresponding to the last joined process to all processes to 2024-12-18T01:36:56.9317615Z ensure the model is the same across all processes 2024-12-18T01:36:56.9317994Z (which is guaranteed by DDP). 2024-12-18T01:36:56.9318288Z 2024-12-18T01:36:56.9318616Z To use this to enable training with uneven inputs across processes, 2024-12-18T01:36:56.9319170Z simply wrap this context manager around your training loop. No further 2024-12-18T01:36:56.9319684Z modifications to the model or data loading is required. 2024-12-18T01:36:56.9320065Z 2024-12-18T01:36:56.9320286Z .. warning:: 2024-12-18T01:36:56.9320647Z If the model or training loop this context manager is wrapped around 2024-12-18T01:36:56.9321155Z has additional distributed collective operations, such as 2024-12-18T01:36:56.9321651Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2024-12-18T01:36:56.9322160Z ``throw_on_early_termination`` must be enabled. This is because this 2024-12-18T01:36:56.9322698Z context manager is not aware of non-DDP collective communication. 2024-12-18T01:36:56.9323204Z This flag will cause all ranks to throw when any one rank 2024-12-18T01:36:56.9323705Z exhausts inputs, allowing these errors to be caught and recovered 2024-12-18T01:36:56.9324143Z from across all ranks. 2024-12-18T01:36:56.9324414Z 2024-12-18T01:36:56.9324623Z Args: 2024-12-18T01:36:56.9324936Z divide_by_initial_world_size (bool): If ``True``, will divide 2024-12-18T01:36:56.9325490Z gradients by the initial ``world_size`` DDP training was launched 2024-12-18T01:36:56.9325985Z with. If ``False``, will compute the effective world size 2024-12-18T01:36:56.9326447Z (number of ranks that have not depleted their inputs yet) and 2024-12-18T01:36:56.9326906Z divide gradients by that during allreduce. Set 2024-12-18T01:36:56.9327351Z ``divide_by_initial_world_size=True`` to ensure every input 2024-12-18T01:36:56.9327864Z sample including the uneven inputs have equal weight in terms of 2024-12-18T01:36:56.9328368Z how much they contribute to the global gradient. This is 2024-12-18T01:36:56.9328837Z achieved by always dividing the gradient by the initial 2024-12-18T01:36:56.9329311Z ``world_size`` even when we encounter uneven inputs. If you set 2024-12-18T01:36:56.9329832Z this to ``False``, we divide the gradient by the remaining 2024-12-18T01:36:56.9330322Z number of nodes. This ensures parity with training on a smaller 2024-12-18T01:36:56.9330822Z ``world_size`` although it also means the uneven inputs would 2024-12-18T01:36:56.9331325Z contribute more towards the global gradient. Typically, you 2024-12-18T01:36:56.9331860Z would want to set this to ``True`` for cases where the last few 2024-12-18T01:36:56.9332362Z inputs of your training job are uneven. In extreme cases, where 2024-12-18T01:36:56.9332869Z there is a large discrepancy in the number of inputs, setting 2024-12-18T01:36:56.9333324Z this to ``False`` might provide better results. 2024-12-18T01:36:56.9333799Z enable (bool): Whether to enable uneven input detection or not. Pass 2024-12-18T01:36:56.9334307Z in ``enable=False`` to disable in cases where you know that 2024-12-18T01:36:56.9334784Z inputs are even across participating processes. Default is 2024-12-18T01:36:56.9335216Z ``True``. 2024-12-18T01:36:56.9335574Z throw_on_early_termination (bool): Whether to throw an error 2024-12-18T01:36:56.9336059Z or continue training when at least one rank has exhausted 2024-12-18T01:36:56.9336552Z inputs. If ``True``, will throw upon the first rank reaching end 2024-12-18T01:36:56.9337044Z of data. If ``False``, will continue training with a smaller 2024-12-18T01:36:56.9337527Z effective world size until all ranks are joined. Note that if 2024-12-18T01:36:56.9337974Z this flag is specified, then the flag 2024-12-18T01:36:56.9338401Z ``divide_by_initial_world_size`` would be ignored. Default 2024-12-18T01:36:56.9338801Z is ``False``. 2024-12-18T01:36:56.9339074Z 2024-12-18T01:36:56.9339275Z 2024-12-18T01:36:56.9339500Z Example:: 2024-12-18T01:36:56.9339734Z 2024-12-18T01:36:56.9339985Z >>> # xdoctest: +SKIP("Distributed") 2024-12-18T01:36:56.9340323Z >>> import torch 2024-12-18T01:36:56.9340616Z >>> import torch.distributed as dist 2024-12-18T01:36:56.9340952Z >>> import os 2024-12-18T01:36:56.9341248Z >>> import torch.multiprocessing as mp 2024-12-18T01:36:56.9341600Z >>> import torch.nn as nn 2024-12-18T01:36:56.9341918Z >>> # On each spawned worker 2024-12-18T01:36:56.9342222Z >>> def worker(rank): 2024-12-18T01:36:56.9342588Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2024-12-18T01:36:56.9343003Z >>> torch.cuda.set_device(rank) 2024-12-18T01:36:56.9343373Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2024-12-18T01:36:56.9343810Z >>> model = torch.nn.parallel.DistributedDataParallel( 2024-12-18T01:36:56.9344243Z >>> model, device_ids=[rank], output_device=rank 2024-12-18T01:36:56.9344598Z >>> ) 2024-12-18T01:36:56.9344877Z >>> # Rank 1 gets one more input than rank 0. 2024-12-18T01:36:56.9345314Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2024-12-18T01:36:56.9345768Z >>> with model.join(): 2024-12-18T01:36:56.9346063Z >>> for _ in range(5): 2024-12-18T01:36:56.9346388Z >>> for inp in inputs: 2024-12-18T01:36:56.9346728Z >>> loss = model(inp).sum() 2024-12-18T01:36:56.9347076Z >>> loss.backward() 2024-12-18T01:36:56.9347493Z >>> # Without the join() API, the below synchronization will hang 2024-12-18T01:36:56.9347958Z >>> # blocking for rank 1's allreduce to complete. 2024-12-18T01:36:56.9348346Z >>> torch.cuda.synchronize(device=rank) 2024-12-18T01:36:56.9348683Z 2024-12-18T01:36:56.9349063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9349552Z 2024-12-18T01:36:56.9349781Z warnings.warn(msg) 2024-12-18T01:36:56.9350033Z 2024-12-18T01:36:56.9350375Z --- Parse Warning: 87 / 105 --- 2024-12-18T01:36:56.9351677Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2039. 2024-12-18T01:36:56.9353146Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9353623Z 2024-12-18T01:36:56.9354050Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2024-12-18T01:36:56.9354575Z 2024-12-18T01:36:56.9354916Z Registers an optimizer with DDP such that the optimization for a 2024-12-18T01:36:56.9355455Z parameter will run immediately when that parameter's gradient is 2024-12-18T01:36:56.9356087Z finished with reduction, instead of waiting for all parameters' 2024-12-18T01:36:56.9356666Z gradients to finish reduction. This can result in a training speedup 2024-12-18T01:36:56.9357216Z depending on your workload since the optimizer can run while gradient 2024-12-18T01:36:56.9357785Z reduction for other parameters are still ongoing. In addition, this has 2024-12-18T01:36:56.9358366Z the potential to reduce peak memory consumption during training, as it 2024-12-18T01:36:56.9358919Z only needs to load the per-parameter optimizer states of a single 2024-12-18T01:36:56.9359454Z parameter at a time, instead of loading all per-parameter optimizer 2024-12-18T01:36:56.9359870Z states at once. 2024-12-18T01:36:56.9360111Z 2024-12-18T01:36:56.9360324Z Args: 2024-12-18T01:36:56.9360657Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2024-12-18T01:36:56.9361083Z as a fused optimizer. 2024-12-18T01:36:56.9361436Z *args (Sequence[Any]): Arguments to forward to `optim`. 2024-12-18T01:36:56.9361944Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2024-12-18T01:36:56.9362507Z to optimize, similar to `params` argument of traditional `torch.optim` 2024-12-18T01:36:56.9363059Z Optimizers. If this is omitted, all DDP model parameters will be 2024-12-18T01:36:56.9363480Z optimized. 2024-12-18T01:36:56.9363834Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2024-12-18T01:36:56.9364234Z 2024-12-18T01:36:56.9364456Z .. warning :: 2024-12-18T01:36:56.9364825Z _register_fused_optim should only be called once on a DDP instance, 2024-12-18T01:36:56.9365371Z and registering multiple fused optimizers for the same DDP model 2024-12-18T01:36:56.9365834Z is not currently supported. Please ping 2024-12-18T01:36:56.9366306Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:36:56.9366754Z for your use case. 2024-12-18T01:36:56.9367028Z 2024-12-18T01:36:56.9367244Z .. warning :: 2024-12-18T01:36:56.9367599Z _register_fused_optim and register_comm_hook currently do not 2024-12-18T01:36:56.9368164Z compose together, meaning that custom DDP communication hooks are 2024-12-18T01:36:56.9368679Z not supported with overlapped optimizers. Please ping 2024-12-18T01:36:56.9369206Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:36:56.9369661Z for your use case. 2024-12-18T01:36:56.9369933Z 2024-12-18T01:36:56.9370139Z .. warning :: 2024-12-18T01:36:56.9370519Z Gradient accumulation and DDP `no_sync` are currently not supported 2024-12-18T01:36:56.9370995Z with overlapped optimizer. Please ping 2024-12-18T01:36:56.9371465Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2024-12-18T01:36:56.9371913Z for your use case. 2024-12-18T01:36:56.9372168Z 2024-12-18T01:36:56.9372418Z Example:: 2024-12-18T01:36:56.9372646Z 2024-12-18T01:36:56.9372905Z >>> # xdoctest: +SKIP("No rendezvous handler") 2024-12-18T01:36:56.9373457Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2024-12-18T01:36:56.9374074Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2024-12-18T01:36:56.9374497Z >>> lr = 1e-2 2024-12-18T01:36:56.9374763Z >>> betas = (0.9, 0.99) 2024-12-18T01:36:56.9375052Z >>> eps = 1e-6 2024-12-18T01:36:56.9375471Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2024-12-18T01:36:56.9375945Z >>> # Example with subset of parameters 2024-12-18T01:36:56.9376316Z >>> params_to_opt = [list(net.parameters())[0]] 2024-12-18T01:36:56.9376688Z >>> net._register_fused_optim( 2024-12-18T01:36:56.9377145Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2024-12-18T01:36:56.9377600Z ... ) 2024-12-18T01:36:56.9377829Z 2024-12-18T01:36:56.9378196Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9378693Z 2024-12-18T01:36:56.9378927Z warnings.warn(msg) 2024-12-18T01:36:56.9379201Z 2024-12-18T01:36:56.9379530Z --- Parse Warning: 88 / 105 --- 2024-12-18T01:36:56.9380747Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6. 2024-12-18T01:36:56.9382087Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9382692Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2024-12-18T01:36:56.9383117Z 2024-12-18T01:36:56.9383514Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:36:56.9384185Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:36:56.9384820Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:36:56.9385503Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:36:56.9386017Z 2024-12-18T01:36:56.9386233Z .. note:: 2024-12-18T01:36:56.9386622Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2024-12-18T01:36:56.9387191Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2024-12-18T01:36:56.9387753Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:36:56.9388309Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:36:56.9388883Z One place we are confident in is that NHWC(channels_last) conversion for 2024-12-18T01:36:56.9389456Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2024-12-18T01:36:56.9390006Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:36:56.9390448Z 2024-12-18T01:36:56.9390796Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:36:56.9391268Z channels_last. This ensures that; 2024-12-18T01:36:56.9391723Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:36:56.9392296Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:36:56.9392887Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:36:56.9393352Z from memory_format conversion. 2024-12-18T01:36:56.9393671Z 2024-12-18T01:36:56.9394022Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:36:56.9394604Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:36:56.9395228Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:36:56.9395904Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:36:56.9396360Z 2024-12-18T01:36:56.9396713Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:36:56.9397313Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:36:56.9398053Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:36:56.9398642Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:36:56.9399194Z another convolution layer. There's no point in propagating that 2024-12-18T01:36:56.9399758Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:36:56.9400206Z ``memory_format``. 2024-12-18T01:36:56.9400493Z 2024-12-18T01:36:56.9400916Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:36:56.9401489Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:36:56.9401958Z immediately before a convolution. 2024-12-18T01:36:56.9402287Z 2024-12-18T01:36:56.9402501Z Args: 2024-12-18T01:36:56.9403048Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2024-12-18T01:36:56.9403488Z ``nn.Module`` 2024-12-18T01:36:56.9403870Z memory_format: user specified ``memory_format``, 2024-12-18T01:36:56.9404328Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:36:56.9404722Z 2024-12-18T01:36:56.9404935Z Returns: 2024-12-18T01:36:56.9405221Z The original module with updated ``nn.Conv2d`` 2024-12-18T01:36:56.9405569Z 2024-12-18T01:36:56.9405783Z Example: 2024-12-18T01:36:56.9406073Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:56.9406490Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:36:56.9406986Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:36:56.9407450Z >>> model = nn.Sequential( 2024-12-18T01:36:56.9407789Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2024-12-18T01:36:56.9408136Z >>> # This is identical to: 2024-12-18T01:36:56.9408597Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:36:56.9409230Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2024-12-18T01:36:56.9409713Z >>> out = model(input) 2024-12-18T01:36:56.9410007Z 2024-12-18T01:36:56.9410394Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9410863Z 2024-12-18T01:36:56.9411092Z warnings.warn(msg) 2024-12-18T01:36:56.9411348Z 2024-12-18T01:36:56.9411700Z --- Parse Warning: 89 / 105 --- 2024-12-18T01:36:56.9412984Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81. 2024-12-18T01:36:56.9414326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9414923Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2024-12-18T01:36:56.9415533Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2024-12-18T01:36:56.9416185Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2024-12-18T01:36:56.9416834Z This function is used to facilitate the computation to adopt NHWC kernels, which 2024-12-18T01:36:56.9417554Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2024-12-18T01:36:56.9418064Z 2024-12-18T01:36:56.9418291Z .. note:: 2024-12-18T01:36:56.9418694Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2024-12-18T01:36:56.9419274Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2024-12-18T01:36:56.9419867Z layer with 4d weight will be affected by ``model.to``, which does not 2024-12-18T01:36:56.9420429Z necessarily benefit from conversion to specified ``memory_format``. 2024-12-18T01:36:56.9421009Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2024-12-18T01:36:56.9421592Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2024-12-18T01:36:56.9422142Z even in cases where we have to apply permutation to input tensors. 2024-12-18T01:36:56.9422548Z 2024-12-18T01:36:56.9422895Z Hence our strategy here is to convert only the weight of convolution to 2024-12-18T01:36:56.9423409Z channels_last_3d. This ensures that; 2024-12-18T01:36:56.9423865Z 1. Fast convolution kernels will be used, the benefit of which could 2024-12-18T01:36:56.9424438Z outweigh overhead of permutation (if input is not in the same format). 2024-12-18T01:36:56.9425016Z 2. No unnecessary permutations are applied on layers that do not benefit 2024-12-18T01:36:56.9425492Z from memory_format conversion. 2024-12-18T01:36:56.9425806Z 2024-12-18T01:36:56.9426155Z The optimal case is that, layers between convolution layers are channels 2024-12-18T01:36:56.9426741Z last compatible. Input tensor would be permuted to channels last when it 2024-12-18T01:36:56.9427330Z encounters the first convolution layer and stay in that memory format. 2024-12-18T01:36:56.9427907Z Hence following convolutions will not need to permute its input tensor. 2024-12-18T01:36:56.9428353Z 2024-12-18T01:36:56.9428700Z In case where a channels last incompatible layer is between convolution 2024-12-18T01:36:56.9429256Z layers, we need to permute the input tensor back to contiguous format 2024-12-18T01:36:56.9429814Z for that layer. The input tensor will go through the remaining layers in 2024-12-18T01:36:56.9430380Z contiguous format and be permuted to channels last when it encounters 2024-12-18T01:36:56.9430937Z another convolution layer. There's no point in propagating that 2024-12-18T01:36:56.9431486Z permutation to an earlier layer, as most layers are quite agnostic to 2024-12-18T01:36:56.9431925Z ``memory_format``. 2024-12-18T01:36:56.9432200Z 2024-12-18T01:36:56.9432551Z This claim might change when PyTorch supports fusion of permutation, as 2024-12-18T01:36:56.9433119Z there might have been a better spot to fuse the permutation other than 2024-12-18T01:36:56.9433585Z immediately before a convolution. 2024-12-18T01:36:56.9433939Z 2024-12-18T01:36:56.9434148Z Args: 2024-12-18T01:36:56.9434507Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2024-12-18T01:36:56.9434937Z ``nn.Module`` 2024-12-18T01:36:56.9435097Z memory_format: user specified ``memory_format``, 2024-12-18T01:36:56.9435284Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2024-12-18T01:36:56.9435383Z 2024-12-18T01:36:56.9435474Z Returns: 2024-12-18T01:36:56.9435702Z The original module with updated ``nn.Conv3d`` 2024-12-18T01:36:56.9435822Z 2024-12-18T01:36:56.9435916Z Example: 2024-12-18T01:36:56.9436070Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2024-12-18T01:36:56.9436229Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2024-12-18T01:36:56.9436533Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2024-12-18T01:36:56.9436645Z >>> model = nn.Sequential( 2024-12-18T01:36:56.9436766Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2024-12-18T01:36:56.9436894Z >>> # This is identical to: 2024-12-18T01:36:56.9437145Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:36:56.9437458Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2024-12-18T01:36:56.9437563Z >>> out = model(input) 2024-12-18T01:36:56.9437668Z 2024-12-18T01:36:56.9437925Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9438012Z 2024-12-18T01:36:56.9438129Z warnings.warn(msg) 2024-12-18T01:36:56.9438214Z 2024-12-18T01:36:56.9438445Z --- Parse Warning: 90 / 105 --- 2024-12-18T01:36:56.9439344Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=936. 2024-12-18T01:36:56.9439626Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9439860Z Prune tensor by removing random channels along the specified dimension. 2024-12-18T01:36:56.9439952Z 2024-12-18T01:36:56.9440200Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:36:56.9440419Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:36:56.9440577Z along the specified ``dim`` selected at random. 2024-12-18T01:36:56.9440771Z Modifies module in place (and also return the modified module) 2024-12-18T01:36:56.9440860Z by: 2024-12-18T01:36:56.9440957Z 2024-12-18T01:36:56.9441166Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:56.9441404Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:56.9441617Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:56.9441842Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:56.9441942Z ``name+'_orig'``. 2024-12-18T01:36:56.9442033Z 2024-12-18T01:36:56.9442136Z Args: 2024-12-18T01:36:56.9442315Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:36:56.9442511Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:36:56.9442609Z will act. 2024-12-18T01:36:56.9442794Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:36:56.9442968Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:36:56.9443173Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:36:56.9443322Z absolute number of parameters to prune. 2024-12-18T01:36:56.9443546Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:36:56.9443645Z 2024-12-18T01:36:56.9443737Z Returns: 2024-12-18T01:36:56.9443956Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:36:56.9444053Z 2024-12-18T01:36:56.9444150Z Examples: 2024-12-18T01:36:56.9444264Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9444386Z >>> m = prune.random_structured( 2024-12-18T01:36:56.9444539Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2024-12-18T01:36:56.9444628Z ... ) 2024-12-18T01:36:56.9444814Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2024-12-18T01:36:56.9444939Z >>> print(columns_pruned) 2024-12-18T01:36:56.9445054Z 3 2024-12-18T01:36:56.9445151Z 2024-12-18T01:36:56.9445406Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9445497Z 2024-12-18T01:36:56.9445611Z warnings.warn(msg) 2024-12-18T01:36:56.9445697Z 2024-12-18T01:36:56.9445901Z --- Parse Warning: 91 / 105 --- 2024-12-18T01:36:56.9446776Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=977. 2024-12-18T01:36:56.9447050Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9447352Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2024-12-18T01:36:56.9447440Z 2024-12-18T01:36:56.9447686Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2024-12-18T01:36:56.9447905Z by removing the specified ``amount`` of (currently unpruned) channels 2024-12-18T01:36:56.9448119Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2024-12-18T01:36:56.9448321Z Modifies module in place (and also return the modified module) 2024-12-18T01:36:56.9448422Z by: 2024-12-18T01:36:56.9448510Z 2024-12-18T01:36:56.9448719Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:56.9448949Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:56.9449157Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:56.9449371Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:56.9449469Z ``name+'_orig'``. 2024-12-18T01:36:56.9449570Z 2024-12-18T01:36:56.9449662Z Args: 2024-12-18T01:36:56.9449842Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:36:56.9450038Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:36:56.9450138Z will act. 2024-12-18T01:36:56.9450325Z amount (int or float): quantity of parameters to prune. 2024-12-18T01:36:56.9450505Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:36:56.9450717Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:36:56.9450854Z absolute number of parameters to prune. 2024-12-18T01:36:56.9451047Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2024-12-18T01:36:56.9451214Z entries for argument ``p`` in :func:`torch.norm`. 2024-12-18T01:36:56.9451410Z dim (int): index of the dim along which we define channels to prune. 2024-12-18T01:36:56.9451654Z importance_scores (torch.Tensor): tensor of importance scores (of same 2024-12-18T01:36:56.9451848Z shape as module parameter) used to compute mask for pruning. 2024-12-18T01:36:56.9452081Z The values in this tensor indicate the importance of the corresponding 2024-12-18T01:36:56.9452239Z elements in the parameter being pruned. 2024-12-18T01:36:56.9452466Z If unspecified or None, the module parameter will be used in its place. 2024-12-18T01:36:56.9452566Z 2024-12-18T01:36:56.9452659Z Returns: 2024-12-18T01:36:56.9452891Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:36:56.9452978Z 2024-12-18T01:36:56.9453072Z Examples: 2024-12-18T01:36:56.9453208Z >>> from torch.nn.utils import prune 2024-12-18T01:36:56.9453321Z >>> m = prune.ln_structured( 2024-12-18T01:36:56.9453518Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2024-12-18T01:36:56.9453607Z ... ) 2024-12-18T01:36:56.9453708Z 2024-12-18T01:36:56.9453988Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9454078Z 2024-12-18T01:36:56.9454195Z warnings.warn(msg) 2024-12-18T01:36:56.9454284Z 2024-12-18T01:36:56.9454490Z --- Parse Warning: 92 / 105 --- 2024-12-18T01:36:56.9455397Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1024. 2024-12-18T01:36:56.9455668Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9455754Z 2024-12-18T01:36:56.9456171Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2024-12-18T01:36:56.9456271Z 2024-12-18T01:36:56.9456387Z Modifies modules in place by: 2024-12-18T01:36:56.9456494Z 2024-12-18T01:36:56.9456706Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:56.9456952Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:56.9457176Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:56.9457384Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:56.9457495Z ``name+'_orig'``. 2024-12-18T01:36:56.9457584Z 2024-12-18T01:36:56.9457683Z Args: 2024-12-18T01:36:56.9457887Z parameters (Iterable of (module, name) tuples): parameters of 2024-12-18T01:36:56.9458083Z the model to prune in a global fashion, i.e. by aggregating all 2024-12-18T01:36:56.9458302Z weights prior to deciding which ones to prune. module must be of 2024-12-18T01:36:56.9458459Z type :class:`nn.Module`, and name must be a string. 2024-12-18T01:36:56.9458692Z pruning_method (function): a valid pruning function from this module, 2024-12-18T01:36:56.9458876Z or a custom one implemented by the user that satisfies the 2024-12-18T01:36:56.9459117Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2024-12-18T01:36:56.9459347Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2024-12-18T01:36:56.9459574Z the corresponding parameter's importance scores tensor. The tensor 2024-12-18T01:36:56.9459796Z should be the same shape as the parameter, and is used for computing 2024-12-18T01:36:56.9459901Z mask for pruning. 2024-12-18T01:36:56.9460125Z If unspecified or None, the parameter will be used in place of its 2024-12-18T01:36:56.9460227Z importance scores. 2024-12-18T01:36:56.9460371Z kwargs: other keyword arguments such as: 2024-12-18T01:36:56.9460567Z amount (int or float): quantity of parameters to prune across the 2024-12-18T01:36:56.9460675Z specified parameters. 2024-12-18T01:36:56.9460863Z If ``float``, should be between 0.0 and 1.0 and represent the 2024-12-18T01:36:56.9461066Z fraction of parameters to prune. If ``int``, it represents the 2024-12-18T01:36:56.9461300Z absolute number of parameters to prune. 2024-12-18T01:36:56.9461389Z 2024-12-18T01:36:56.9461480Z Raises: 2024-12-18T01:36:56.9461648Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2024-12-18T01:36:56.9461735Z 2024-12-18T01:36:56.9461833Z Note: 2024-12-18T01:36:56.9462048Z Since global structured pruning doesn't make much sense unless the 2024-12-18T01:36:56.9462255Z norm is normalized by the size of the parameter, we now limit the 2024-12-18T01:36:56.9462406Z scope of global pruning to unstructured methods. 2024-12-18T01:36:56.9462494Z 2024-12-18T01:36:56.9462599Z Examples: 2024-12-18T01:36:56.9462720Z >>> from torch.nn.utils import prune 2024-12-18T01:36:56.9475247Z >>> from collections import OrderedDict 2024-12-18T01:36:56.9475564Z >>> net = nn.Sequential(OrderedDict([ 2024-12-18T01:36:56.9475804Z ... ('first', nn.Linear(10, 4)), 2024-12-18T01:36:56.9475922Z ... ('second', nn.Linear(4, 1)), 2024-12-18T01:36:56.9476026Z ... ])) 2024-12-18T01:36:56.9476137Z >>> parameters_to_prune = ( 2024-12-18T01:36:56.9476255Z ... (net.first, 'weight'), 2024-12-18T01:36:56.9476360Z ... (net.second, 'weight'), 2024-12-18T01:36:56.9476449Z ... ) 2024-12-18T01:36:56.9476621Z >>> prune.global_unstructured( 2024-12-18T01:36:56.9476732Z ... parameters_to_prune, 2024-12-18T01:36:56.9476889Z ... pruning_method=prune.L1Unstructured, 2024-12-18T01:36:56.9476986Z ... amount=10, 2024-12-18T01:36:56.9477075Z ... ) 2024-12-18T01:36:56.9477319Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2024-12-18T01:36:56.9477412Z tensor(10) 2024-12-18T01:36:56.9477509Z 2024-12-18T01:36:56.9477593Z 2024-12-18T01:36:56.9477847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9477976Z 2024-12-18T01:36:56.9478083Z warnings.warn(msg) 2024-12-18T01:36:56.9478180Z 2024-12-18T01:36:56.9478424Z --- Parse Warning: 93 / 105 --- 2024-12-18T01:36:56.9479311Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1143. 2024-12-18T01:36:56.9479570Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9479960Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2024-12-18T01:36:56.9480059Z 2024-12-18T01:36:56.9480272Z Modifies module in place (and also return the modified module) by: 2024-12-18T01:36:56.9480377Z 2024-12-18T01:36:56.9480590Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2024-12-18T01:36:56.9480826Z binary mask applied to the parameter ``name`` by the pruning method. 2024-12-18T01:36:56.9481039Z 2) replacing the parameter ``name`` by its pruned version, while the 2024-12-18T01:36:56.9481247Z original (unpruned) parameter is stored in a new parameter named 2024-12-18T01:36:56.9481359Z ``name+'_orig'``. 2024-12-18T01:36:56.9481448Z 2024-12-18T01:36:56.9481552Z Args: 2024-12-18T01:36:56.9481731Z module (nn.Module): module containing the tensor to prune 2024-12-18T01:36:56.9481932Z name (str): parameter name within ``module`` on which pruning 2024-12-18T01:36:56.9482027Z will act. 2024-12-18T01:36:56.9482207Z mask (Tensor): binary mask to be applied to the parameter. 2024-12-18T01:36:56.9482314Z 2024-12-18T01:36:56.9482407Z Returns: 2024-12-18T01:36:56.9482639Z module (nn.Module): modified (i.e. pruned) version of the input module 2024-12-18T01:36:56.9482722Z 2024-12-18T01:36:56.9482817Z Examples: 2024-12-18T01:36:56.9483006Z >>> from torch.nn.utils import prune 2024-12-18T01:36:56.9483124Z >>> m = prune.custom_from_mask( 2024-12-18T01:36:56.9483317Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2024-12-18T01:36:56.9483404Z ... ) 2024-12-18T01:36:56.9483525Z >>> print(m.bias_mask) 2024-12-18T01:36:56.9483621Z tensor([0., 1., 0.]) 2024-12-18T01:36:56.9483709Z 2024-12-18T01:36:56.9483810Z 2024-12-18T01:36:56.9484061Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9484160Z 2024-12-18T01:36:56.9484262Z warnings.warn(msg) 2024-12-18T01:36:56.9484348Z 2024-12-18T01:36:56.9484554Z --- Parse Warning: 94 / 105 --- 2024-12-18T01:36:56.9485444Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=116. 2024-12-18T01:36:56.9485722Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9486076Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2024-12-18T01:36:56.9486176Z 2024-12-18T01:36:56.9486444Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2024-12-18T01:36:56.9486660Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2024-12-18T01:36:56.9486887Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2024-12-18T01:36:56.9486978Z (UAI 2018). 2024-12-18T01:36:56.9487076Z 2024-12-18T01:36:56.9487288Z Exponential Moving Average is a variation of `Polyak averaging`_, 2024-12-18T01:36:56.9487544Z but using exponential weights instead of equal weights across iterations. 2024-12-18T01:36:56.9487631Z 2024-12-18T01:36:56.9487889Z AveragedModel class creates a copy of the provided module :attr:`model` 2024-12-18T01:36:56.9488126Z on the device :attr:`device` and allows to compute running averages of the 2024-12-18T01:36:56.9488241Z parameters of the :attr:`model`. 2024-12-18T01:36:56.9488341Z 2024-12-18T01:36:56.9488428Z Args: 2024-12-18T01:36:56.9488588Z model (torch.nn.Module): model to use with SWA/EMA 2024-12-18T01:36:56.9488835Z device (torch.device, optional): if provided, the averaged model will be 2024-12-18T01:36:56.9488949Z stored on the :attr:`device` 2024-12-18T01:36:56.9489167Z avg_fn (function, optional): the averaging function used to update 2024-12-18T01:36:56.9489365Z parameters; the function must take in the current value of the 2024-12-18T01:36:56.9489600Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2024-12-18T01:36:56.9489798Z parameter, and the number of models already averaged; if None, 2024-12-18T01:36:56.9489960Z an equally weighted average is used (default: None) 2024-12-18T01:36:56.9490197Z multi_avg_fn (function, optional): the averaging function used to update 2024-12-18T01:36:56.9490428Z parameters inplace; the function must take in the current values of the 2024-12-18T01:36:56.9490710Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2024-12-18T01:36:56.9490937Z parameters as a list, and the number of models already averaged; if None, 2024-12-18T01:36:56.9491109Z an equally weighted average is used (default: None) 2024-12-18T01:36:56.9491310Z use_buffers (bool): if ``True``, it will compute running averages for 2024-12-18T01:36:56.9491544Z both the parameters and the buffers of the model. (default: ``False``) 2024-12-18T01:36:56.9491635Z 2024-12-18T01:36:56.9491726Z Example: 2024-12-18T01:36:56.9491877Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.9492036Z >>> loader, optimizer, model, loss_fn = ... 2024-12-18T01:36:56.9492222Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2024-12-18T01:36:56.9492445Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2024-12-18T01:36:56.9492564Z >>> T_max=300) 2024-12-18T01:36:56.9492678Z >>> swa_start = 160 2024-12-18T01:36:56.9492824Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2024-12-18T01:36:56.9492940Z >>> for i in range(300): 2024-12-18T01:36:56.9493057Z >>> for input, target in loader: 2024-12-18T01:36:56.9493187Z >>> optimizer.zero_grad() 2024-12-18T01:36:56.9493353Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:36:56.9493465Z >>> optimizer.step() 2024-12-18T01:36:56.9493585Z >>> if i > swa_start: 2024-12-18T01:36:56.9493719Z >>> swa_model.update_parameters(model) 2024-12-18T01:36:56.9493848Z >>> swa_scheduler.step() 2024-12-18T01:36:56.9493941Z >>> else: 2024-12-18T01:36:56.9494062Z >>> scheduler.step() 2024-12-18T01:36:56.9494179Z >>> 2024-12-18T01:36:56.9494335Z >>> # Update bn statistics for the swa_model at the end 2024-12-18T01:36:56.9494514Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2024-12-18T01:36:56.9494600Z 2024-12-18T01:36:56.9494915Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2024-12-18T01:36:56.9495111Z If no averaging function is provided, the default is to compute 2024-12-18T01:36:56.9495263Z equally-weighted average of the weights (SWA). 2024-12-18T01:36:56.9495362Z 2024-12-18T01:36:56.9495453Z Example: 2024-12-18T01:36:56.9495624Z >>> # xdoctest: +SKIP("undefined variables") 2024-12-18T01:36:56.9495832Z >>> # Compute exponential moving averages of the weights and buffers 2024-12-18T01:36:56.9496018Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2024-12-18T01:36:56.9496240Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2024-12-18T01:36:56.9496328Z 2024-12-18T01:36:56.9496448Z .. note:: 2024-12-18T01:36:56.9496669Z When using SWA/EMA with models containing Batch Normalization you may 2024-12-18T01:36:56.9496888Z need to update the activation statistics for Batch Normalization. 2024-12-18T01:36:56.9497119Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2024-12-18T01:36:56.9497354Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2024-12-18T01:36:56.9497597Z statistics in a post-training step by passing data through the model. The 2024-12-18T01:36:56.9497833Z second does it during the parameter update phase by averaging all buffers. 2024-12-18T01:36:56.9498282Z Empirical evidence has shown that updating the statistics in normalization 2024-12-18T01:36:56.9498510Z layers increases accuracy, but you may wish to empirically test which 2024-12-18T01:36:56.9498682Z approach yields the best results in your problem. 2024-12-18T01:36:56.9498771Z 2024-12-18T01:36:56.9498877Z .. note:: 2024-12-18T01:36:56.9499136Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2024-12-18T01:36:56.9499221Z 2024-12-18T01:36:56.9499326Z .. note:: 2024-12-18T01:36:56.9499527Z When :meth:`update_parameters` is called for the first time (i.e. 2024-12-18T01:36:56.9499727Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2024-12-18T01:36:56.9499934Z to the parameters of :class:`AveragedModel`. For every subsequent 2024-12-18T01:36:56.9500198Z call of :meth:`update_parameters` the function `avg_fn` is used 2024-12-18T01:36:56.9500315Z to update the parameters. 2024-12-18T01:36:56.9500402Z 2024-12-18T01:36:56.9500640Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:36:56.9500772Z https://arxiv.org/abs/1803.05407 2024-12-18T01:36:56.9501022Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2024-12-18T01:36:56.9501114Z Average: 2024-12-18T01:36:56.9501235Z https://arxiv.org/abs/1806.05594 2024-12-18T01:36:56.9501452Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2024-12-18T01:36:56.9501571Z https://arxiv.org/abs/1904.11943 2024-12-18T01:36:56.9501847Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2024-12-18T01:36:56.9501953Z Generalizes Well: 2024-12-18T01:36:56.9502086Z https://arxiv.org/abs/2001.02312 2024-12-18T01:36:56.9502191Z .. _Polyak averaging: 2024-12-18T01:36:56.9502368Z https://paperswithcode.com/method/polyak-averaging 2024-12-18T01:36:56.9502468Z 2024-12-18T01:36:56.9502944Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9503085Z 2024-12-18T01:36:56.9503194Z warnings.warn(msg) 2024-12-18T01:36:56.9503279Z 2024-12-18T01:36:56.9503519Z --- Parse Warning: 95 / 105 --- 2024-12-18T01:36:56.9504344Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=368. 2024-12-18T01:36:56.9504616Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9504835Z Anneals the learning rate in each parameter group to a fixed value. 2024-12-18T01:36:56.9504974Z 2024-12-18T01:36:56.9505210Z This learning rate scheduler is meant to be used with Stochastic Weight 2024-12-18T01:36:56.9505434Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2024-12-18T01:36:56.9505523Z 2024-12-18T01:36:56.9505614Z Args: 2024-12-18T01:36:56.9505801Z optimizer (torch.optim.Optimizer): wrapped optimizer 2024-12-18T01:36:56.9506012Z swa_lrs (float or list): the learning rate value for all param groups 2024-12-18T01:36:56.9506157Z together or separately for each group. 2024-12-18T01:36:56.9506363Z annealing_epochs (int): number of epochs in the annealing phase 2024-12-18T01:36:56.9506460Z (default: 10) 2024-12-18T01:36:56.9506689Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2024-12-18T01:36:56.9506903Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2024-12-18T01:36:56.9507018Z (default: "cos") 2024-12-18T01:36:56.9507204Z last_epoch (int): the index of the last epoch (default: -1) 2024-12-18T01:36:56.9507304Z 2024-12-18T01:36:56.9507489Z The :class:`SWALR` scheduler can be used together with other 2024-12-18T01:36:56.9507708Z schedulers to switch to a constant learning rate late in the training 2024-12-18T01:36:56.9507829Z as in the example below. 2024-12-18T01:36:56.9507917Z 2024-12-18T01:36:56.9508018Z Example: 2024-12-18T01:36:56.9508156Z >>> # xdoctest: +SKIP("Undefined variables") 2024-12-18T01:36:56.9508287Z >>> loader, optimizer, model = ... 2024-12-18T01:36:56.9508405Z >>> lr_lambda = lambda epoch: 0.9 2024-12-18T01:36:56.9508628Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2024-12-18T01:36:56.9508746Z >>> lr_lambda=lr_lambda) 2024-12-18T01:36:56.9508937Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2024-12-18T01:36:56.9509148Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2024-12-18T01:36:56.9509251Z >>> swa_start = 160 2024-12-18T01:36:56.9509369Z >>> for i in range(300): 2024-12-18T01:36:56.9509491Z >>> for input, target in loader: 2024-12-18T01:36:56.9509620Z >>> optimizer.zero_grad() 2024-12-18T01:36:56.9509759Z >>> loss_fn(model(input), target).backward() 2024-12-18T01:36:56.9509881Z >>> optimizer.step() 2024-12-18T01:36:56.9509985Z >>> if i > swa_start: 2024-12-18T01:36:56.9510099Z >>> swa_scheduler.step() 2024-12-18T01:36:56.9510203Z >>> else: 2024-12-18T01:36:56.9510313Z >>> scheduler.step() 2024-12-18T01:36:56.9510469Z 2024-12-18T01:36:56.9510694Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2024-12-18T01:36:56.9510816Z https://arxiv.org/abs/1803.05407 2024-12-18T01:36:56.9510916Z 2024-12-18T01:36:56.9511166Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9511264Z 2024-12-18T01:36:56.9511366Z warnings.warn(msg) 2024-12-18T01:36:56.9511451Z 2024-12-18T01:36:56.9511692Z --- Parse Warning: 96 / 105 --- 2024-12-18T01:36:56.9512564Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1274. 2024-12-18T01:36:56.9512839Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9512991Z Asserts that ``actual`` and ``expected`` are close. 2024-12-18T01:36:56.9513093Z 2024-12-18T01:36:56.9513455Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2024-12-18T01:36:56.9513592Z 2024-12-18T01:36:56.9513691Z .. math:: 2024-12-18T01:36:56.9513774Z 2024-12-18T01:36:56.9514142Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2024-12-18T01:36:56.9514230Z 2024-12-18T01:36:56.9514581Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2024-12-18T01:36:56.9514789Z only considered equal to each other if ``equal_nan`` is ``True``. 2024-12-18T01:36:56.9514873Z 2024-12-18T01:36:56.9515087Z In addition, they are only considered close if they have the same 2024-12-18T01:36:56.9515170Z 2024-12-18T01:36:56.9515379Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2024-12-18T01:36:56.9515514Z - ``dtype`` (if ``check_dtype`` is ``True``), 2024-12-18T01:36:56.9515782Z - ``layout`` (if ``check_layout`` is ``True``), and 2024-12-18T01:36:56.9515915Z - stride (if ``check_stride`` is ``True``). 2024-12-18T01:36:56.9516001Z 2024-12-18T01:36:56.9516309Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2024-12-18T01:36:56.9516391Z 2024-12-18T01:36:56.9516757Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2024-12-18T01:36:56.9517119Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2024-12-18T01:36:56.9517357Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2024-12-18T01:36:56.9517735Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2024-12-18T01:36:56.9517822Z 2024-12-18T01:36:56.9518112Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2024-12-18T01:36:56.9518461Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2024-12-18T01:36:56.9518610Z definition above. 2024-12-18T01:36:56.9518697Z 2024-12-18T01:36:56.9519007Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2024-12-18T01:36:56.9519378Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2024-12-18T01:36:56.9519746Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2024-12-18T01:36:56.9520115Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2024-12-18T01:36:56.9520346Z their elements are considered close according to the above definition. 2024-12-18T01:36:56.9520471Z 2024-12-18T01:36:56.9520567Z .. note:: 2024-12-18T01:36:56.9520663Z 2024-12-18T01:36:56.9520989Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2024-12-18T01:36:56.9521320Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2024-12-18T01:36:56.9521622Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2024-12-18T01:36:56.9521713Z 2024-12-18T01:36:56.9521814Z Args: 2024-12-18T01:36:56.9521927Z actual (Any): Actual input. 2024-12-18T01:36:56.9522057Z expected (Any): Expected input. 2024-12-18T01:36:56.9522409Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2024-12-18T01:36:56.9522571Z are allowed. Otherwise type equality is required. 2024-12-18T01:36:56.9522966Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2024-12-18T01:36:56.9523233Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:36:56.9523594Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2024-12-18T01:36:56.9523855Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2024-12-18T01:36:56.9524109Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2024-12-18T01:36:56.9524392Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2024-12-18T01:36:56.9524653Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2024-12-18T01:36:56.9524887Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2024-12-18T01:36:56.9525245Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2024-12-18T01:36:56.9525586Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2024-12-18T01:36:56.9525748Z :func:`torch.promote_types`) before being compared. 2024-12-18T01:36:56.9526108Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2024-12-18T01:36:56.9526437Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2024-12-18T01:36:56.9526545Z compared. 2024-12-18T01:36:56.9526898Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2024-12-18T01:36:56.9527256Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2024-12-18T01:36:56.9527608Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2024-12-18T01:36:56.9527767Z should return the new message. 2024-12-18T01:36:56.9527855Z 2024-12-18T01:36:56.9527944Z Raises: 2024-12-18T01:36:56.9528187Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2024-12-18T01:36:56.9528356Z ValueError: If only ``rtol`` or ``atol`` is specified. 2024-12-18T01:36:56.9528688Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2024-12-18T01:36:56.9529041Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2024-12-18T01:36:56.9529157Z different types. 2024-12-18T01:36:56.9529513Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2024-12-18T01:36:56.9529900Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2024-12-18T01:36:56.9530223Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2024-12-18T01:36:56.9530519Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2024-12-18T01:36:56.9530674Z :attr:`~torch.Tensor.layout`. 2024-12-18T01:36:56.9530894Z AssertionError: If only one of corresponding tensors is quantized. 2024-12-18T01:36:56.9531282Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2024-12-18T01:36:56.9531570Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2024-12-18T01:36:56.9531693Z :attr:`~torch.Tensor.device`. 2024-12-18T01:36:56.9532063Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2024-12-18T01:36:56.9532422Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2024-12-18T01:36:56.9532780Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2024-12-18T01:36:56.9532867Z 2024-12-18T01:36:56.9533232Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2024-12-18T01:36:56.9533388Z ``dtype``'s, the maximum of both tolerances is used. 2024-12-18T01:36:56.9533483Z 2024-12-18T01:36:56.9533618Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9533756Z | ``dtype`` | ``rtol`` | ``atol`` | 2024-12-18T01:36:56.9533870Z +===========================+============+==========+ 2024-12-18T01:36:56.9534012Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:36:56.9534152Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9534293Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2024-12-18T01:36:56.9534426Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9534565Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:56.9534701Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9534840Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:36:56.9534966Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9535112Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2024-12-18T01:36:56.9535236Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9535388Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:56.9535514Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9535658Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2024-12-18T01:36:56.9535819Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9535955Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:56.9536090Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9536231Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:56.9536366Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9536503Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:56.9536626Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9536769Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:56.9536895Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9537063Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2024-12-18T01:36:56.9537189Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9537331Z | other | ``0.0`` | ``0.0`` | 2024-12-18T01:36:56.9537454Z +---------------------------+------------+----------+ 2024-12-18T01:36:56.9537540Z 2024-12-18T01:36:56.9537641Z .. note:: 2024-12-18T01:36:56.9537724Z 2024-12-18T01:36:56.9538135Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2024-12-18T01:36:56.9538482Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2024-12-18T01:36:56.9538751Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2024-12-18T01:36:56.9538836Z 2024-12-18T01:36:56.9538935Z >>> import functools 2024-12-18T01:36:56.9539206Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2024-12-18T01:36:56.9539318Z >>> assert_equal(1e-9, 1e-10) 2024-12-18T01:36:56.9539474Z Traceback (most recent call last): 2024-12-18T01:36:56.9539562Z ... 2024-12-18T01:36:56.9539699Z AssertionError: Scalars are not equal! 2024-12-18T01:36:56.9539790Z 2024-12-18T01:36:56.9539903Z Expected 1e-10 but got 1e-09. 2024-12-18T01:36:56.9540046Z Absolute difference: 9.000000000000001e-10 2024-12-18T01:36:56.9540151Z Relative difference: 9.0 2024-12-18T01:36:56.9540247Z 2024-12-18T01:36:56.9540334Z Examples: 2024-12-18T01:36:56.9540448Z >>> # tensor to tensor comparison 2024-12-18T01:36:56.9540595Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2024-12-18T01:36:56.9540726Z >>> actual = torch.acos(torch.cos(expected)) 2024-12-18T01:36:56.9540890Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:56.9540975Z 2024-12-18T01:36:56.9541100Z >>> # scalar to scalar comparison 2024-12-18T01:36:56.9541198Z >>> import math 2024-12-18T01:36:56.9541306Z >>> expected = math.sqrt(2.0) 2024-12-18T01:36:56.9541428Z >>> actual = 2.0 / math.sqrt(2.0) 2024-12-18T01:36:56.9541572Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:56.9541665Z 2024-12-18T01:36:56.9541795Z >>> # numpy array to numpy array comparison 2024-12-18T01:36:56.9541895Z >>> import numpy as np 2024-12-18T01:36:56.9542031Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2024-12-18T01:36:56.9542153Z >>> actual = np.arccos(np.cos(expected)) 2024-12-18T01:36:56.9542310Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:56.9542396Z 2024-12-18T01:36:56.9542525Z >>> # sequence to sequence comparison 2024-12-18T01:36:56.9542630Z >>> import numpy as np 2024-12-18T01:36:56.9542880Z >>> # The types of the sequences do not have to match. They only have to have the same 2024-12-18T01:36:56.9543021Z >>> # length and their elements have to match. 2024-12-18T01:36:56.9543205Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2024-12-18T01:36:56.9543322Z >>> actual = tuple(expected) 2024-12-18T01:36:56.9543472Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:56.9543552Z 2024-12-18T01:36:56.9543685Z >>> # mapping to mapping comparison 2024-12-18T01:36:56.9543811Z >>> from collections import OrderedDict 2024-12-18T01:36:56.9543919Z >>> import numpy as np 2024-12-18T01:36:56.9544025Z >>> foo = torch.tensor(1.0) 2024-12-18T01:36:56.9544123Z >>> bar = 2.0 2024-12-18T01:36:56.9544224Z >>> baz = np.array(3.0) 2024-12-18T01:36:56.9544474Z >>> # The types and a possible ordering of mappings do not have to match. They only 2024-12-18T01:36:56.9544717Z >>> # have to have the same set of keys and their elements have to match. 2024-12-18T01:36:56.9544923Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2024-12-18T01:36:56.9545070Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2024-12-18T01:36:56.9545218Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:56.9545311Z 2024-12-18T01:36:56.9545480Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:36:56.9545595Z >>> actual = expected.clone() 2024-12-18T01:36:56.9545772Z >>> # By default, directly related instances can be compared 2024-12-18T01:36:56.9545988Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2024-12-18T01:36:56.9546188Z >>> # This check can be made more strict with allow_subclasses=False 2024-12-18T01:36:56.9546307Z >>> torch.testing.assert_close( 2024-12-18T01:36:56.9546521Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2024-12-18T01:36:56.9546634Z ... ) 2024-12-18T01:36:56.9546760Z Traceback (most recent call last): 2024-12-18T01:36:56.9546857Z ... 2024-12-18T01:36:56.9547064Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:36:56.9547290Z and . 2024-12-18T01:36:56.9547516Z >>> # If the inputs are not directly related, they are never considered close 2024-12-18T01:36:56.9547691Z >>> torch.testing.assert_close(actual.numpy(), expected) 2024-12-18T01:36:56.9547818Z Traceback (most recent call last): 2024-12-18T01:36:56.9547908Z ... 2024-12-18T01:36:56.9548202Z TypeError: No comparison pair was able to handle inputs of type 2024-12-18T01:36:56.9548313Z and . 2024-12-18T01:36:56.9548583Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2024-12-18T01:36:56.9548706Z >>> # their type if check_dtype=False. 2024-12-18T01:36:56.9548880Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2024-12-18T01:36:56.9548973Z 2024-12-18T01:36:56.9549076Z >>> # NaN != NaN by default. 2024-12-18T01:36:56.9549212Z >>> expected = torch.tensor(float("Nan")) 2024-12-18T01:36:56.9549325Z >>> actual = expected.clone() 2024-12-18T01:36:56.9549484Z >>> torch.testing.assert_close(actual, expected) 2024-12-18T01:36:56.9549605Z Traceback (most recent call last): 2024-12-18T01:36:56.9549695Z ... 2024-12-18T01:36:56.9549829Z AssertionError: Scalars are not close! 2024-12-18T01:36:56.9549922Z 2024-12-18T01:36:56.9550039Z Expected nan but got nan. 2024-12-18T01:36:56.9550182Z Absolute difference: nan (up to 1e-05 allowed) 2024-12-18T01:36:56.9550332Z Relative difference: nan (up to 1.3e-06 allowed) 2024-12-18T01:36:56.9550545Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2024-12-18T01:36:56.9550668Z 2024-12-18T01:36:56.9550804Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2024-12-18T01:36:56.9550925Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2024-12-18T01:36:56.9551085Z >>> # The default error message can be overwritten. 2024-12-18T01:36:56.9551368Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2024-12-18T01:36:56.9551487Z Traceback (most recent call last): 2024-12-18T01:36:56.9551580Z ... 2024-12-18T01:36:56.9551733Z AssertionError: Argh, the tensors are not close! 2024-12-18T01:36:56.9551966Z >>> # If msg is a callable, it can be used to augment the generated message with 2024-12-18T01:36:56.9552095Z >>> # extra information 2024-12-18T01:36:56.9552225Z >>> torch.testing.assert_close( 2024-12-18T01:36:56.9552422Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2024-12-18T01:36:56.9552510Z ... ) 2024-12-18T01:36:56.9552640Z Traceback (most recent call last): 2024-12-18T01:36:56.9552727Z ... 2024-12-18T01:36:56.9552847Z AssertionError: Header 2024-12-18T01:36:56.9552938Z 2024-12-18T01:36:56.9553079Z Tensor-likes are not close! 2024-12-18T01:36:56.9553185Z 2024-12-18T01:36:56.9553299Z Mismatched elements: 2 / 3 (66.7%) 2024-12-18T01:36:56.9553534Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2024-12-18T01:36:56.9553757Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2024-12-18T01:36:56.9553861Z 2024-12-18T01:36:56.9553952Z Footer 2024-12-18T01:36:56.9554038Z 2024-12-18T01:36:56.9554297Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9554406Z 2024-12-18T01:36:56.9554519Z warnings.warn(msg) 2024-12-18T01:36:56.9554601Z 2024-12-18T01:36:56.9554820Z --- Parse Warning: 97 / 105 --- 2024-12-18T01:36:56.9555847Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=110. 2024-12-18T01:36:56.9556110Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9556264Z Register a container-like type as pytree node. 2024-12-18T01:36:56.9556345Z 2024-12-18T01:36:56.9556441Z Args: 2024-12-18T01:36:56.9556630Z cls (type): A Python type to treat as an internal pytree node. 2024-12-18T01:36:56.9556898Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2024-12-18T01:36:56.9557155Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2024-12-18T01:36:56.9557444Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2024-12-18T01:36:56.9557573Z passed to the ``unflatten_fn``. 2024-12-18T01:36:56.9557844Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2024-12-18T01:36:56.9558110Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2024-12-18T01:36:56.9558267Z The function should return an instance of ``cls``. 2024-12-18T01:36:56.9558532Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2024-12-18T01:36:56.9558699Z qualified name used when serializing the tree spec. 2024-12-18T01:36:56.9559003Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2024-12-18T01:36:56.9559287Z to convert the context of the pytree to a custom json dumpable representation. This is 2024-12-18T01:36:56.9559594Z used for json serialization, which is being used in :mod:`torch.export` right now. 2024-12-18T01:36:56.9559894Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2024-12-18T01:36:56.9560156Z how to convert the custom json dumpable representation of the context back to the 2024-12-18T01:36:56.9560416Z original context. This is used for json deserialization, which is being used in 2024-12-18T01:36:56.9560536Z :mod:`torch.export` right now. 2024-12-18T01:36:56.9560624Z 2024-12-18T01:36:56.9560738Z Example:: 2024-12-18T01:36:56.9560824Z 2024-12-18T01:36:56.9560935Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9561114Z >>> # Registry a Python type with lambda functions 2024-12-18T01:36:56.9561231Z >>> register_pytree_node( 2024-12-18T01:36:56.9561326Z ... set, 2024-12-18T01:36:56.9561451Z ... lambda s: (sorted(s), None, None), 2024-12-18T01:36:56.9561587Z ... lambda children, _: set(children), 2024-12-18T01:36:56.9561676Z ... ) 2024-12-18T01:36:56.9561777Z 2024-12-18T01:36:56.9562057Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9562144Z 2024-12-18T01:36:56.9562248Z warnings.warn(msg) 2024-12-18T01:36:56.9562330Z 2024-12-18T01:36:56.9562542Z --- Parse Warning: 98 / 105 --- 2024-12-18T01:36:56.9563477Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1201. 2024-12-18T01:36:56.9563749Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9563862Z 2024-12-18T01:36:56.9564081Z Context passed to policy function during selective checkpointing. 2024-12-18T01:36:56.9564177Z 2024-12-18T01:36:56.9564403Z This class is used to pass relevant metadata to the policy function during 2024-12-18T01:36:56.9564675Z selective checkpointing. The metadata includes whether the current invocation 2024-12-18T01:36:56.9564842Z of the policy function is during recomputation or not. 2024-12-18T01:36:56.9564939Z 2024-12-18T01:36:56.9565027Z Example: 2024-12-18T01:36:56.9565133Z >>> # xdoctest: +SKIP(stub) 2024-12-18T01:36:56.9565232Z >>> 2024-12-18T01:36:56.9565363Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:36:56.9565485Z >>> print(ctx.is_recompute) 2024-12-18T01:36:56.9565569Z >>> 2024-12-18T01:36:56.9565841Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:36:56.9565944Z >>> 2024-12-18T01:36:56.9566097Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:36:56.9566204Z >>> fn, x, y, 2024-12-18T01:36:56.9566304Z >>> use_reentrant=False, 2024-12-18T01:36:56.9566411Z >>> context_fn=context_fn, 2024-12-18T01:36:56.9566507Z >>> ) 2024-12-18T01:36:56.9566590Z 2024-12-18T01:36:56.9566853Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9566938Z 2024-12-18T01:36:56.9567048Z warnings.warn(msg) 2024-12-18T01:36:56.9567131Z 2024-12-18T01:36:56.9567321Z --- Parse Warning: 99 / 105 --- 2024-12-18T01:36:56.9568291Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1335. 2024-12-18T01:36:56.9568551Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9568650Z 2024-12-18T01:36:56.9568910Z Helper to avoid recomputing certain ops during activation checkpointing. 2024-12-18T01:36:56.9569005Z 2024-12-18T01:36:56.9569221Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2024-12-18T01:36:56.9569381Z operations are recomputed during the backward pass. 2024-12-18T01:36:56.9569479Z 2024-12-18T01:36:56.9569565Z Args: 2024-12-18T01:36:56.9569694Z policy_fn_or_list (Callable or List): 2024-12-18T01:36:56.9569859Z - If a policy function is provided, it should accept a 2024-12-18T01:36:56.9570097Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2024-12-18T01:36:56.9570312Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2024-12-18T01:36:56.9570546Z indicating whether the execution of the op should be recomputed or not. 2024-12-18T01:36:56.9570783Z - If a list of operations is provided, it is equivalent to a policy 2024-12-18T01:36:56.9570967Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2024-12-18T01:36:56.9571192Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2024-12-18T01:36:56.9571289Z operations. 2024-12-18T01:36:56.9571500Z allow_cache_entry_mutation (bool, optional): By default, an error is 2024-12-18T01:36:56.9571748Z raised if any tensors cached by selective activation checkpoint are 2024-12-18T01:36:56.9571949Z mutated in order to ensure correctness. If set to `True`, this check 2024-12-18T01:36:56.9572052Z is disabled. 2024-12-18T01:36:56.9572143Z Returns: 2024-12-18T01:36:56.9572267Z A tuple of two context managers. 2024-12-18T01:36:56.9572354Z 2024-12-18T01:36:56.9572439Z Example: 2024-12-18T01:36:56.9572562Z >>> # xdoctest: +REQUIRES(LINUX) 2024-12-18T01:36:56.9572659Z >>> import functools 2024-12-18T01:36:56.9572752Z >>> 2024-12-18T01:36:56.9572908Z >>> x = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:36:56.9573034Z >>> y = torch.rand(10, 10, requires_grad=True) 2024-12-18T01:36:56.9573128Z >>> 2024-12-18T01:36:56.9573221Z >>> ops_to_save = [ 2024-12-18T01:36:56.9573346Z >>> torch.ops.aten.mm.default, 2024-12-18T01:36:56.9573434Z >>> ] 2024-12-18T01:36:56.9573520Z >>> 2024-12-18T01:36:56.9573657Z >>> def policy_fn(ctx, op, *args, **kwargs): 2024-12-18T01:36:56.9573756Z >>> if op in ops_to_save: 2024-12-18T01:36:56.9573897Z >>> return CheckpointPolicy.MUST_SAVE 2024-12-18T01:36:56.9573985Z >>> else: 2024-12-18T01:36:56.9574143Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2024-12-18T01:36:56.9574229Z >>> 2024-12-18T01:36:56.9574495Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2024-12-18T01:36:56.9574592Z >>> 2024-12-18T01:36:56.9574693Z >>> # or equivalently 2024-12-18T01:36:56.9574974Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2024-12-18T01:36:56.9575064Z >>> 2024-12-18T01:36:56.9575157Z >>> def fn(x, y): 2024-12-18T01:36:56.9575367Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2024-12-18T01:36:56.9575450Z >>> 2024-12-18T01:36:56.9575608Z >>> out = torch.utils.checkpoint.checkpoint( 2024-12-18T01:36:56.9575699Z >>> fn, x, y, 2024-12-18T01:36:56.9575810Z >>> use_reentrant=False, 2024-12-18T01:36:56.9575916Z >>> context_fn=context_fn, 2024-12-18T01:36:56.9575999Z >>> ) 2024-12-18T01:36:56.9576097Z 2024-12-18T01:36:56.9576348Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9576441Z 2024-12-18T01:36:56.9576542Z warnings.warn(msg) 2024-12-18T01:36:56.9576623Z 2024-12-18T01:36:56.9576821Z --- Parse Warning: 100 / 105 --- 2024-12-18T01:36:56.9577721Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=957. 2024-12-18T01:36:56.9577990Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9578078Z 2024-12-18T01:36:56.9578234Z Create a :class:`setuptools.Extension` for C++. 2024-12-18T01:36:56.9578318Z 2024-12-18T01:36:56.9578558Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:36:56.9578787Z bare minimum (but often sufficient) arguments to build a C++ extension. 2024-12-18T01:36:56.9578870Z 2024-12-18T01:36:56.9579084Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:36:56.9579272Z constructor. Full list arguments can be found at 2024-12-18T01:36:56.9579604Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:36:56.9579689Z 2024-12-18T01:36:56.9579783Z .. note:: 2024-12-18T01:36:56.9580020Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:36:56.9580226Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:36:56.9580455Z the user's responsibility in their library to not use APIs from 2024-12-18T01:36:56.9580683Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:36:56.9580908Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:36:56.9581122Z example, to give access to custom ops from python, the library should 2024-12-18T01:36:56.9581253Z register the ops through the dispatcher. 2024-12-18T01:36:56.9581344Z 2024-12-18T01:36:56.9581432Z Example: 2024-12-18T01:36:56.9581539Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9581714Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:56.9581830Z >>> from setuptools import setup 2024-12-18T01:36:56.9582059Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2024-12-18T01:36:56.9582149Z >>> setup( 2024-12-18T01:36:56.9582258Z ... name='extension', 2024-12-18T01:36:56.9582355Z ... ext_modules=[ 2024-12-18T01:36:56.9582463Z ... CppExtension( 2024-12-18T01:36:56.9582568Z ... name='extension', 2024-12-18T01:36:56.9582691Z ... sources=['extension.cpp'], 2024-12-18T01:36:56.9582819Z ... extra_compile_args=['-g'], 2024-12-18T01:36:56.9582973Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2024-12-18T01:36:56.9583072Z ... ], 2024-12-18T01:36:56.9583167Z ... cmdclass={ 2024-12-18T01:36:56.9583289Z ... 'build_ext': BuildExtension 2024-12-18T01:36:56.9583383Z ... }) 2024-12-18T01:36:56.9583471Z 2024-12-18T01:36:56.9583728Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9583809Z 2024-12-18T01:36:56.9583909Z warnings.warn(msg) 2024-12-18T01:36:56.9584000Z 2024-12-18T01:36:56.9584190Z --- Parse Warning: 101 / 105 --- 2024-12-18T01:36:56.9585083Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1019. 2024-12-18T01:36:56.9585342Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9585438Z 2024-12-18T01:36:56.9585598Z Create a :class:`setuptools.Extension` for CUDA/C++. 2024-12-18T01:36:56.9585684Z 2024-12-18T01:36:56.9585933Z Convenience method that creates a :class:`setuptools.Extension` with the 2024-12-18T01:36:56.9586135Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2024-12-18T01:36:56.9586403Z extension. This includes the CUDA include path, library path and runtime 2024-12-18T01:36:56.9586491Z library. 2024-12-18T01:36:56.9586589Z 2024-12-18T01:36:56.9586794Z All arguments are forwarded to the :class:`setuptools.Extension` 2024-12-18T01:36:56.9586946Z constructor. Full list arguments can be found at 2024-12-18T01:36:56.9587276Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2024-12-18T01:36:56.9587361Z 2024-12-18T01:36:56.9587461Z .. note:: 2024-12-18T01:36:56.9587685Z The PyTorch python API (as provided in libtorch_python) cannot be built 2024-12-18T01:36:56.9587902Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2024-12-18T01:36:56.9588101Z the user's responsibility in their library to not use APIs from 2024-12-18T01:36:56.9588354Z libtorch_python (in particular pytorch/python bindings) and to only use 2024-12-18T01:36:56.9588575Z APIs from libtorch (aten objects, operators and the dispatcher). For 2024-12-18T01:36:56.9588782Z example, to give access to custom ops from python, the library should 2024-12-18T01:36:56.9588918Z register the ops through the dispatcher. 2024-12-18T01:36:56.9589001Z 2024-12-18T01:36:56.9589097Z Example: 2024-12-18T01:36:56.9589225Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9589377Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:56.9589504Z >>> from setuptools import setup 2024-12-18T01:36:56.9589725Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2024-12-18T01:36:56.9589825Z >>> setup( 2024-12-18T01:36:56.9589931Z ... name='cuda_extension', 2024-12-18T01:36:56.9590024Z ... ext_modules=[ 2024-12-18T01:36:56.9590141Z ... CUDAExtension( 2024-12-18T01:36:56.9590262Z ... name='cuda_extension', 2024-12-18T01:36:56.9590468Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:36:56.9590604Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:36:56.9590739Z ... 'nvcc': ['-O2']}, 2024-12-18T01:36:56.9590904Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2024-12-18T01:36:56.9590993Z ... ], 2024-12-18T01:36:56.9591101Z ... cmdclass={ 2024-12-18T01:36:56.9591222Z ... 'build_ext': BuildExtension 2024-12-18T01:36:56.9591321Z ... }) 2024-12-18T01:36:56.9591409Z 2024-12-18T01:36:56.9591513Z Compute capabilities: 2024-12-18T01:36:56.9591612Z 2024-12-18T01:36:56.9591910Z By default the extension will be compiled to run on all archs of the cards visible during the 2024-12-18T01:36:56.9592212Z building process of the extension, plus PTX. If down the road a new card is installed the 2024-12-18T01:36:56.9592505Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2024-12-18T01:36:56.9592819Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2024-12-18T01:36:56.9593105Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2024-12-18T01:36:56.9593234Z support (see below for details on PTX). 2024-12-18T01:36:56.9593332Z 2024-12-18T01:36:56.9593640Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2024-12-18T01:36:56.9593775Z CCs you want the extension to support: 2024-12-18T01:36:56.9593863Z 2024-12-18T01:36:56.9594065Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2024-12-18T01:36:56.9594301Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2024-12-18T01:36:56.9594391Z 2024-12-18T01:36:56.9594718Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2024-12-18T01:36:56.9595060Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2024-12-18T01:36:56.9595371Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2024-12-18T01:36:56.9595745Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2024-12-18T01:36:56.9596077Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2024-12-18T01:36:56.9596349Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2024-12-18T01:36:56.9596665Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2024-12-18T01:36:56.9596991Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2024-12-18T01:36:56.9597124Z "8.0 8.6" would be better. 2024-12-18T01:36:56.9597224Z 2024-12-18T01:36:56.9597522Z Note that while it's possible to include all supported archs, the more archs get included the 2024-12-18T01:36:56.9597824Z slower the building process will be, as it will build a separate kernel image for each arch. 2024-12-18T01:36:56.9598078Z 2024-12-18T01:36:56.9598464Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2024-12-18T01:36:56.9598690Z To workaround the issue, move python binding logic to pure C++ file. 2024-12-18T01:36:56.9598777Z 2024-12-18T01:36:56.9598886Z Example use: 2024-12-18T01:36:56.9598989Z #include 2024-12-18T01:36:56.9599162Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2024-12-18T01:36:56.9599250Z 2024-12-18T01:36:56.9599341Z Instead of: 2024-12-18T01:36:56.9599465Z #include 2024-12-18T01:36:56.9599626Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2024-12-18T01:36:56.9599757Z 2024-12-18T01:36:56.9600031Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2024-12-18T01:36:56.9600543Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2024-12-18T01:36:56.9600623Z 2024-12-18T01:36:56.9600735Z Relocatable device code linking: 2024-12-18T01:36:56.9600832Z 2024-12-18T01:36:56.9601105Z If you want to reference device symbols across compilation units (across object files), 2024-12-18T01:36:56.9601375Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2024-12-18T01:36:56.9601728Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2024-12-18T01:36:56.9602063Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2024-12-18T01:36:56.9602386Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2024-12-18T01:36:56.9602759Z helps reduce the protentional perf degradation of `-rdc`. 2024-12-18T01:36:56.9602943Z Note that it needs to be used at both steps to be useful. 2024-12-18T01:36:56.9603031Z 2024-12-18T01:36:56.9603407Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2024-12-18T01:36:56.9603584Z There is also a case where `-dlink` is used without `-rdc`: 2024-12-18T01:36:56.9603846Z when an extension is linked against a static lib containing rdc-compiled objects 2024-12-18T01:36:56.9604060Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2024-12-18T01:36:56.9604146Z 2024-12-18T01:36:56.9604355Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2024-12-18T01:36:56.9604445Z 2024-12-18T01:36:56.9604545Z Example: 2024-12-18T01:36:56.9604649Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9604803Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:56.9604961Z >>> CUDAExtension( 2024-12-18T01:36:56.9605070Z ... name='cuda_extension', 2024-12-18T01:36:56.9605242Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:36:56.9605338Z ... dlink=True, 2024-12-18T01:36:56.9605469Z ... dlink_libraries=["dlink_lib"], 2024-12-18T01:36:56.9605601Z ... extra_compile_args={'cxx': ['-g'], 2024-12-18T01:36:56.9605729Z ... 'nvcc': ['-O2', '-rdc=true']}) 2024-12-18T01:36:56.9605823Z 2024-12-18T01:36:56.9606073Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9606172Z 2024-12-18T01:36:56.9606275Z warnings.warn(msg) 2024-12-18T01:36:56.9606360Z 2024-12-18T01:36:56.9606636Z --- Parse Warning: 102 / 105 --- 2024-12-18T01:36:56.9607484Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1300. 2024-12-18T01:36:56.9607754Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9607844Z 2024-12-18T01:36:56.9608032Z Load a PyTorch C++ extension just-in-time (JIT). 2024-12-18T01:36:56.9608124Z 2024-12-18T01:36:56.9608334Z To load an extension, a Ninja build file is emitted, which is used to 2024-12-18T01:36:56.9608553Z compile the given sources into a dynamic library. This library is 2024-12-18T01:36:56.9608778Z subsequently loaded into the current Python process as a module and 2024-12-18T01:36:56.9608921Z returned from this function, ready for use. 2024-12-18T01:36:56.9609010Z 2024-12-18T01:36:56.9609231Z By default, the directory to which the build file is emitted and the 2024-12-18T01:36:56.9609560Z resulting library compiled to is ``/torch_extensions/``, where 2024-12-18T01:36:56.9609773Z ```` is the temporary folder on the current platform and ```` 2024-12-18T01:36:56.9609998Z the name of the extension. This location can be overridden in two ways. 2024-12-18T01:36:56.9610209Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2024-12-18T01:36:56.9610444Z replaces ``/torch_extensions`` and all extensions will be compiled 2024-12-18T01:36:56.9610665Z into subfolders of this directory. Second, if the ``build_directory`` 2024-12-18T01:36:56.9610911Z argument to this function is supplied, it overrides the entire path, i.e. 2024-12-18T01:36:56.9611079Z the library will be compiled into that folder directly. 2024-12-18T01:36:56.9611167Z 2024-12-18T01:36:56.9611390Z To compile the sources, the default system compiler (``c++``) is used, 2024-12-18T01:36:56.9611631Z which can be overridden by setting the ``CXX`` environment variable. To pass 2024-12-18T01:36:56.9611867Z additional arguments to the compilation process, ``extra_cflags`` or 2024-12-18T01:36:56.9612088Z ``extra_ldflags`` can be provided. For example, to compile your extension 2024-12-18T01:36:56.9612312Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2024-12-18T01:36:56.9612470Z ``extra_cflags`` to pass further include directories. 2024-12-18T01:36:56.9612562Z 2024-12-18T01:36:56.9612807Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2024-12-18T01:36:56.9612992Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2024-12-18T01:36:56.9613239Z detected and compiled with nvcc rather than the C++ compiler. This includes 2024-12-18T01:36:56.9613449Z passing the CUDA lib64 directory as a library directory, and linking 2024-12-18T01:36:56.9613615Z ``cudart``. You can pass additional flags to nvcc via 2024-12-18T01:36:56.9613820Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2024-12-18T01:36:56.9614053Z heuristics for finding the CUDA install directory are used, which usually 2024-12-18T01:36:56.9614317Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2024-12-18T01:36:56.9614414Z safest option. 2024-12-18T01:36:56.9614512Z 2024-12-18T01:36:56.9614599Z Args: 2024-12-18T01:36:56.9614808Z name: The name of the extension to build. This MUST be the same as the 2024-12-18T01:36:56.9614928Z name of the pybind11 module! 2024-12-18T01:36:56.9615130Z sources: A list of relative or absolute paths to C++ source files. 2024-12-18T01:36:56.9615359Z extra_cflags: optional list of compiler flags to forward to the build. 2024-12-18T01:36:56.9615575Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2024-12-18T01:36:56.9615697Z when building CUDA sources. 2024-12-18T01:36:56.9615939Z extra_ldflags: optional list of linker flags to forward to the build. 2024-12-18T01:36:56.9616158Z extra_include_paths: optional list of include directories to forward 2024-12-18T01:36:56.9616270Z to the build. 2024-12-18T01:36:56.9616450Z build_directory: optional path to use as build workspace. 2024-12-18T01:36:56.9616642Z verbose: If ``True``, turns on verbose logging of load steps. 2024-12-18T01:36:56.9616887Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:36:56.9617061Z the build. If set to ``None`` (default), this value is 2024-12-18T01:36:56.9617262Z automatically determined based on the existence of ``.cu`` or 2024-12-18T01:36:56.9617433Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2024-12-18T01:36:56.9617556Z and libraries to be included. 2024-12-18T01:36:56.9617760Z is_python_module: If ``True`` (default), imports the produced shared 2024-12-18T01:36:56.9617960Z library as a Python module. If ``False``, behavior depends on 2024-12-18T01:36:56.9618084Z ``is_standalone``. 2024-12-18T01:36:56.9618300Z is_standalone: If ``False`` (default) loads the constructed extension 2024-12-18T01:36:56.9618497Z into the process as a plain dynamic library. If ``True``, build a 2024-12-18T01:36:56.9618604Z standalone executable. 2024-12-18T01:36:56.9618703Z 2024-12-18T01:36:56.9618792Z Returns: 2024-12-18T01:36:56.9618922Z If ``is_python_module`` is ``True``: 2024-12-18T01:36:56.9619098Z Returns the loaded PyTorch extension as a Python module. 2024-12-18T01:36:56.9619197Z 2024-12-18T01:36:56.9619397Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2024-12-18T01:36:56.9619607Z Returns nothing. (The shared library is loaded into the process as 2024-12-18T01:36:56.9619715Z a side effect.) 2024-12-18T01:36:56.9619800Z 2024-12-18T01:36:56.9619925Z If ``is_standalone`` is ``True``. 2024-12-18T01:36:56.9620126Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2024-12-18T01:36:56.9620304Z added to the PATH environment variable as a side effect.) 2024-12-18T01:36:56.9620399Z 2024-12-18T01:36:56.9620490Z Example: 2024-12-18T01:36:56.9620605Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9620745Z >>> from torch.utils.cpp_extension import load 2024-12-18T01:36:56.9620855Z >>> module = load( 2024-12-18T01:36:56.9620958Z ... name='extension', 2024-12-18T01:36:56.9621123Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2024-12-18T01:36:56.9621239Z ... extra_cflags=['-O2'], 2024-12-18T01:36:56.9621339Z ... verbose=True) 2024-12-18T01:36:56.9621432Z 2024-12-18T01:36:56.9621682Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9621770Z 2024-12-18T01:36:56.9621879Z warnings.warn(msg) 2024-12-18T01:36:56.9621965Z 2024-12-18T01:36:56.9622182Z --- Parse Warning: 103 / 105 --- 2024-12-18T01:36:56.9623077Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1593. 2024-12-18T01:36:56.9623356Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9623441Z 2024-12-18T01:36:56.9623649Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2024-12-18T01:36:56.9623746Z 2024-12-18T01:36:56.9623977Z This function behaves exactly like :func:`load`, but takes its sources as 2024-12-18T01:36:56.9624224Z strings rather than filenames. These strings are stored to files in the 2024-12-18T01:36:56.9624434Z build directory, after which the behavior of :func:`load_inline` is 2024-12-18T01:36:56.9624584Z identical to :func:`load`. 2024-12-18T01:36:56.9624669Z 2024-12-18T01:36:56.9624759Z See `the 2024-12-18T01:36:56.9625095Z tests `_ 2024-12-18T01:36:56.9625224Z for good examples of using this function. 2024-12-18T01:36:56.9625326Z 2024-12-18T01:36:56.9625561Z Sources may omit two required parts of a typical non-inline C++ extension: 2024-12-18T01:36:56.9625828Z the necessary header includes, as well as the (pybind11) binding code. More 2024-12-18T01:36:56.9626076Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2024-12-18T01:36:56.9626264Z single ``.cpp`` file. This file is then prepended with ``#include 2024-12-18T01:36:56.9626387Z ``. 2024-12-18T01:36:56.9626473Z 2024-12-18T01:36:56.9626710Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2024-12-18T01:36:56.9626950Z automatically generated for each function specified. ``functions`` can 2024-12-18T01:36:56.9627193Z either be a list of function names, or a dictionary mapping from function 2024-12-18T01:36:56.9627432Z names to docstrings. If a list is given, the name of each function is used 2024-12-18T01:36:56.9627528Z as its docstring. 2024-12-18T01:36:56.9627623Z 2024-12-18T01:36:56.9627838Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2024-12-18T01:36:56.9628029Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2024-12-18T01:36:56.9628242Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2024-12-18T01:36:56.9628460Z separately, but ultimately linked into a single library. Note that no 2024-12-18T01:36:56.9628701Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2024-12-18T01:36:56.9628920Z to a CUDA kernel, you must create a C++ function that calls it, and either 2024-12-18T01:36:56.9629147Z declare or define this C++ function in one of the ``cpp_sources`` (and 2024-12-18T01:36:56.9629265Z include its name in ``functions``). 2024-12-18T01:36:56.9629365Z 2024-12-18T01:36:56.9629550Z See :func:`load` for a description of arguments omitted below. 2024-12-18T01:36:56.9629635Z 2024-12-18T01:36:56.9629734Z Args: 2024-12-18T01:36:56.9629954Z cpp_sources: A string, or list of strings, containing C++ source code. 2024-12-18T01:36:56.9630190Z cuda_sources: A string, or list of strings, containing CUDA source code. 2024-12-18T01:36:56.9630398Z functions: A list of function names for which to generate function 2024-12-18T01:36:56.9630611Z bindings. If a dictionary is given, it should map function names to 2024-12-18T01:36:56.9630804Z docstrings (which are otherwise just the function names). 2024-12-18T01:36:56.9631028Z with_cuda: Determines whether CUDA headers and libraries are added to 2024-12-18T01:36:56.9631202Z the build. If set to ``None`` (default), this value is 2024-12-18T01:36:56.9631409Z automatically determined based on whether ``cuda_sources`` is 2024-12-18T01:36:56.9631601Z provided. Set it to ``True`` to force CUDA headers 2024-12-18T01:36:56.9631717Z and libraries to be included. 2024-12-18T01:36:56.9631925Z with_pytorch_error_handling: Determines whether pytorch error and 2024-12-18T01:36:56.9632132Z warning macros are handled by pytorch instead of pybind. To do 2024-12-18T01:36:56.9632347Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2024-12-18T01:36:56.9632557Z function. This redirection might cause issues in obscure cases 2024-12-18T01:36:56.9632741Z of cpp. This flag should be set to ``False`` when this redirect 2024-12-18T01:36:56.9632852Z causes issues. 2024-12-18T01:36:56.9632936Z 2024-12-18T01:36:56.9633028Z Example: 2024-12-18T01:36:56.9633217Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2024-12-18T01:36:56.9633379Z >>> from torch.utils.cpp_extension import load_inline 2024-12-18T01:36:56.9633488Z >>> source = """ 2024-12-18T01:36:56.9633639Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2024-12-18T01:36:56.9633753Z return x.sin() + y.sin(); 2024-12-18T01:36:56.9633841Z } 2024-12-18T01:36:56.9633928Z """ 2024-12-18T01:36:56.9634110Z >>> module = load_inline(name='inline_extension', 2024-12-18T01:36:56.9634234Z ... cpp_sources=[source], 2024-12-18T01:36:56.9634369Z ... functions=['sin_add']) 2024-12-18T01:36:56.9634456Z 2024-12-18T01:36:56.9634550Z .. note:: 2024-12-18T01:36:56.9634796Z Since load_inline will just-in-time compile the source code, please ensure 2024-12-18T01:36:56.9635025Z that you have the right toolchains installed in the runtime. For example, 2024-12-18T01:36:56.9635255Z when loading C++, make sure a C++ compiler is available. If you're loading 2024-12-18T01:36:56.9635529Z a CUDA extension, you will need to additionally install the corresponding CUDA 2024-12-18T01:36:56.9635887Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2024-12-18T01:36:56.9636124Z are not included when you install torch and must be additionally installed. 2024-12-18T01:36:56.9636212Z 2024-12-18T01:36:56.9636473Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2024-12-18T01:36:56.9636691Z the extension. This may use up too many resources on some systems. One 2024-12-18T01:36:56.9636925Z can control the number of workers by setting the `MAX_JOBS` environment 2024-12-18T01:36:56.9637042Z variable to a non-negative number. 2024-12-18T01:36:56.9637143Z 2024-12-18T01:36:56.9637391Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9637477Z 2024-12-18T01:36:56.9637592Z warnings.warn(msg) 2024-12-18T01:36:56.9637675Z 2024-12-18T01:36:56.9637904Z --- Parse Warning: 104 / 105 --- 2024-12-18T01:36:56.9638850Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2024-12-18T01:36:56.9639123Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9639210Z 2024-12-18T01:36:56.9639501Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2024-12-18T01:36:56.9639600Z 2024-12-18T01:36:56.9639887Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2024-12-18T01:36:56.9640145Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2024-12-18T01:36:56.9640375Z server like load. It can emulate multiple calling threads to a single module 2024-12-18T01:36:56.9640627Z provided. In the future we plan to enhance this component to support inter and 2024-12-18T01:36:56.9640914Z intra-op parallelism as well as multiple models running in a single process. 2024-12-18T01:36:56.9640999Z 2024-12-18T01:36:56.9641260Z Please note that even though nn.Module is supported, it might incur an overhead 2024-12-18T01:36:56.9641483Z from the need to hold GIL every time we execute Python code or pass around 2024-12-18T01:36:56.9641730Z inputs as Python objects. As soon as you have a ScriptModule version of your 2024-12-18T01:36:56.9641960Z model for inference deployment it is better to switch to using it in this 2024-12-18T01:36:56.9642067Z benchmark. 2024-12-18T01:36:56.9642157Z 2024-12-18T01:36:56.9642250Z Example:: 2024-12-18T01:36:56.9642349Z 2024-12-18T01:36:56.9642475Z >>> # xdoctest: +SKIP("undefined vars") 2024-12-18T01:36:56.9642662Z >>> from torch.utils import ThroughputBenchmark 2024-12-18T01:36:56.9642800Z >>> bench = ThroughputBenchmark(my_module) 2024-12-18T01:36:56.9642977Z >>> # Pre-populate benchmark's data set with the inputs 2024-12-18T01:36:56.9643082Z >>> for input in inputs: 2024-12-18T01:36:56.9643301Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2024-12-18T01:36:56.9643442Z ... bench.add_input(input[0], x2=input[1]) 2024-12-18T01:36:56.9643666Z >>> # Inputs supplied above are randomly used during the execution 2024-12-18T01:36:56.9643788Z >>> stats = bench.benchmark( 2024-12-18T01:36:56.9643897Z ... num_calling_threads=4, 2024-12-18T01:36:56.9644003Z ... num_warmup_iters = 100, 2024-12-18T01:36:56.9644113Z ... num_iters = 1000, 2024-12-18T01:36:56.9644202Z ... ) 2024-12-18T01:36:56.9644391Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2024-12-18T01:36:56.9644571Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2024-12-18T01:36:56.9644668Z 2024-12-18T01:36:56.9644960Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9645049Z 2024-12-18T01:36:56.9645158Z warnings.warn(msg) 2024-12-18T01:36:56.9645244Z 2024-12-18T01:36:56.9645447Z --- Parse Warning: 105 / 105 --- 2024-12-18T01:36:56.9646367Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=17. 2024-12-18T01:36:56.9646640Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2024-12-18T01:36:56.9646844Z Sampler that restricts data loading to a subset of the dataset. 2024-12-18T01:36:56.9646931Z 2024-12-18T01:36:56.9647077Z It is especially useful in conjunction with 2024-12-18T01:36:56.9647332Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2024-12-18T01:36:56.9647605Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2024-12-18T01:36:56.9647836Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2024-12-18T01:36:56.9647977Z original dataset that is exclusive to it. 2024-12-18T01:36:56.9648063Z 2024-12-18T01:36:56.9648157Z .. note:: 2024-12-18T01:36:56.9648404Z Dataset is assumed to be of constant size and that any instance of it always 2024-12-18T01:36:56.9648545Z returns the same elements in the same order. 2024-12-18T01:36:56.9648641Z 2024-12-18T01:36:56.9648730Z Args: 2024-12-18T01:36:56.9648855Z dataset: Dataset used for sampling. 2024-12-18T01:36:56.9649081Z num_replicas (int, optional): Number of processes participating in 2024-12-18T01:36:56.9649330Z distributed training. By default, :attr:`world_size` is retrieved from the 2024-12-18T01:36:56.9649456Z current distributed group. 2024-12-18T01:36:56.9649734Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2024-12-18T01:36:56.9649946Z By default, :attr:`rank` is retrieved from the current distributed 2024-12-18T01:36:56.9650037Z group. 2024-12-18T01:36:56.9650262Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2024-12-18T01:36:56.9650364Z indices. 2024-12-18T01:36:56.9650558Z seed (int, optional): random seed used to shuffle the sampler if 2024-12-18T01:36:56.9650768Z :attr:`shuffle=True`. This number should be identical across all 2024-12-18T01:36:56.9650932Z processes in the distributed group. Default: ``0``. 2024-12-18T01:36:56.9651157Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2024-12-18T01:36:56.9651382Z tail of the data to make it evenly divisible across the number of 2024-12-18T01:36:56.9651582Z replicas. If ``False``, the sampler will add extra indices to make 2024-12-18T01:36:56.9651807Z the data evenly divisible across the replicas. Default: ``False``. 2024-12-18T01:36:56.9651893Z 2024-12-18T01:36:56.9652000Z .. warning:: 2024-12-18T01:36:56.9652191Z In distributed mode, calling the :meth:`set_epoch` method at 2024-12-18T01:36:56.9652486Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2024-12-18T01:36:56.9652743Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2024-12-18T01:36:56.9652869Z the same ordering will be always used. 2024-12-18T01:36:56.9652966Z 2024-12-18T01:36:56.9653060Z Example:: 2024-12-18T01:36:56.9653160Z 2024-12-18T01:36:56.9653261Z >>> # xdoctest: +SKIP 2024-12-18T01:36:56.9653487Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2024-12-18T01:36:56.9653701Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2024-12-18T01:36:56.9653827Z ... sampler=sampler) 2024-12-18T01:36:56.9653978Z >>> for epoch in range(start_epoch, n_epochs): 2024-12-18T01:36:56.9654084Z ... if is_distributed: 2024-12-18T01:36:56.9654218Z ... sampler.set_epoch(epoch) 2024-12-18T01:36:56.9654317Z ... train(loader) 2024-12-18T01:36:56.9654404Z 2024-12-18T01:36:56.9654665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2024-12-18T01:36:56.9654753Z 2024-12-18T01:36:56.9654867Z warnings.warn(msg) 2024-12-18T01:36:56.9654951Z 2024-12-18T01:36:56.9655069Z  2024-12-18T01:36:56.9655256Z === Found 9 run-time warnings === 2024-12-18T01:36:56.9655435Z --- Runtime Warning: 1 / 9 --- 2024-12-18T01:36:56.9655709Z example = 2024-12-18T01:36:56.9657007Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1354: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1938.) 2024-12-18T01:36:56.9657143Z return super().refine_names(names) 2024-12-18T01:36:56.9657227Z 2024-12-18T01:36:56.9657416Z --- Runtime Warning: 2 / 9 --- 2024-12-18T01:36:56.9657723Z example = 2024-12-18T01:36:56.9658338Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:272: UserWarning: Warning only once for all operators, other operators may also be overridden. 2024-12-18T01:36:56.9658667Z Overriding a previously registered kernel for the same operator and the same dispatch key 2024-12-18T01:36:56.9658873Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2024-12-18T01:36:56.9659220Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2024-12-18T01:36:56.9659324Z dispatch key: CPU 2024-12-18T01:36:56.9659767Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2024-12-18T01:36:56.9660309Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:161.) 2024-12-18T01:36:56.9660485Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2024-12-18T01:36:56.9660576Z 2024-12-18T01:36:56.9660760Z --- Runtime Warning: 3 / 9 --- 2024-12-18T01:36:56.9661014Z example = 2024-12-18T01:36:56.9662807Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py:109: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:182.) 2024-12-18T01:36:56.9663096Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2024-12-18T01:36:56.9663188Z 2024-12-18T01:36:56.9663386Z --- Runtime Warning: 4 / 9 --- 2024-12-18T01:36:56.9663641Z example = 2024-12-18T01:36:56.9665242Z :1: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/SparseCsrTensorImpl.cpp:55.) 2024-12-18T01:36:56.9665337Z 2024-12-18T01:36:56.9665530Z --- Runtime Warning: 5 / 9 --- 2024-12-18T01:36:56.9665839Z example = 2024-12-18T01:36:56.9667292Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:264: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2024-12-18T01:36:56.9667467Z new_node = root_const_gm.graph.get_attr(in_node.target) 2024-12-18T01:36:56.9667572Z 2024-12-18T01:36:56.9667758Z --- Runtime Warning: 6 / 9 --- 2024-12-18T01:36:56.9668049Z example = 2024-12-18T01:36:56.9669112Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:375: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-12-18T01:36:56.9669219Z warnings.warn( 2024-12-18T01:36:56.9669321Z 2024-12-18T01:36:56.9669501Z --- Runtime Warning: 7 / 9 --- 2024-12-18T01:36:56.9669844Z example = 2024-12-18T01:36:56.9670883Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:375: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2024-12-18T01:36:56.9671004Z warnings.warn( 2024-12-18T01:36:56.9671090Z 2024-12-18T01:36:56.9671270Z --- Runtime Warning: 8 / 9 --- 2024-12-18T01:36:56.9671584Z example = 2024-12-18T01:36:56.9672377Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-12-18T01:36:56.9672515Z WeightNorm.apply(module, name, dim) 2024-12-18T01:36:56.9672605Z 2024-12-18T01:36:56.9672783Z --- Runtime Warning: 9 / 9 --- 2024-12-18T01:36:56.9673095Z example = 2024-12-18T01:36:56.9674121Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2024-12-18T01:36:56.9674306Z WeightNorm.apply(module, name, dim) 2024-12-18T01:36:56.9674396Z 2024-12-18T01:36:56.9674722Z === 338 passed, 367 skipped, 114 warnings in 12.51 seconds === 2024-12-18T01:36:56.9674915Z Running test_autoload_enable 1/1 ... [2024-12-18 01:36:56.765819] 2024-12-18T01:36:59.2311913Z running install 2024-12-18T01:36:59.2313295Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2024-12-18T01:36:59.2314092Z !! 2024-12-18T01:36:59.2314213Z 2024-12-18T01:36:59.2314358Z ******************************************************************************** 2024-12-18T01:36:59.2314743Z Please avoid running ``setup.py`` directly. 2024-12-18T01:36:59.2315162Z Instead, use pypa/build, pypa/installer or other 2024-12-18T01:36:59.2315542Z standards-based tools. 2024-12-18T01:36:59.2315854Z 2024-12-18T01:36:59.2316178Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2024-12-18T01:36:59.2316802Z ******************************************************************************** 2024-12-18T01:36:59.2317049Z 2024-12-18T01:36:59.2317153Z !! 2024-12-18T01:36:59.2317377Z self.initialize_options() 2024-12-18T01:36:59.2446802Z running build 2024-12-18T01:36:59.2447076Z running build_py 2024-12-18T01:36:59.2522558Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T01:36:59.2524867Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2024-12-18T01:36:59.2528101Z running build_ext 2024-12-18T01:36:59.3331151Z building 'torch_test_cpp_extension.cpp' extension 2024-12-18T01:36:59.3332190Z creating build/temp.linux-x86_64-cpython-313 2024-12-18T01:36:59.3338869Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T01:37:00.3366361Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2024-12-18T01:37:00.3367370Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2024-12-18T01:37:00.3368264Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2024-12-18T01:37:00.3368971Z from extension.cpp:1: 2024-12-18T01:37:00.3370560Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2024-12-18T01:37:00.3371816Z extension.cpp:45:53: required from here 2024-12-18T01:37:00.3374304Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2024-12-18T01:37:00.3376237Z 1539 | class class_ : public detail::generic_type { 2024-12-18T01:37:00.3376646Z | ^~~~~~ 2024-12-18T01:37:00.3378479Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2024-12-18T01:37:00.3380009Z extension.cpp:45:53: required from here 2024-12-18T01:37:00.3383273Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2024-12-18T01:37:00.3385866Z 1599 | with_internals([&](internals &internals) { 2024-12-18T01:37:00.3386250Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:37:00.3386827Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2024-12-18T01:37:00.3387404Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:37:00.3387854Z 1601 | : internals.registered_types_cpp; 2024-12-18T01:37:00.3388273Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:37:00.3388702Z 1602 | instances[std::type_index(typeid(type_alias))] 2024-12-18T01:37:00.3389115Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:37:00.3389513Z 1603 | = instances[std::type_index(typeid(type))]; 2024-12-18T01:37:00.3389907Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2024-12-18T01:37:00.3390229Z 1604 | }); 2024-12-18T01:37:00.3390490Z | ~ 2024-12-18T01:37:00.3393601Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2024-12-18T01:37:00.7677468Z building 'torch_test_cpp_extension.maia' extension 2024-12-18T01:37:00.7682718Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T01:37:01.7421035Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2024-12-18T01:37:02.1449104Z building 'torch_test_cpp_extension.rng' extension 2024-12-18T01:37:02.1453834Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2024-12-18T01:37:03.3063600Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:37:03.3064497Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:37:03.3065291Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:37:03.3066243Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:37:03.3066941Z from rng_extension.cpp:6: 2024-12-18T01:37:03.3067754Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1123: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:37:03.3068555Z 1123 | # pragma unroll 2024-12-18T01:37:03.3068816Z | 2024-12-18T01:37:03.3069345Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1163, 2024-12-18T01:37:03.3070237Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:37:03.3071050Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:37:03.3071896Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:37:03.3072851Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:37:03.3073703Z from rng_extension.cpp:6: 2024-12-18T01:37:03.3074488Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:37:03.3075240Z 59 | #pragma unroll 2024-12-18T01:37:03.3075499Z | 2024-12-18T01:37:03.3076265Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:37:03.3077023Z 72 | #pragma unroll 2024-12-18T01:37:03.3077285Z | 2024-12-18T01:37:03.3077941Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:37:03.3078781Z 87 | #pragma unroll 2024-12-18T01:37:03.3079048Z | 2024-12-18T01:37:03.3079595Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1164, 2024-12-18T01:37:03.3080496Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2024-12-18T01:37:03.3081353Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2024-12-18T01:37:03.3082124Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2024-12-18T01:37:03.3083022Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2024-12-18T01:37:03.3083688Z from rng_extension.cpp:6: 2024-12-18T01:37:03.3084483Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2024-12-18T01:37:03.3085320Z 153 | #pragma unroll 2024-12-18T01:37:03.3085580Z | 2024-12-18T01:37:03.3088606Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2024-12-18T01:37:03.7366765Z running install_lib 2024-12-18T01:37:03.7446086Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T01:37:03.7545737Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T01:37:03.7645560Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2024-12-18T01:37:03.7751980Z running install_egg_info 2024-12-18T01:37:03.7925043Z running egg_info 2024-12-18T01:37:03.7992666Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2024-12-18T01:37:03.7996077Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2024-12-18T01:37:03.7998116Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2024-12-18T01:37:03.7999903Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2024-12-18T01:37:03.8072740Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:37:03.8081022Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2024-12-18T01:37:03.8082637Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2024-12-18T01:37:03.8084082Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2024-12-18T01:37:03.8089795Z running install_scripts 2024-12-18T01:37:06.9852659Z 2024-12-18T01:37:06.9853114Z Running tests... 2024-12-18T01:37:06.9853493Z ---------------------------------------------------------------------- 2024-12-18T01:37:07.1099944Z . 2024-12-18T01:37:07.1100325Z ---------------------------------------------------------------------- 2024-12-18T01:37:07.1101014Z Ran 1 test in 0.125s 2024-12-18T01:37:07.1101182Z 2024-12-18T01:37:07.1101293Z OK 2024-12-18T01:37:07.1101411Z 2024-12-18T01:37:07.1101541Z Generating XML reports... 2024-12-18T01:37:07.7460296Z Running test_cuda_expandable_segments 1/1 ... [2024-12-18 01:37:07.745687] 2024-12-18T01:37:07.7460789Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:07.7463613Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_expandable_segments.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:07.746095] 2024-12-18T01:37:12.0121037Z 2024-12-18T01:37:12.0122145Z test_cuda_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_expandable_segments_1.1_cd18d0a83354f153_.log 2024-12-18T01:37:12.0122871Z 2024-12-18T01:37:12.0124374Z Running dynamo/test_higher_order_ops 1/1 ... [2024-12-18 01:37:12.012248] 2024-12-18T01:37:12.0124854Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:12.0128099Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:12.012582] 2024-12-18T01:37:15.1195791Z 2024-12-18T01:37:15.1196865Z dynamo/test_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_higher_order_ops_1.1_e4b4bb8d1e7036cf_.log 2024-12-18T01:37:15.1197593Z 2024-12-18T01:37:15.1199358Z Running dynamo/test_misc 1/1 ... [2024-12-18 01:37:15.119736] 2024-12-18T01:37:15.1199775Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:15.1202747Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_misc.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:15.120076] 2024-12-18T01:37:19.0244486Z 2024-12-18T01:37:19.0245452Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_f2508ff12b5630c9_.log 2024-12-18T01:37:19.0246090Z 2024-12-18T01:37:19.0247779Z Running dynamo/test_frame_init 1/1 ... [2024-12-18 01:37:19.024600] 2024-12-18T01:37:19.0248261Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:19.0251194Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:19.024912] 2024-12-18T01:37:22.1002397Z 2024-12-18T01:37:22.1003358Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_bf31cb386694ec65_.log 2024-12-18T01:37:22.1004112Z 2024-12-18T01:37:22.1005384Z Running dynamo/test_nops 1/1 ... [2024-12-18 01:37:22.100368] 2024-12-18T01:37:22.1006010Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:22.1009137Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:22.100687] 2024-12-18T01:37:25.2248271Z 2024-12-18T01:37:25.2249229Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_bb28e85f83b5c09f_.log 2024-12-18T01:37:25.2249868Z 2024-12-18T01:37:25.2251906Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-12-18 01:37:25.225024] 2024-12-18T01:37:25.2252379Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:25.2256211Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:25.225367] 2024-12-18T01:37:28.2634615Z 2024-12-18T01:37:28.2635836Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_8d01458ab9aaf8f8_.log 2024-12-18T01:37:28.2636827Z 2024-12-18T01:37:28.2638189Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-12-18 01:37:28.263649] 2024-12-18T01:37:28.2638650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:28.2642307Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:28.264006] 2024-12-18T01:37:31.3089438Z 2024-12-18T01:37:31.3090930Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_13e1bb7a1cc8ca22_.log 2024-12-18T01:37:31.3091921Z 2024-12-18T01:37:31.3092752Z Running dynamo/test_reconstruct 1/1 ... [2024-12-18 01:37:31.309101] 2024-12-18T01:37:31.3093421Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:31.3096751Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_reconstruct.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:31.309427] 2024-12-18T01:37:34.4531507Z 2024-12-18T01:37:34.4532784Z dynamo/test_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reconstruct_1.1_d9ed2f749ba54c4d_.log 2024-12-18T01:37:34.4533646Z 2024-12-18T01:37:34.4534505Z Running dynamo/test_sdpa 1/1 ... [2024-12-18 01:37:34.453281] 2024-12-18T01:37:34.4535167Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:34.4538531Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:34.453615] 2024-12-18T01:37:37.5480213Z 2024-12-18T01:37:37.5481410Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_99688460facd71ca_.log 2024-12-18T01:37:37.5482055Z 2024-12-18T01:37:37.5484002Z Running dynamo/test_recompiles 1/1 ... [2024-12-18 01:37:37.548195] 2024-12-18T01:37:37.5484588Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:37.5487557Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:37.548512] 2024-12-18T01:37:40.5411969Z 2024-12-18T01:37:40.5413288Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_df546cbc9b80e3dc_.log 2024-12-18T01:37:40.5413979Z 2024-12-18T01:37:40.5414896Z Running dynamo/test_pre_dispatch 1/1 ... [2024-12-18 01:37:40.541305] 2024-12-18T01:37:40.5415561Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:40.5418846Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_pre_dispatch.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:40.541625] 2024-12-18T01:37:43.6171676Z 2024-12-18T01:37:43.6173032Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_1c1f26b5ae986dde_.log 2024-12-18T01:37:43.6174098Z 2024-12-18T01:37:43.6174709Z Running dynamo/test_cudagraphs 1/1 ... [2024-12-18 01:37:43.617305] 2024-12-18T01:37:43.6175397Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:43.6178638Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:43.617605] 2024-12-18T01:37:46.6651575Z 2024-12-18T01:37:46.6652831Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_781b2edcc0504a06_.log 2024-12-18T01:37:46.6653732Z 2024-12-18T01:37:46.6654682Z Running dynamo/test_graph_region_tracker 1/1 ... [2024-12-18 01:37:46.665264] 2024-12-18T01:37:46.6655390Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:46.6658637Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_region_tracker.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:46.665582] 2024-12-18T01:37:49.7924931Z 2024-12-18T01:37:49.7926270Z dynamo/test_graph_region_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_region_tracker_1.1_07c2318493b93ad7_.log 2024-12-18T01:37:49.7927093Z 2024-12-18T01:37:49.7927517Z Running dynamo/test_deviceguard 1/1 ... [2024-12-18 01:37:49.792580] 2024-12-18T01:37:49.7928208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:49.7931877Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:49.792933] 2024-12-18T01:37:52.8877267Z 2024-12-18T01:37:52.8878345Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_d790fc3c87ae2b32_.log 2024-12-18T01:37:52.8879250Z 2024-12-18T01:37:52.8881229Z Running dynamo/test_sources 1/1 ... [2024-12-18 01:37:52.887886] 2024-12-18T01:37:52.8881683Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:52.8884645Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:52.888239] 2024-12-18T01:37:55.9141089Z 2024-12-18T01:37:55.9142193Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_ecb4f7559435a6f2_.log 2024-12-18T01:37:55.9142876Z 2024-12-18T01:37:55.9144577Z Running dynamo/test_structured_trace 1/1 ... [2024-12-18 01:37:55.914287] 2024-12-18T01:37:55.9145063Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:55.9148308Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:55.914621] 2024-12-18T01:37:58.9583008Z 2024-12-18T01:37:58.9584017Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_a3145b0245e182b3_.log 2024-12-18T01:37:58.9584763Z 2024-12-18T01:37:58.9586246Z Running dynamo/test_modes 1/1 ... [2024-12-18 01:37:58.958484] 2024-12-18T01:37:58.9586733Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:37:58.9590141Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:37:58.958814] 2024-12-18T01:38:01.9901605Z 2024-12-18T01:38:01.9902938Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_a47af9d805e784d0_.log 2024-12-18T01:38:01.9903953Z 2024-12-18T01:38:01.9906010Z Running dynamo/test_graph_deduplication 1/1 ... [2024-12-18 01:38:01.990381] 2024-12-18T01:38:01.9906810Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:01.9911043Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_deduplication.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:01.990789] 2024-12-18T01:38:05.1541755Z 2024-12-18T01:38:05.1542909Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_fc5d8404dff63cad_.log 2024-12-18T01:38:05.1543900Z 2024-12-18T01:38:05.1545433Z Running dynamo/test_ctx_manager 1/1 ... [2024-12-18 01:38:05.154357] 2024-12-18T01:38:05.1545948Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:05.1549345Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_ctx_manager.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:05.154690] 2024-12-18T01:38:08.2068846Z 2024-12-18T01:38:08.2069791Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_7c8ef2215af123af_.log 2024-12-18T01:38:08.2070469Z 2024-12-18T01:38:08.2072560Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-12-18 01:38:08.207080] 2024-12-18T01:38:08.2073171Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:08.2076602Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:08.207430] 2024-12-18T01:38:11.1785386Z 2024-12-18T01:38:11.1786427Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_81df5271d66401c7_.log 2024-12-18T01:38:11.1787234Z 2024-12-18T01:38:11.1788809Z Running dynamo/test_trace_rules 1/1 ... [2024-12-18 01:38:11.178708] 2024-12-18T01:38:11.1789269Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:11.1792420Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:11.179030] 2024-12-18T01:38:14.2541314Z 2024-12-18T01:38:14.2542403Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_5ad3c73feb19e482_.log 2024-12-18T01:38:14.2543083Z 2024-12-18T01:38:14.2544922Z Running dynamo/test_debug_utils 1/1 ... [2024-12-18 01:38:14.254321] 2024-12-18T01:38:14.2545389Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:14.2548709Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:14.254664] 2024-12-18T01:38:17.2202106Z 2024-12-18T01:38:17.2203068Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_6a320cb3ec0a6016_.log 2024-12-18T01:38:17.2204003Z 2024-12-18T01:38:17.2205614Z Running dynamo/test_bytecode_utils 1/1 ... [2024-12-18 01:38:17.220384] 2024-12-18T01:38:17.2213626Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:17.2214938Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:17.220697] 2024-12-18T01:38:20.3091296Z 2024-12-18T01:38:20.3092287Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_0221595e634cc158_.log 2024-12-18T01:38:20.3092996Z 2024-12-18T01:38:20.3094083Z Running dynamo/test_recompile_ux 1/1 ... [2024-12-18 01:38:20.309253] 2024-12-18T01:38:20.3094546Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:20.3097842Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:20.309548] 2024-12-18T01:38:23.4130207Z 2024-12-18T01:38:23.4131674Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_3293a8238459cb03_.log 2024-12-18T01:38:23.4132658Z 2024-12-18T01:38:23.4133436Z Running dynamo/test_minifier 1/1 ... [2024-12-18 01:38:23.413152] 2024-12-18T01:38:23.4134102Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:23.4137459Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:23.413502] 2024-12-18T01:38:26.4810506Z 2024-12-18T01:38:26.4811468Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_ec701c6d16eda61d_.log 2024-12-18T01:38:26.4812226Z 2024-12-18T01:38:26.4813978Z Running dynamo/test_comptime 1/1 ... [2024-12-18 01:38:26.481186] 2024-12-18T01:38:26.4814644Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:26.4817819Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_comptime.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:26.481522] 2024-12-18T01:38:29.5253602Z 2024-12-18T01:38:29.5254584Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_0dfda0c1b758430f_.log 2024-12-18T01:38:29.5255273Z 2024-12-18T01:38:29.5256939Z Running test_hub 1/1 ... [2024-12-18 01:38:29.525530] 2024-12-18T01:38:29.5257370Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:29.5260908Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:29.525873] 2024-12-18T01:38:32.5727829Z 2024-12-18T01:38:32.5729009Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_0bb1cc5ca2a6542c_.log 2024-12-18T01:38:32.5729910Z 2024-12-18T01:38:32.5731743Z Running optim/test_swa_utils 1/1 ... [2024-12-18 01:38:32.572984] 2024-12-18T01:38:32.5732429Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:32.5736995Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_swa_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:32.573374] 2024-12-18T01:38:35.6847296Z 2024-12-18T01:38:35.6848670Z optim/test_swa_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_swa_utils_1.1_e00fbeffeec5c2e0_.log 2024-12-18T01:38:35.6849906Z 2024-12-18T01:38:35.6853022Z Running test_quantization 1/4 ... [2024-12-18 01:38:35.685122] 2024-12-18T01:38:35.6853686Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:35.6858362Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=1', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:35.685506] 2024-12-18T01:38:39.7552808Z 2024-12-18T01:38:39.7553926Z test_quantization 1/4 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_1.4_7050267e5ff70181_.log 2024-12-18T01:38:39.7555257Z Running 0 items in this shard: 2024-12-18T01:38:39.7555485Z 2024-12-18T01:38:39.7556534Z Running profiler/test_record_function 1/1 ... [2024-12-18 01:38:39.755481] 2024-12-18T01:38:39.7557281Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:39.7560403Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:39.755813] 2024-12-18T01:38:42.9242372Z 2024-12-18T01:38:42.9243637Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_d62e0cebe0f32883_.log 2024-12-18T01:38:42.9244501Z Running 0 items in this shard: 2024-12-18T01:38:42.9244748Z 2024-12-18T01:38:42.9245806Z Running profiler/test_execution_trace 1/1 ... [2024-12-18 01:38:42.924394] 2024-12-18T01:38:42.9246471Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:42.9249311Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:42.924693] 2024-12-18T01:38:46.0931795Z 2024-12-18T01:38:46.0932787Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_065edbdfece9d482_.log 2024-12-18T01:38:46.0933655Z Running 0 items in this shard: 2024-12-18T01:38:46.0933850Z 2024-12-18T01:38:46.1007009Z Running test_cuda_expandable_segments 1/1 ... [2024-12-18 01:38:46.100380] 2024-12-18T01:38:46.1007788Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:46.1011246Z Running dynamo/test_higher_order_ops 1/1 ... [2024-12-18 01:38:46.100789] 2024-12-18T01:38:46.1012166Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:46.1013486Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_expandable_segments.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:46.100904] 2024-12-18T01:38:46.1015997Z Running dynamo/test_misc 1/1 ... [2024-12-18 01:38:46.100983] 2024-12-18T01:38:46.1016509Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:46.1017642Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:46.101306] 2024-12-18T01:38:46.1019479Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_misc.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:46.101438] 2024-12-18T01:38:49.4788056Z 2024-12-18T01:38:49.4789850Z dynamo/test_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_higher_order_ops_1.1_57177bf2f642a2b7_.log 2024-12-18T01:38:49.4791133Z 2024-12-18T01:38:50.2784556Z Uploading artifacts took 0.80 seconds 2024-12-18T01:38:50.3629412Z 2024-12-18T01:38:50.3630947Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_e935ac09fcf6cdca_.log 2024-12-18T01:38:50.3632093Z 2024-12-18T01:38:50.6501014Z 2024-12-18T01:38:50.6502531Z test_cuda_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_expandable_segments_1.1_16fbd9c961deda42_.log 2024-12-18T01:38:50.6503936Z 2024-12-18T01:38:53.0486465Z Running dynamo/test_frame_init 1/1 ... [2024-12-18 01:38:53.048231] 2024-12-18T01:38:53.0487125Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:53.0488586Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_frame_init.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:53.048588] 2024-12-18T01:38:53.8041970Z Running dynamo/test_nops 1/1 ... [2024-12-18 01:38:53.803786] 2024-12-18T01:38:53.8042615Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:53.8044132Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:53.804138] 2024-12-18T01:38:54.1404334Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-12-18 01:38:54.140003] 2024-12-18T01:38:54.1405113Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:54.1407228Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_fx_passes_pre_grad.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:54.140375] 2024-12-18T01:38:56.3091546Z 2024-12-18T01:38:56.3093010Z dynamo/test_frame_init 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_frame_init_1.1_0382141dcf6e8e77_.log 2024-12-18T01:38:56.3094225Z 2024-12-18T01:38:57.1398241Z 2024-12-18T01:38:57.1399129Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_7580fdef940fa74a_.log 2024-12-18T01:38:57.1399752Z 2024-12-18T01:38:57.4656510Z 2024-12-18T01:38:57.4658108Z dynamo/test_fx_passes_pre_grad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_8631972e8af55ee2_.log 2024-12-18T01:38:57.4659589Z 2024-12-18T01:38:59.7077059Z Running dynamo/test_skip_non_tensor 1/1 ... [2024-12-18 01:38:59.707276] 2024-12-18T01:38:59.7077808Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:38:59.7079786Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_non_tensor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:38:59.707650] 2024-12-18T01:39:00.6347868Z Running dynamo/test_reconstruct 1/1 ... [2024-12-18 01:39:00.634378] 2024-12-18T01:39:00.6348585Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:00.6350789Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_reconstruct.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:00.634784] 2024-12-18T01:39:00.9679972Z Running dynamo/test_sdpa 1/1 ... [2024-12-18 01:39:00.967590] 2024-12-18T01:39:00.9680793Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:00.9683064Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:00.967961] 2024-12-18T01:39:03.0143084Z 2024-12-18T01:39:03.0144890Z dynamo/test_skip_non_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_non_tensor_1.1_0e031f6df6711327_.log 2024-12-18T01:39:03.0146284Z 2024-12-18T01:39:03.9551779Z 2024-12-18T01:39:03.9553194Z dynamo/test_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_reconstruct_1.1_c47f76cd70529572_.log 2024-12-18T01:39:03.9554288Z 2024-12-18T01:39:04.4098403Z 2024-12-18T01:39:04.4100206Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_cc61707e92a90f03_.log 2024-12-18T01:39:04.4101438Z 2024-12-18T01:39:06.5695953Z Running dynamo/test_recompiles 1/1 ... [2024-12-18 01:39:06.569139] 2024-12-18T01:39:06.5696850Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:06.5699262Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:06.569518] 2024-12-18T01:39:07.5540383Z Running dynamo/test_pre_dispatch 1/1 ... [2024-12-18 01:39:07.553598] 2024-12-18T01:39:07.5541146Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:07.5542819Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_pre_dispatch.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:07.553963] 2024-12-18T01:39:07.9865434Z Running dynamo/test_cudagraphs 1/1 ... [2024-12-18 01:39:07.986093] 2024-12-18T01:39:07.9866236Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:07.9871628Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:07.986795] 2024-12-18T01:39:09.8309938Z 2024-12-18T01:39:09.8311335Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_bd40e72860a2eda5_.log 2024-12-18T01:39:09.8312115Z 2024-12-18T01:39:10.8334829Z 2024-12-18T01:39:10.8336238Z dynamo/test_pre_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_pre_dispatch_1.1_b076d25b23fbc8ce_.log 2024-12-18T01:39:10.8336959Z 2024-12-18T01:39:11.2401631Z 2024-12-18T01:39:11.2402662Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_d57524e3e1262838_.log 2024-12-18T01:39:11.2403605Z 2024-12-18T01:39:13.3365045Z Running dynamo/test_graph_region_tracker 1/1 ... [2024-12-18 01:39:13.336054] 2024-12-18T01:39:13.3365945Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:13.3368078Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_region_tracker.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:13.336422] 2024-12-18T01:39:14.3403820Z Running dynamo/test_deviceguard 1/1 ... [2024-12-18 01:39:14.339942] 2024-12-18T01:39:14.3404397Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:14.3405892Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:14.340283] 2024-12-18T01:39:14.6672700Z Running dynamo/test_sources 1/1 ... [2024-12-18 01:39:14.666843] 2024-12-18T01:39:14.6673442Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:14.6676196Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:14.667236] 2024-12-18T01:39:16.6989367Z 2024-12-18T01:39:16.6990534Z dynamo/test_graph_region_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_region_tracker_1.1_407b5017edad1fb3_.log 2024-12-18T01:39:16.6991314Z 2024-12-18T01:39:17.6484117Z 2024-12-18T01:39:17.6485673Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_07fbdb2e3af6318a_.log 2024-12-18T01:39:17.6486396Z 2024-12-18T01:39:18.0480494Z 2024-12-18T01:39:18.0481980Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_c48d9c27d796b204_.log 2024-12-18T01:39:18.0483184Z 2024-12-18T01:39:20.2779381Z Running dynamo/test_structured_trace 1/1 ... [2024-12-18 01:39:20.277459] 2024-12-18T01:39:20.2780118Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:20.2781774Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:20.277840] 2024-12-18T01:39:21.2592847Z Running dynamo/test_modes 1/1 ... [2024-12-18 01:39:21.258812] 2024-12-18T01:39:21.2593711Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:21.2595763Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:21.259176] 2024-12-18T01:39:21.5660320Z Running dynamo/test_graph_deduplication 1/1 ... [2024-12-18 01:39:21.565570] 2024-12-18T01:39:21.5661154Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:21.5663374Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_deduplication.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:21.565974] 2024-12-18T01:39:23.6249106Z 2024-12-18T01:39:23.6250450Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_a162f4523b20b9ee_.log 2024-12-18T01:39:23.6251414Z 2024-12-18T01:39:24.6713012Z 2024-12-18T01:39:24.6714288Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_fbb29f96ca81abeb_.log 2024-12-18T01:39:24.6714931Z 2024-12-18T01:39:25.0142294Z 2024-12-18T01:39:25.0143708Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_1e1a681f1d7b1d48_.log 2024-12-18T01:39:25.0144489Z 2024-12-18T01:39:27.1803937Z Running dynamo/test_ctx_manager 1/1 ... [2024-12-18 01:39:27.179960] 2024-12-18T01:39:27.1804680Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:27.1806787Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_ctx_manager.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:27.180343] 2024-12-18T01:39:28.2222164Z Running dynamo/test_activation_checkpointing 1/1 ... [2024-12-18 01:39:28.221769] 2024-12-18T01:39:28.2223264Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:28.2225937Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:28.222154] 2024-12-18T01:39:28.5485767Z Running dynamo/test_trace_rules 1/1 ... [2024-12-18 01:39:28.548118] 2024-12-18T01:39:28.5486586Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:28.5488641Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:28.548459] 2024-12-18T01:39:30.5455338Z 2024-12-18T01:39:30.5456682Z dynamo/test_ctx_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_ctx_manager_1.1_46b0a5e65c2fe9ba_.log 2024-12-18T01:39:30.5457746Z 2024-12-18T01:39:31.6340855Z 2024-12-18T01:39:31.6342065Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_83ec69ea48fb62a2_.log 2024-12-18T01:39:31.6342888Z 2024-12-18T01:39:31.8730514Z 2024-12-18T01:39:31.8735715Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_0eb89d4006f39b30_.log 2024-12-18T01:39:31.8740578Z 2024-12-18T01:39:34.1488513Z Running dynamo/test_debug_utils 1/1 ... [2024-12-18 01:39:34.148399] 2024-12-18T01:39:34.1489400Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:34.1491403Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:34.148766] 2024-12-18T01:39:35.2052831Z Running dynamo/test_bytecode_utils 1/1 ... [2024-12-18 01:39:35.204832] 2024-12-18T01:39:35.2053766Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:35.2055889Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:35.205182] 2024-12-18T01:39:35.4517520Z Running dynamo/test_recompile_ux 1/1 ... [2024-12-18 01:39:35.451315] 2024-12-18T01:39:35.4518405Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:35.4520225Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:35.451708] 2024-12-18T01:39:37.4442268Z 2024-12-18T01:39:37.4443686Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_04cbdd2a18249f09_.log 2024-12-18T01:39:37.4444913Z 2024-12-18T01:39:38.4857203Z 2024-12-18T01:39:38.4858826Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_2fcc1373f0ec57bd_.log 2024-12-18T01:39:38.4860171Z 2024-12-18T01:39:38.8275359Z 2024-12-18T01:39:38.8277216Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_78dddf6d395cd6aa_.log 2024-12-18T01:39:38.8278912Z 2024-12-18T01:39:40.9369012Z Running dynamo/test_minifier 1/1 ... [2024-12-18 01:39:40.936485] 2024-12-18T01:39:40.9369835Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:40.9371788Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:40.936854] 2024-12-18T01:39:41.9851975Z Running dynamo/test_comptime 1/1 ... [2024-12-18 01:39:41.984786] 2024-12-18T01:39:41.9852705Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:41.9854925Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_comptime.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:41.985155] 2024-12-18T01:39:42.2874584Z Running test_hub 1/1 ... [2024-12-18 01:39:42.287075] 2024-12-18T01:39:42.2875391Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:42.2877421Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:42.287443] 2024-12-18T01:39:44.2652573Z 2024-12-18T01:39:44.2654013Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_61883a91e0a05794_.log 2024-12-18T01:39:44.2655312Z 2024-12-18T01:39:45.2356646Z 2024-12-18T01:39:45.2358040Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_328e5985dede106c_.log 2024-12-18T01:39:45.2359174Z 2024-12-18T01:39:45.6599737Z 2024-12-18T01:39:45.6600602Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_32709423bb8ab6fa_.log 2024-12-18T01:39:45.6601180Z 2024-12-18T01:39:47.7154287Z Running optim/test_swa_utils 1/1 ... [2024-12-18 01:39:47.715004] 2024-12-18T01:39:47.7155118Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:47.7157151Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_swa_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:47.715344] 2024-12-18T01:39:48.7220489Z Running test_quantization 1/4 ... [2024-12-18 01:39:48.721619] 2024-12-18T01:39:48.7221356Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:48.7223559Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=1', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:48.721973] 2024-12-18T01:39:49.1638951Z Running profiler/test_record_function 1/1 ... [2024-12-18 01:39:49.163386] 2024-12-18T01:39:49.1640149Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:49.1647105Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:49.164312] 2024-12-18T01:39:50.9422168Z 2024-12-18T01:39:50.9423756Z optim/test_swa_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_swa_utils_1.1_1caeceef681f7ebe_.log 2024-12-18T01:39:50.9425244Z 2024-12-18T01:39:53.4981542Z 2024-12-18T01:39:53.4983295Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_9910e7d737320886_.log 2024-12-18T01:39:53.4990248Z Running 4 items in this shard: test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_delegation_with_profiler, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function_fork, test/profiler/test_record_function.py::TestRecordFunction::test_record_function 2024-12-18T01:39:53.4993539Z 2024-12-18T01:39:54.6400332Z Running profiler/test_execution_trace 1/1 ... [2024-12-18 01:39:54.639495] 2024-12-18T01:39:54.6401230Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:39:54.6403312Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-12-18 01:39:54.639922] 2024-12-18T01:40:00.1670201Z 2024-12-18T01:40:00.1672152Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_e8738ac528d4c5a6_.log 2024-12-18T01:40:00.1679798Z Running 7 items in this shard: test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_alone_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_nested_tensor_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_no_capture_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_repeat_in_loop_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_start_stop_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_with_kineto_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_with_pt2_cpu 2024-12-18T01:40:00.1685599Z 2024-12-18T01:46:17.9186331Z 2024-12-18T01:46:17.9189216Z test_quantization 1/4 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_1.4_eb97359331d1a3a2_.log 2024-12-18T01:46:17.9380506Z Running 277 items in this shard: test/test_quantization.py::TestQuantizedOps::test_group_norm, test/test_quantization.py::TestQuantizedOps::test_leaky_relu_observed_output, test/test_quantization.py::TestQuantizedOps::test_max_pool2d_pt2e, test/test_quantization.py::TestQuantizedOps::test_max_pool3d_nhwc, test/test_quantization.py::TestQuantizedOps::test_mul_scalar_relu, test/test_quantization.py::TestQuantizedOps::test_qadd_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_qlayer_norm, test/test_quantization.py::TestQuantizedOps::test_qmul_relu_same_qparams, test/test_quantization.py::TestQuantizedOps::test_quantized_equal, test/test_quantization.py::TestQuantizedOps::test_quantized_mean_qnnpack, test/test_quantization.py::TestQuantizedOps::test_sigmoid, test/test_quantization.py::TestQNNPackOps::test_adaptive_avg_pool2d, test/test_quantization.py::TestQNNPackOps::test_qnnpack_add_broadcast, test/test_quantization.py::TestQNNPackOps::test_qnnpack_sigmoid, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_relu_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_leaky_relu, test/test_quantization.py::TestQuantizedLinear::test_qlinear_pt2e, test/test_quantization.py::TestQuantizedLinear::test_qlinear_relu, test/test_quantization.py::TestQuantizedLinear::test_qlinear_with_input_q_dq_qweight_dq_output_fp32, test/test_quantization.py::TestQuantizedLinear::test_wrapped_quantized_linear, test/test_quantization.py::TestQuantizedConv::test_conv_reorder_issue_onednn, test/test_quantization.py::TestQuantizedConv::test_qconv2d, test/test_quantization.py::TestQuantizedConv::test_qconv2d_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_hardswish_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv2d_pt2e, test/test_quantization.py::TestQuantizedConv::test_qconv3d_unpack, test/test_quantization.py::TestQuantizedConv::test_qconv_transpose2d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv1d, test/test_quantization.py::TestDynamicQuantizedOps::test_dynamic_conv2d, test/test_quantization.py::TestDynamicQuantizedOps::test_linear_dynamic_fp16_onednn, test/test_quantization.py::TestDynamicQuantizedOps::test_linear_prepack_fp16_numerics, test/test_quantization.py::TestDynamicQuantizedOps::test_qlinear_legacy, test/test_quantization.py::TestComparatorOps::test_compare_tensor_tensor, test/test_quantization.py::TestPadding::test_constant_padNd, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_2d_indices, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_bag_4bit, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_backward_per_channel_cachemask_cuda, test/test_quantization.py::TestFakeQuantizeOps::test_fake_quant_per_channel_qparam_range, test/test_quantization.py::TestFakeQuantizeOps::test_forward_per_channel_half_precision_numerics, test/test_quantization.py::TestFakeQuantizeOps::test_fq_module_per_tensor, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_backward_per_tensor_cpu, test/test_quantization.py::TestFakeQuantizeOps::test_learnable_forward_per_tensor_cuda, test/test_quantization.py::TestFusedObsFakeQuant::test_fused_obs_fake_quant_moving_avg_per_channel, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_dequantize_fp16_cuda, test/test_quantization.py::TestQuantizedTensor::test_per_channel_qtensor_creation_cuda, test/test_quantization.py::TestQuantizedTensor::test_per_tensor_to_device, test/test_quantization.py::TestQuantizedTensor::test_qtensor_channel_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_copy, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_qtensor_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cpu, test/test_quantization.py::TestQuantizedTensor::test_qtensor_index_select_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_load_save, test/test_quantization.py::TestQuantizedTensor::test_qtensor_masked_fill_cuda, test/test_quantization.py::TestQuantizedTensor::test_qtensor_quantize_per_channel, test/test_quantization.py::TestQuantizedTensor::test_qtensor_reshape, test/test_quantization.py::TestQuantizedTensor::test_qtensor_sub_byte_not_aligned_cols, test/test_quantization.py::TestQuantizedTensor::test_qtensor_view, test/test_quantization.py::TestQuantizedTensor::test_quant_pin_memory, test/test_quantization.py::TestQuantizedTensor::test_repeat, test/test_quantization.py::TestObserver::test_histogram_observer_consistent_buffer_shape, test/test_quantization.py::TestObserver::test_zero_numel, test/test_quantization.py::TestStaticQuantizedModule::test_channel_shuffle, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_conv2d_add, test/test_quantization.py::TestStaticQuantizedModule::test_instance_norm, test/test_quantization.py::TestStaticQuantizedModule::test_layer_norm, test/test_quantization.py::TestStaticQuantizedModule::test_leaky_relu, test/test_quantization.py::TestStaticQuantizedModule::test_pool_api, test/test_quantization.py::TestStaticQuantizedModule::test_prelu, test/test_quantization.py::TestDynamicQuantizedModule::test_dynamic_conv2d, test/test_quantization.py::TestReferenceQuantizedModule::test_rnn, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_one_sided, test/test_quantization.py::TestHistogramObserver::test_histogram_observer_update_within_range_succeeds, test/test_quantization.py::TestDistributed::test_device_affinity, test/test_quantization.py::TestDistributed::test_fake_quant_preserves_buffers, test/test_quantization.py::TestFusedObsFakeQuantModule::test_embedding_qat_config, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_mod_reduce_range, test/test_quantization.py::TestFusedObsFakeQuantModule::test_fused_obs_fq_moving_avg_module, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_fuser_method, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_input_type_to_index, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_observation_type, test/test_quantization.py::TestBackendConfig::test_backend_op_config_set_qat_module, test/test_quantization.py::TestBackendConfig::test_dtype_config_to_dict, test/test_quantization.py::TestUtils::test_get_fqn_to_example_inputs_simple, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_tensor, test/test_quantization.py::TestUtils::test_uint4_int4_dtype, test/test_quantization.py::TestQuantizationDocs::test_quantization_doc_ptsq, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_convtranspose_per_channel_qconfig_none, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_custom_module_class, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_quantized_embedding_bag, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_quantwrapper_attaches_qconfig_to_dequant, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_single_layer, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_skip_quant, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_two_layers, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_forward_hooks_preserved, test/test_quantization.py::TestQuantizeEagerPTQDynamic::test_per_channel_linear_quantize, test/test_quantization.py::TestQuantizeEagerOps::test_conv_transpose_2d, test/test_quantization.py::TestQuantizeEagerQAT::test_defused_embedding_bag_linear, test/test_quantization.py::TestQuantizeEagerQAT::test_embedding_bag_linear, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_numerics, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_linear_bn_symm_numerics, test/test_quantization.py::TestFuseEager::test_fuse_module_eval, test/test_quantization.py::TestFuseEager::test_fuse_modules_with_nested_hooks, test/test_quantization.py::TestFuseEager::test_fusion_conv_with_bias, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_lstm_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_stub_linear_dynamic, test/test_quantization.py::TestNumericSuiteEager::test_mobilenet_v3, test/test_quantization.py::TestEqualizeEager::test_cross_layer_equalization, test/test_quantization.py::TestEqualizeEager::test_equalize, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_convrelu, test/test_quantization.py::TestBiasCorrectionEager::test_conv_chain, test/test_quantization.py::TestFuseFx::test_fuse_conv_bn_relu, test/test_quantization.py::TestQuantizeFx::test_conv_linear_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_not_reference, test/test_quantization.py::TestQuantizeFx::test_conv_transpose_reference, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_set_observed_to_quantized_mapping, test/test_quantization.py::TestQuantizeFx::test_deepcopy_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_default_qconfig_mapping_override_global, test/test_quantization.py::TestQuantizeFx::test_dynamic_quant_fp16, test/test_quantization.py::TestQuantizeFx::test_fuse_custom_config_set_preserved_attributes, test/test_quantization.py::TestQuantizeFx::test_get_executorch_backend_config, test/test_quantization.py::TestQuantizeFx::test_linear_tanh_lowering, test/test_quantization.py::TestQuantizeFx::test_mul_add_fp16_config, test/test_quantization.py::TestQuantizeFx::test_no_obs_between_unmatched_node_and_copy_node, test/test_quantization.py::TestQuantizeFx::test_non_traceable_module, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_output_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_preserved_attributes, test/test_quantization.py::TestQuantizeFx::test_prepared_model_deepcopy, test/test_quantization.py::TestQuantizeFx::test_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_propagate_dtypes_for_known_nodes_dict_split_tuple_args, test/test_quantization.py::TestQuantizeFx::test_qat_and_script, test/test_quantization.py::TestQuantizeFx::test_qat_skip_untraced, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_with_fused_modules, test/test_quantization.py::TestQuantizeFx::test_qconfig_for_call_func, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_repr, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_to_dict, test/test_quantization.py::TestQuantizeFx::test_qparams_fqn, test/test_quantization.py::TestQuantizeFx::test_quantized_input_fp32_output, test/test_quantization.py::TestQuantizeFx::test_relu_lowering, test/test_quantization.py::TestQuantizeFx::test_reshape_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFx::test_sequential, test/test_quantization.py::TestQuantizeFx::test_static_lstm, test/test_quantization.py::TestQuantizeFx::test_static_lstm_with_custom_fixed_qparams, test/test_quantization.py::TestQuantizeFxOps::test_add, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_wrong_qconfig, test/test_quantization.py::TestQuantizeFxOps::test_gelu_reference, test/test_quantization.py::TestQuantizeFxOps::test_general_shape_ops, test/test_quantization.py::TestQuantizeFxOps::test_general_value_ops, test/test_quantization.py::TestQuantizeFxOps::test_linear_module, test/test_quantization.py::TestQuantizeFxOps::test_linear_static_fp16, test/test_quantization.py::TestQuantizeFxOps::test_mul, test/test_quantization.py::TestQuantizeFxOps::test_mul_relu, test/test_quantization.py::TestQuantizeFxOps::test_narrow, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle, test/test_quantization.py::TestQuantizeFxOps::test_prelu, test/test_quantization.py::TestQuantizeFxOps::test_ref_pattern_multi_use, test/test_quantization.py::TestQuantizeFxOps::test_reshape_fp16, test/test_quantization.py::TestQuantizeFxOps::test_softmax_normal, test/test_quantization.py::TestQuantizeFxModels::test_prepare_serialize_switch_device_convert, test/test_quantization.py::TestQuantizeFxModels::test_qat_embeddingbag_linear, test/test_quantization.py::TestQuantizeFxModels::test_static_gpu_convert_basic, test/test_quantization.py::TestQuantizeFxModels::test_torchvision, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_quantization.py::TestGraphUtils::test_customized_equivalet_types_dict, test/test_quantization.py::TestDuplicateDQPass::test_no_need_for_duplicate_dq, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_dq, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_two_dq, test/test_quantization.py::TestNumericDebugger::test_added_node_gets_unique_id, test/test_quantization.py::TestNumericDebugger::test_copy_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_deepcopy_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_extract_results_from_loggers, test/test_quantization.py::TestNumericDebugger::test_quantize_pt2e_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_re_export_preserve_handle, test/test_quantization.py::TestNumericDebugger::test_run_decompositions_preserve_handle, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_add_shadow_loggers_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_conv_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_dynamic, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_linear_fun_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_extract_weights_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_fp16_shadows_fp32, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_fp32_coverage, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_fun, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_vs_linear_fp16_shadow_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_kwargs_shadow, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_mod_ptq, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_mod_qat, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_io_dtype_coverage, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_op_with_either_fp32_or_int8_input, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_ops_with_same_fp32_and_int8_signature, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_shadow_activations_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_shadow_loggers_preserve_qat_numerics, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_module, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_conv_bn_relu_fusion_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_linear_mod_fp32_quant, test/test_quantization.py::TestFXNumericSuiteNShadows::test_custom_functions_and_tracer, test/test_quantization.py::TestFXNumericSuiteNShadows::test_logger_enabled_and_save_activations_flags, test/test_quantization.py::TestFXNumericSuiteNShadows::test_mobilenet_v2, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_end_to_end, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_from_list, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_repr, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_compare_shadow_activations_linear, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_resnet18, test/test_quantization.py::TestFXNumericSuiteCoreAPIsModels::test_sparsenn_compare_activations, test/test_quantization.py::TestFxModelReportDetector::test_conv_sub_class_considered, test/test_quantization.py::TestFxModelReportDetector::test_fusion_layer_in_sequential, test/test_quantization.py::TestFxModelReportDetector::test_qat_aware_model_example, test/test_quantization.py::TestFxModelReportClass::test_generate_visualizer, test/test_quantization.py::TestFxDetectOutliers::test_multiple_run_consistent_spike_outlier_report_gen, test/test_quantization.py::TestFxDetectOutliers::test_outlier_detection_determine_points, test/test_quantization.py::TestFxModelReportVisualizer::test_get_modules_and_features, test/test_quantization.py::TestEqualizeFx::test_input_weight_eq_observer, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_activation_values, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_branching, test/test_quantization.py::TestEqualizeFx::test_selective_equalization, test/test_quantization.py::TestSerialization::test_conv2d, test/test_quantization.py::TestSerialization::test_conv2d_nobias, test/test_quantization.py::TestSerialization::test_conv3d, test/test_quantization.py::TestSerialization::test_linear_dynamic, test/test_quantization.py::TestSerialization::test_linear_relu_package_quantization_transforms, test/test_quantization.py::TestSerialization::test_per_channel_observer, test/test_quantization.py::TestSerialization::test_per_tensor_observer, test/test_quantization.py::TestQuantizeJit::test_conv, test/test_quantization.py::TestQuantizeJit::test_conv_bn, test/test_quantization.py::TestQuantizeJit::test_conv_transpose, test/test_quantization.py::TestQuantizeJit::test_single_linear_dynamic, test/test_quantization.py::TestQuantizeJitPasses::test_convtranspose_trace, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_complex_cases, test/test_quantization.py::TestQuantizeJitPasses::test_foldbn_in_submodule, test/test_quantization.py::TestQuantizeJitPasses::test_fuse_linear, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_general_ops, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_interface, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_weight_dtype, test/test_quantization.py::TestQuantizeJitPasses::test_swap_functional_linear, test/test_quantization.py::TestQuantizeJitOps::test_cat_linear, test/test_quantization.py::TestQuantizeJitOps::test_general_shape_ops, test/test_quantization.py::TestQuantizeJitOps::test_general_value_ops, test/test_quantization.py::TestQuantizeJitOps::test_qbatch_norm_relu_BNRelu, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_alpha, test/test_quantization.py::TestQuantizeJitOps::test_quantized_add_scalar, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv, test/test_quantization.py::TestQuantizeJitOps::test_quantized_mul_scalar, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_convert_dynamic_fp16, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_shared_weights, test/test_quantization.py::TestQuantizeDynamicJitOps::test_linear, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_fuse_modules, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_observer, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_qconfig, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_utils, test/test_quantization.py::TestAOMigrationNNQuantized::test_import_nn_quantizable_activation, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_conv, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_embedding_ops, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_utils, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_intrinsic_qat_linear_fused, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_no_import_nn_intrinsic_quantized_dynamic, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_equalize, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_utils, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_rte_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_soak_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_type_promotion_fails_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_mul_cpu_float8_e4m3fn 2024-12-18T01:46:17.9530908Z 2024-12-18T01:46:18.6743675Z Running test batch 'tests to run' cost 6876.88 seconds 2024-12-18T01:46:19.6140513Z 2024-12-18T01:46:19.6141144Z real 114m42.110s 2024-12-18T01:46:19.6141523Z user 149m42.569s 2024-12-18T01:46:19.6141858Z sys 14m45.731s 2024-12-18T01:46:19.6142185Z + assert_git_not_dirty 2024-12-18T01:46:19.6142949Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2024-12-18T01:46:19.6143499Z + [[ linux-focal-py3.13-clang10 != *xla* ]] 2024-12-18T01:46:19.6147249Z ++ git status --porcelain 2024-12-18T01:46:19.6148069Z ++ grep -v '?? third_party' 2024-12-18T01:46:48.5306216Z ++ true 2024-12-18T01:46:48.5345230Z + git_status= 2024-12-18T01:46:48.5345654Z + [[ -n '' ]] 2024-12-18T01:46:48.5347934Z + [[ 1 == 1 ]] 2024-12-18T01:46:48.5348464Z + test_aten 2024-12-18T01:46:48.5348859Z + echo 'Running ATen tests with pytorch lib' 2024-12-18T01:46:48.5349755Z Running ATen tests with pytorch lib 2024-12-18T01:46:48.5350080Z + [[ -n '' ]] 2024-12-18T01:46:48.5350336Z + echo 'Running test with the build folder' 2024-12-18T01:46:48.5350686Z Running test with the build folder 2024-12-18T01:46:48.5350995Z + TEST_BASE_DIR=build/bin 2024-12-18T01:46:48.5351472Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin 2024-12-18T01:46:48.5387525Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin 2024-12-18T01:46:48.5397173Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin 2024-12-18T01:46:48.5407270Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin 2024-12-18T01:46:48.5418107Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorchbind_test.so build/bin 2024-12-18T01:46:48.5425748Z + ls build/bin 2024-12-18T01:46:48.5478427Z BackoffTest cpu_generator_test 2024-12-18T01:46:48.5479070Z CMakeFiles cpu_profiling_allocator_test 2024-12-18T01:46:48.5479705Z CTestTestfile.cmake cpu_rng_test 2024-12-18T01:46:48.5480320Z CppSignature_test dispatch_key_set_test 2024-12-18T01:46:48.5480915Z Dict_test dlconvertor_test 2024-12-18T01:46:48.5481342Z Dimname_test example_allreduce 2024-12-18T01:46:48.5481711Z FileStoreTest extension_backend_test 2024-12-18T01:46:48.5482059Z HashStoreTest half_test 2024-12-18T01:46:48.5482389Z IListRef_test inline_container_test 2024-12-18T01:46:48.5482805Z KernelFunction_test ivalue_test 2024-12-18T01:46:48.5483267Z List_test kernel_function_legacy_test 2024-12-18T01:46:48.5483649Z Makefile kernel_function_test 2024-12-18T01:46:48.5484090Z MaybeOwned_test kernel_lambda_legacy_test 2024-12-18T01:46:48.5484474Z NamedTensor_test kernel_lambda_test 2024-12-18T01:46:48.5484880Z ProcessGroupGlooTest kernel_stackbased_test 2024-12-18T01:46:48.5485287Z StorageUtils_test lazy_tensor_test 2024-12-18T01:46:48.5485623Z TCPStoreTest legacy_vmap_test 2024-12-18T01:46:48.5485972Z aot_model_compiler_test libc10.so 2024-12-18T01:46:48.5486308Z apply_utils_test 'libcaffe2*' 2024-12-18T01:46:48.5486631Z atest 'libmkldnn*' 2024-12-18T01:46:48.5487062Z backend_fallback_test 'libnccl*' 2024-12-18T01:46:48.5487372Z basic libtorch.so 2024-12-18T01:46:48.5487674Z broadcast_test libtorch_cpu.so 2024-12-18T01:46:48.5488038Z c10_ArrayRef_test libtorch_global_deps.so 2024-12-18T01:46:48.5488416Z c10_Bitset_test libtorch_python.so 2024-12-18T01:46:48.5488844Z c10_CompileTimeFunctionPointer_test libtorchbind_test.so 2024-12-18T01:46:48.5489357Z c10_ConstexprCrc_test make_boxed_from_unboxed_functor_test 2024-12-18T01:46:48.5489806Z c10_DeadlockDetection_test math_kernel_test 2024-12-18T01:46:48.5490203Z c10_DeviceGuard_test memory_format_test 2024-12-18T01:46:48.5490585Z c10_Device_test memory_overlapping_test 2024-12-18T01:46:48.5490990Z c10_DispatchKeySet_test mobile_memory_cleanup 2024-12-18T01:46:48.5491429Z c10_Half_test native_test 2024-12-18T01:46:48.5491773Z c10_InlineDeviceGuard_test op_allowlist_test 2024-12-18T01:46:48.5492206Z c10_InlineStreamGuard_test op_registration_test 2024-12-18T01:46:48.5492617Z c10_LeftRight_test operator_name_test 2024-12-18T01:46:48.5492996Z c10_Metaprogramming_test operators_test 2024-12-18T01:46:48.5493412Z c10_NetworkFlow_test packedtensoraccessor_test 2024-12-18T01:46:48.5493842Z c10_Scalar_test parallel_benchmark 2024-12-18T01:46:48.5494194Z c10_SizesAndStrides_test pow_test 2024-12-18T01:46:48.5494534Z c10_StreamGuard_test protoc 2024-12-18T01:46:48.5494861Z c10_SymInt_test protoc-3.13.0.0 2024-12-18T01:46:48.5495212Z c10_Synchronized_test quantized_test 2024-12-18T01:46:48.5495567Z c10_ThreadLocal_test reduce_ops_test 2024-12-18T01:46:48.5495948Z c10_TypeIndex_test reportMemoryUsage_test 2024-12-18T01:46:48.5496338Z c10_TypeList_test scalar_tensor_test 2024-12-18T01:46:48.5496693Z c10_TypeTraits_test scalar_test 2024-12-18T01:46:48.5497081Z c10_accumulate_test static_runtime_bench 2024-12-18T01:46:48.5497448Z c10_bfloat16_test static_runtime_test 2024-12-18T01:46:48.5497827Z c10_bit_cast_test stride_properties_test 2024-12-18T01:46:48.5498419Z c10_complex_math_test tensor_iterator_test 2024-12-18T01:46:48.5498782Z c10_complex_test test_api 2024-12-18T01:46:48.5499090Z c10_cow_test test_cpp_rpc 2024-12-18T01:46:48.5499398Z c10_error_test test_dist_autograd 2024-12-18T01:46:48.5499778Z c10_exception_test test_edge_op_registration 2024-12-18T01:46:48.5500143Z c10_flags_test test_jit 2024-12-18T01:46:48.5500450Z c10_generic_math_test test_lazy 2024-12-18T01:46:48.5500815Z c10_intrusive_ptr_benchmark test_mobile_nnc 2024-12-18T01:46:48.5501184Z c10_intrusive_ptr_test test_parallel 2024-12-18T01:46:48.5501536Z c10_irange_test test_tensorexpr 2024-12-18T01:46:48.5501867Z c10_lazy_test thread_init_test 2024-12-18T01:46:48.5502212Z c10_logging_test torch_shm_manager 2024-12-18T01:46:48.5502574Z c10_optional_test tutorial_tensorexpr 2024-12-18T01:46:48.5502966Z c10_ordered_preserving_dict_test type_ptr_test 2024-12-18T01:46:48.5503320Z c10_registry_test type_test 2024-12-18T01:46:48.5503674Z c10_small_vector_test undefined_tensor_test 2024-12-18T01:46:48.5504068Z c10_ssize_test vec_test_all_types_AVX2 2024-12-18T01:46:48.5504460Z c10_string_util_test vec_test_all_types_AVX512 2024-12-18T01:46:48.5504879Z c10_string_view_test vec_test_all_types_DEFAULT 2024-12-18T01:46:48.5505270Z c10_tempfile_test verify_api_visibility 2024-12-18T01:46:48.5505626Z c10_typeid_test weakref_test 2024-12-18T01:46:48.5505961Z cmake_install.cmake wrapdim_test 2024-12-18T01:46:48.5506310Z cpu_allocator_test xla_tensor_test 2024-12-18T01:46:48.5506653Z + aten/tools/run_tests.sh build/bin 2024-12-18T01:46:48.5521575Z + set -e 2024-12-18T01:46:48.5524211Z ++ dirname aten/tools/run_tests.sh 2024-12-18T01:46:48.5539551Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2024-12-18T01:46:48.5540193Z + export CPP_TESTS_DIR=build/bin 2024-12-18T01:46:48.5540542Z + CPP_TESTS_DIR=build/bin 2024-12-18T01:46:48.5540817Z + VALGRIND=ON 2024-12-18T01:46:48.5542537Z + python test/run_test.py --cpp --verbose -i cpp/basic cpp/atest cpp/scalar_test cpp/broadcast_test cpp/wrapdim_test cpp/apply_utils_test cpp/dlconvertor_test cpp/native_test cpp/scalar_tensor_test cpp/undefined_tensor_test cpp/extension_backend_test cpp/lazy_tensor_test cpp/tensor_iterator_test cpp/Dimname_test cpp/Dict_test cpp/NamedTensor_test cpp/cpu_generator_test cpp/legacy_vmap_test cpp/operators_test 2024-12-18T01:46:48.6575148Z /var/lib/jenkins/workspace/test/run_test.py:22: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2024-12-18T01:46:48.6576309Z import pkg_resources 2024-12-18T01:46:51.8814812Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json?versionId=PhiMB7EP3187qvpKvnORewoK3InOIvX5 to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2024-12-18T01:46:51.9009644Z Found test times from artifacts 2024-12-18T01:46:51.9744667Z Found test times from artifacts 2024-12-18T01:46:51.9765717Z Running all tests 2024-12-18T01:46:51.9770122Z Running parallel tests on 3 processes 2024-12-18T01:46:51.9772005Z Name: tests to run (est. time: 0.0min) 2024-12-18T01:46:51.9772414Z Serial tests (0): 2024-12-18T01:46:51.9772679Z Parallel tests (19): 2024-12-18T01:46:51.9772944Z cpp/Dict_test 1/1 2024-12-18T01:46:51.9773207Z cpp/Dimname_test 1/1 2024-12-18T01:46:51.9773477Z cpp/NamedTensor_test 1/1 2024-12-18T01:46:51.9773770Z cpp/apply_utils_test 1/1 2024-12-18T01:46:51.9774063Z cpp/atest 1/1 2024-12-18T01:46:51.9774309Z cpp/basic 1/1 2024-12-18T01:46:51.9774574Z cpp/broadcast_test 1/1 2024-12-18T01:46:51.9774854Z cpp/cpu_generator_test 1/1 2024-12-18T01:46:51.9775232Z cpp/dlconvertor_test 1/1 2024-12-18T01:46:51.9775540Z cpp/extension_backend_test 1/1 2024-12-18T01:46:51.9775853Z cpp/lazy_tensor_test 1/1 2024-12-18T01:46:51.9776145Z cpp/legacy_vmap_test 1/1 2024-12-18T01:46:51.9776416Z cpp/native_test 1/1 2024-12-18T01:46:51.9776685Z cpp/operators_test 1/1 2024-12-18T01:46:51.9776969Z cpp/scalar_tensor_test 1/1 2024-12-18T01:46:51.9777259Z cpp/scalar_test 1/1 2024-12-18T01:46:51.9777537Z cpp/tensor_iterator_test 1/1 2024-12-18T01:46:51.9777835Z cpp/undefined_tensor_test 1/1 2024-12-18T01:46:51.9778142Z cpp/wrapdim_test 1/1 2024-12-18T01:46:51.9778435Z Name: excluded (est. time: 0.0min) 2024-12-18T01:46:51.9778737Z Serial tests (0): 2024-12-18T01:46:51.9778990Z Parallel tests (0): 2024-12-18T01:46:51.9830298Z Running cpp/Dict_test 1/1 ... [2024-12-18 01:46:51.982693] 2024-12-18T01:46:51.9831087Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:51.9837200Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-07e8f8257bfa05b5.xml', '-x', '--reruns=2'] ... [2024-12-18 01:46:51.983308] 2024-12-18T01:46:54.6543340Z 2024-12-18T01:46:54.6544595Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_6cc502b647f7ca3d_.log 2024-12-18T01:46:54.6545575Z 2024-12-18T01:46:54.6547304Z Running cpp/Dimname_test 1/1 ... [2024-12-18 01:46:54.654220] 2024-12-18T01:46:54.6547900Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:54.6549651Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0cf67d2b2d9bd743.xml', '-x', '--reruns=2'] ... [2024-12-18 01:46:54.654658] 2024-12-18T01:46:56.2719787Z 2024-12-18T01:46:56.2720835Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_9fd77c52cc88b84f_.log 2024-12-18T01:46:56.2721727Z 2024-12-18T01:46:56.2721953Z Running cpp/NamedTensor_test 1/1 ... [2024-12-18 01:46:56.271854] 2024-12-18T01:46:56.2722426Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:56.2724462Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-49ae1d9f0caae99b.xml', '-x', '--reruns=2'] ... [2024-12-18 01:46:56.272202] 2024-12-18T01:46:57.8893149Z 2024-12-18T01:46:57.8894619Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_134a0a465f601d46_.log 2024-12-18T01:46:57.8895314Z 2024-12-18T01:46:57.8895527Z Running cpp/apply_utils_test 1/1 ... [2024-12-18 01:46:57.889129] 2024-12-18T01:46:57.8896366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:57.8897567Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-62be609d6ebf6105.xml', '-x', '--reruns=2'] ... [2024-12-18 01:46:57.889472] 2024-12-18T01:46:59.5064460Z 2024-12-18T01:46:59.5065671Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_ff8e4dd46daef9d2_.log 2024-12-18T01:46:59.5066343Z 2024-12-18T01:46:59.5066525Z Running cpp/atest 1/1 ... [2024-12-18 01:46:59.506299] 2024-12-18T01:46:59.5066922Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:46:59.5068501Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c0026bdba2822882.xml', '-x', '--reruns=2'] ... [2024-12-18 01:46:59.506639] 2024-12-18T01:47:01.1237718Z 2024-12-18T01:47:01.1238857Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_0451150a69a911dc_.log 2024-12-18T01:47:01.1239469Z 2024-12-18T01:47:01.1239635Z Running cpp/basic 1/1 ... [2024-12-18 01:47:01.123620] 2024-12-18T01:47:01.1240028Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:01.1242695Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9794a65a332d78e5.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:01.124010] 2024-12-18T01:47:02.7413501Z 2024-12-18T01:47:02.7414377Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_88666cc8dcd02c84_.log 2024-12-18T01:47:02.7414949Z 2024-12-18T01:47:02.7415166Z Running cpp/broadcast_test 1/1 ... [2024-12-18 01:47:02.741190] 2024-12-18T01:47:02.7415610Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:02.7417806Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9d6c1f41f4073df6.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:02.741551] 2024-12-18T01:47:04.4088824Z 2024-12-18T01:47:04.4090045Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_fc9b06d520cefe9f_.log 2024-12-18T01:47:04.4090694Z 2024-12-18T01:47:04.4090950Z Running cpp/cpu_generator_test 1/1 ... [2024-12-18 01:47:04.408710] 2024-12-18T01:47:04.4091522Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:04.4093045Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3e4a15dbaf0075a9.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:04.409038] 2024-12-18T01:47:06.0260193Z 2024-12-18T01:47:06.0261462Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_8ac2c290d06a40cb_.log 2024-12-18T01:47:06.0262928Z 2024-12-18T01:47:06.0263294Z Running cpp/dlconvertor_test 1/1 ... [2024-12-18 01:47:06.025846] 2024-12-18T01:47:06.0263964Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:06.0265168Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-65b235cead2fed73.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:06.026170] 2024-12-18T01:47:07.6432482Z 2024-12-18T01:47:07.6433543Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_b9418f183bf4f252_.log 2024-12-18T01:47:07.6434452Z 2024-12-18T01:47:07.6434684Z Running cpp/extension_backend_test 1/1 ... [2024-12-18 01:47:07.643069] 2024-12-18T01:47:07.6435156Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:07.6436886Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-19b645e4c81ce976.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:07.643436] 2024-12-18T01:47:09.2105622Z 2024-12-18T01:47:09.2106884Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_d067c4d558177028_.log 2024-12-18T01:47:09.2107613Z 2024-12-18T01:47:09.2107834Z Running cpp/lazy_tensor_test 1/1 ... [2024-12-18 01:47:09.210427] 2024-12-18T01:47:09.2108248Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:09.2109900Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-36c7ce86ad2d4720.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:09.210770] 2024-12-18T01:47:10.8278190Z 2024-12-18T01:47:10.8279223Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_16c6120cfefbbba0_.log 2024-12-18T01:47:10.8279878Z 2024-12-18T01:47:10.8280100Z Running cpp/legacy_vmap_test 1/1 ... [2024-12-18 01:47:10.827685] 2024-12-18T01:47:10.8280556Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:10.8282975Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-17faaa8dcb8b397e.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:10.828055] 2024-12-18T01:47:12.4452596Z 2024-12-18T01:47:12.4453507Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_73963e723bc3255a_.log 2024-12-18T01:47:12.4454216Z 2024-12-18T01:47:12.4454415Z Running cpp/native_test 1/1 ... [2024-12-18 01:47:12.445100] 2024-12-18T01:47:12.4454837Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:12.4457136Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4f4f336f1699b85d.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:12.445452] 2024-12-18T01:47:14.0624480Z 2024-12-18T01:47:14.0625819Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_9888f29e8e15beec_.log 2024-12-18T01:47:14.0626821Z 2024-12-18T01:47:14.0627139Z Running cpp/operators_test 1/1 ... [2024-12-18 01:47:14.062306] 2024-12-18T01:47:14.0627796Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:14.0629573Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-77b1aefa763fd783.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:14.062691] 2024-12-18T01:47:15.6799694Z 2024-12-18T01:47:15.6800856Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_08fe341eba8df6ed_.log 2024-12-18T01:47:15.6802008Z 2024-12-18T01:47:15.6802315Z Running cpp/scalar_tensor_test 1/1 ... [2024-12-18 01:47:15.679846] 2024-12-18T01:47:15.6803021Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:15.6804375Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-00aff770cda2435e.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:15.680142] 2024-12-18T01:47:17.2973699Z 2024-12-18T01:47:17.2975020Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_5d3e44d6262b51a8_.log 2024-12-18T01:47:17.2975951Z 2024-12-18T01:47:17.2976222Z Running cpp/scalar_test 1/1 ... [2024-12-18 01:47:17.297251] 2024-12-18T01:47:17.2976777Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:17.2978534Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d57dbb5619269bcb.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:17.297565] 2024-12-18T01:47:18.9146739Z 2024-12-18T01:47:18.9148122Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_12741a57dcb2ca88_.log 2024-12-18T01:47:18.9148739Z 2024-12-18T01:47:18.9148975Z Running cpp/tensor_iterator_test 1/1 ... [2024-12-18 01:47:18.914548] 2024-12-18T01:47:18.9149424Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:18.9151721Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-a72851e64a83845e.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:18.914927] 2024-12-18T01:47:20.5321272Z 2024-12-18T01:47:20.5322370Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_a9273be29e48b704_.log 2024-12-18T01:47:20.5323106Z 2024-12-18T01:47:20.5323342Z Running cpp/undefined_tensor_test 1/1 ... [2024-12-18 01:47:20.531988] 2024-12-18T01:47:20.5323804Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:20.5326003Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-bb519014e0ab6ecd.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:20.532354] 2024-12-18T01:47:22.1494361Z 2024-12-18T01:47:22.1495510Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_b8bed8e6df4956b3_.log 2024-12-18T01:47:22.1496251Z 2024-12-18T01:47:22.1496664Z Running cpp/wrapdim_test 1/1 ... [2024-12-18 01:47:22.149275] 2024-12-18T01:47:22.1497220Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:22.1499153Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-53499286572a52e9.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:22.149635] 2024-12-18T01:47:23.7667690Z 2024-12-18T01:47:23.7668604Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_bd70b902d4f8c446_.log 2024-12-18T01:47:23.7669258Z 2024-12-18T01:47:23.7679214Z Running cpp/Dict_test 1/1 ... [2024-12-18 01:47:23.767650] 2024-12-18T01:47:23.7679847Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:23.7681664Z Running cpp/Dimname_test 1/1 ... [2024-12-18 01:47:23.767894] 2024-12-18T01:47:23.7682705Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:23.7683351Z Running cpp/NamedTensor_test 1/1 ... [2024-12-18 01:47:23.767936] 2024-12-18T01:47:23.7684066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:23.7686009Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-8ec06e550667530f.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:23.768289] 2024-12-18T01:47:23.7689503Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-02e6a01783ab6228.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:23.768475] 2024-12-18T01:47:23.7691740Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-526a55a3a422a790.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:23.768516] 2024-12-18T01:47:27.6410600Z 2024-12-18T01:47:27.6411872Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_45d99f9a9720fda1_.log 2024-12-18T01:47:27.6413239Z 2024-12-18T01:47:28.5701722Z 2024-12-18T01:47:28.5703430Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_36bbab4e8aba4113_.log 2024-12-18T01:47:28.5704271Z 2024-12-18T01:47:32.5350376Z Running cpp/apply_utils_test 1/1 ... [2024-12-18 01:47:32.534600] 2024-12-18T01:47:32.5351234Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:32.5356549Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-70a1ef44c1af3192.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:32.535197] 2024-12-18T01:47:32.9592565Z Running cpp/atest 1/1 ... [2024-12-18 01:47:32.958771] 2024-12-18T01:47:32.9593318Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:32.9596596Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c895dcaaf0ce6ab3.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:32.959266] 2024-12-18T01:47:34.0557563Z 2024-12-18T01:47:34.0558788Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_b60727342cb2ea77_.log 2024-12-18T01:47:34.0560041Z 2024-12-18T01:47:36.3775671Z 2024-12-18T01:47:36.3777819Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_60f0ffd6b77f6bea_.log 2024-12-18T01:47:36.3778828Z 2024-12-18T01:47:37.9588030Z Running cpp/basic 1/1 ... [2024-12-18 01:47:37.958325] 2024-12-18T01:47:37.9588688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:37.9591878Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e157b46c79354b41.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:37.958820] 2024-12-18T01:47:38.2542344Z 2024-12-18T01:47:38.2543524Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_495741ebc07ca351_.log 2024-12-18T01:47:38.2544201Z 2024-12-18T01:47:40.4740168Z Running cpp/broadcast_test 1/1 ... [2024-12-18 01:47:40.473583] 2024-12-18T01:47:40.4741018Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:40.4744487Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-adfea19654baf9cf.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:40.474072] 2024-12-18T01:47:41.0290986Z 2024-12-18T01:47:41.0292208Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_c6fa46c82593b3f9_.log 2024-12-18T01:47:41.0293272Z 2024-12-18T01:47:42.0740059Z Running cpp/cpu_generator_test 1/1 ... [2024-12-18 01:47:42.073591] 2024-12-18T01:47:42.0740558Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:42.0743184Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5d253eb605605f8b.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:42.074043] 2024-12-18T01:47:42.8928792Z 2024-12-18T01:47:42.8930612Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_b44d7fa47e75e6f8_.log 2024-12-18T01:47:42.8932878Z 2024-12-18T01:47:44.5858274Z Running cpp/dlconvertor_test 1/1 ... [2024-12-18 01:47:44.585382] 2024-12-18T01:47:44.5859066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:44.5864691Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5559cbb9873551ce.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:44.586020] 2024-12-18T01:47:46.8975761Z 2024-12-18T01:47:46.8977155Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_9c5ecad2e7b508f0_.log 2024-12-18T01:47:46.8978008Z 2024-12-18T01:47:47.2557728Z 2024-12-18T01:47:47.2559004Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_18bca9de20e25fb7_.log 2024-12-18T01:47:47.2560236Z 2024-12-18T01:47:47.2971958Z Running cpp/extension_backend_test 1/1 ... [2024-12-18 01:47:47.296788] 2024-12-18T01:47:47.2972779Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:47.2976469Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4c454623909855f9.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:47.297274] 2024-12-18T01:47:49.7660196Z 2024-12-18T01:47:49.7661665Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_310a0cb5e3e952d9_.log 2024-12-18T01:47:49.7662403Z 2024-12-18T01:47:50.4624242Z Running cpp/lazy_tensor_test 1/1 ... [2024-12-18 01:47:50.462060] 2024-12-18T01:47:50.4624791Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:50.4628201Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-24b7f50ef9aa35d6.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:50.462489] 2024-12-18T01:47:50.7685957Z Running cpp/legacy_vmap_test 1/1 ... [2024-12-18 01:47:50.768215] 2024-12-18T01:47:50.7686770Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:50.7691413Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3ce0bda5632033d3.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:50.768737] 2024-12-18T01:47:53.1319764Z 2024-12-18T01:47:53.1321343Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_31a72bde64f9fd52_.log 2024-12-18T01:47:53.1322477Z 2024-12-18T01:47:53.6528161Z Running cpp/native_test 1/1 ... [2024-12-18 01:47:53.652371] 2024-12-18T01:47:53.6528850Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:53.6533629Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-64c3a2856b3e40d4.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:53.652877] 2024-12-18T01:47:56.6737435Z 2024-12-18T01:47:56.6738997Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_003646f521862b84_.log 2024-12-18T01:47:56.6740206Z 2024-12-18T01:47:57.2451342Z 2024-12-18T01:47:57.2452757Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_d5fe2bf6075cbfe4_.log 2024-12-18T01:47:57.2453611Z 2024-12-18T01:47:57.2926552Z Running cpp/operators_test 1/1 ... [2024-12-18 01:47:57.292270] 2024-12-18T01:47:57.2927301Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:47:57.2930432Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-cd8a3112aef415a4.xml', '-x', '--reruns=2'] ... [2024-12-18 01:47:57.292708] 2024-12-18T01:48:00.0120973Z 2024-12-18T01:48:00.0122618Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_925fbae30087838b_.log 2024-12-18T01:48:00.0123702Z 2024-12-18T01:48:00.4669225Z Running cpp/scalar_tensor_test 1/1 ... [2024-12-18 01:48:00.466485] 2024-12-18T01:48:00.4670080Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:48:00.4672589Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-54d0345979f29ec0.xml', '-x', '--reruns=2'] ... [2024-12-18 01:48:00.466918] 2024-12-18T01:48:00.9218420Z Running cpp/scalar_test 1/1 ... [2024-12-18 01:48:00.921397] 2024-12-18T01:48:00.9219517Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:48:00.9222849Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-8ae594807f7e9ba2.xml', '-x', '--reruns=2'] ... [2024-12-18 01:48:00.921881] 2024-12-18T01:48:03.3369608Z 2024-12-18T01:48:03.3370941Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_95b64905adfe80a6_.log 2024-12-18T01:48:03.3372004Z 2024-12-18T01:48:03.9001993Z Running cpp/tensor_iterator_test 1/1 ... [2024-12-18 01:48:03.899756] 2024-12-18T01:48:03.9003223Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:48:03.9005958Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e06455d76e8a5db9.xml', '-x', '--reruns=2'] ... [2024-12-18 01:48:03.900267] 2024-12-18T01:48:04.1923388Z 2024-12-18T01:48:04.1924393Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_5b42c7785fa33ef2_.log 2024-12-18T01:48:04.1925009Z 2024-12-18T01:48:07.1449428Z Running cpp/undefined_tensor_test 1/1 ... [2024-12-18 01:48:07.144461] 2024-12-18T01:48:07.1450540Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:48:07.1453821Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e64d821265a79e23.xml', '-x', '--reruns=2'] ... [2024-12-18 01:48:07.144969] 2024-12-18T01:48:08.0463527Z Running cpp/wrapdim_test 1/1 ... [2024-12-18 01:48:08.045578] 2024-12-18T01:48:08.0464318Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2024-12-18T01:48:08.0469884Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-df025e85fad1dc52.xml', '-x', '--reruns=2'] ... [2024-12-18 01:48:08.046269] 2024-12-18T01:48:10.2657002Z 2024-12-18T01:48:10.2658354Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_e9c870b39a50f551_.log 2024-12-18T01:48:10.2659531Z 2024-12-18T01:48:11.4187660Z 2024-12-18T01:48:11.4188950Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_9bc3d522d48454f9_.log 2024-12-18T01:48:11.4190118Z 2024-12-18T01:48:16.3882048Z 2024-12-18T01:48:16.3883334Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_9e66800b2933afba_.log 2024-12-18T01:48:16.3884816Z 2024-12-18T01:48:17.1391844Z Running test batch 'tests to run' cost 85.16 seconds 2024-12-18T01:48:17.8158171Z + run_if_exists tensor_interop_test 2024-12-18T01:48:17.8158866Z + local test_name=tensor_interop_test 2024-12-18T01:48:17.8159423Z + [[ -x build/bin/tensor_interop_test ]] 2024-12-18T01:48:17.8160019Z + echo 'Warning: tensor_interop_test does not exist.' 2024-12-18T01:48:17.8160675Z Warning: tensor_interop_test does not exist. 2024-12-18T01:48:17.8161487Z + run_if_exists cudnn_test 2024-12-18T01:48:17.8161967Z + local test_name=cudnn_test 2024-12-18T01:48:17.8162407Z + [[ -x build/bin/cudnn_test ]] 2024-12-18T01:48:17.8162915Z + echo 'Warning: cudnn_test does not exist.' 2024-12-18T01:48:17.8163460Z Warning: cudnn_test does not exist. 2024-12-18T01:48:17.8163976Z + run_if_exists cuda_generator_test 2024-12-18T01:48:17.8164462Z + local test_name=cuda_generator_test 2024-12-18T01:48:17.8164952Z + [[ -x build/bin/cuda_generator_test ]] 2024-12-18T01:48:17.8165534Z + echo 'Warning: cuda_generator_test does not exist.' 2024-12-18T01:48:17.8165988Z Warning: cuda_generator_test does not exist. 2024-12-18T01:48:17.8166516Z + run_if_exists apply_test 2024-12-18T01:48:17.8166820Z + local test_name=apply_test 2024-12-18T01:48:17.8167114Z + [[ -x build/bin/apply_test ]] 2024-12-18T01:48:17.8167445Z + echo 'Warning: apply_test does not exist.' 2024-12-18T01:48:17.8167819Z Warning: apply_test does not exist. 2024-12-18T01:48:17.8168168Z + run_if_exists stream_test 2024-12-18T01:48:17.8168465Z + local test_name=stream_test 2024-12-18T01:48:17.8168814Z + [[ -x build/bin/stream_test ]] 2024-12-18T01:48:17.8169136Z + echo 'Warning: stream_test does not exist.' 2024-12-18T01:48:17.8169541Z Warning: stream_test does not exist. 2024-12-18T01:48:17.8169864Z + run_if_exists cuda_half_test 2024-12-18T01:48:17.8170219Z + local test_name=cuda_half_test 2024-12-18T01:48:17.8170517Z + [[ -x build/bin/cuda_half_test ]] 2024-12-18T01:48:17.8170885Z + echo 'Warning: cuda_half_test does not exist.' 2024-12-18T01:48:17.8171280Z Warning: cuda_half_test does not exist. 2024-12-18T01:48:17.8171639Z + run_if_exists cuda_vectorized_test 2024-12-18T01:48:17.8172004Z + local test_name=cuda_vectorized_test 2024-12-18T01:48:17.8172324Z + [[ -x build/bin/cuda_vectorized_test ]] 2024-12-18T01:48:17.8172765Z + echo 'Warning: cuda_vectorized_test does not exist.' 2024-12-18T01:48:17.8173200Z Warning: cuda_vectorized_test does not exist. 2024-12-18T01:48:17.8173602Z + run_if_exists cuda_distributions_test 2024-12-18T01:48:17.8174000Z + local test_name=cuda_distributions_test 2024-12-18T01:48:17.8174360Z + [[ -x build/bin/cuda_distributions_test ]] 2024-12-18T01:48:17.8174803Z + echo 'Warning: cuda_distributions_test does not exist.' 2024-12-18T01:48:17.8175231Z Warning: cuda_distributions_test does not exist. 2024-12-18T01:48:17.8175652Z + run_if_exists cuda_optional_test 2024-12-18T01:48:17.8175971Z + local test_name=cuda_optional_test 2024-12-18T01:48:17.8176360Z + [[ -x build/bin/cuda_optional_test ]] 2024-12-18T01:48:17.8176728Z + echo 'Warning: cuda_optional_test does not exist.' 2024-12-18T01:48:17.8177172Z Warning: cuda_optional_test does not exist. 2024-12-18T01:48:17.8177643Z + run_if_exists cuda_tensor_interop_test 2024-12-18T01:48:17.8178061Z + local test_name=cuda_tensor_interop_test 2024-12-18T01:48:17.8178422Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2024-12-18T01:48:17.8178878Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2024-12-18T01:48:17.8179359Z Warning: cuda_tensor_interop_test does not exist. 2024-12-18T01:48:17.8179735Z + run_if_exists cuda_complex_test 2024-12-18T01:48:17.8180099Z + local test_name=cuda_complex_test 2024-12-18T01:48:17.8180438Z + [[ -x build/bin/cuda_complex_test ]] 2024-12-18T01:48:17.8180808Z + echo 'Warning: cuda_complex_test does not exist.' 2024-12-18T01:48:17.8181235Z Warning: cuda_complex_test does not exist. 2024-12-18T01:48:17.8181615Z + run_if_exists cuda_complex_math_test 2024-12-18T01:48:17.8181976Z + local test_name=cuda_complex_math_test 2024-12-18T01:48:17.8182447Z + [[ -x build/bin/cuda_complex_math_test ]] 2024-12-18T01:48:17.8191831Z + echo 'Warning: cuda_complex_math_test does not exist.' 2024-12-18T01:48:17.8192354Z Warning: cuda_complex_math_test does not exist. 2024-12-18T01:48:17.8192723Z + run_if_exists cuda_cub_test 2024-12-18T01:48:17.8193047Z + local test_name=cuda_cub_test 2024-12-18T01:48:17.8193402Z + [[ -x build/bin/cuda_cub_test ]] 2024-12-18T01:48:17.8193751Z + echo 'Warning: cuda_cub_test does not exist.' 2024-12-18T01:48:17.8194264Z Warning: cuda_cub_test does not exist. 2024-12-18T01:48:17.8194623Z + run_if_exists cuda_atomic_ops_test 2024-12-18T01:48:17.8194975Z + local test_name=cuda_atomic_ops_test 2024-12-18T01:48:17.8195326Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2024-12-18T01:48:17.8195849Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2024-12-18T01:48:17.8196309Z Warning: cuda_atomic_ops_test does not exist. 2024-12-18T01:48:17.8196641Z + '[' ON == ON ']' 2024-12-18T01:48:17.8197402Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2024-12-18T01:48:17.8483040Z ==5234== Memcheck, a memory error detector 2024-12-18T01:48:17.8483544Z ==5234== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2024-12-18T01:48:17.8484172Z ==5234== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2024-12-18T01:48:17.8484741Z ==5234== Command: build/bin/basic --gtest_filter=-*CUDA 2024-12-18T01:48:17.8485104Z ==5234== 2024-12-18T01:48:18.3488175Z ==5234== Warning: set address range perms: large range [0x4a08000, 0x1548c000) (defined) 2024-12-18T01:48:46.2197236Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2024-12-18T01:48:46.2472088Z Note: Google Test filter = -*CUDA 2024-12-18T01:48:46.2519006Z [==========] Running 4 tests from 1 test suite. 2024-12-18T01:48:46.2545807Z [----------] Global test environment set-up. 2024-12-18T01:48:46.2616827Z [----------] 4 tests from BasicTest 2024-12-18T01:48:46.2640640Z [ RUN ] BasicTest.BasicTestCPU 2024-12-18T01:48:47.6608030Z 357 ms 2024-12-18T01:48:47.7445225Z 53 ms 2024-12-18T01:48:47.8174185Z 64 ms 2024-12-18T01:48:48.4809169Z [ OK ] BasicTest.BasicTestCPU (2214 ms) 2024-12-18T01:48:48.4819046Z [ RUN ] BasicTest.BasicTestHalfCPU 2024-12-18T01:48:48.6116175Z 81 ms 2024-12-18T01:48:48.6622097Z 45 ms 2024-12-18T01:48:48.7285943Z 64 ms 2024-12-18T01:48:48.7822591Z [ OK ] BasicTest.BasicTestHalfCPU (296 ms) 2024-12-18T01:48:48.7823040Z [ RUN ] BasicTest.FactoryMethodsTest 2024-12-18T01:48:48.8217514Z [ OK ] BasicTest.FactoryMethodsTest (39 ms) 2024-12-18T01:48:48.8217970Z [ RUN ] BasicTest.BasicStdTestCPU 2024-12-18T01:48:48.9465523Z Simple example: called once 2024-12-18T01:48:48.9971836Z throw: call_once will retry 2024-12-18T01:48:49.0394407Z throw: call_once will retry 2024-12-18T01:48:49.0399121Z Didn't throw, call_once will not attempt again 2024-12-18T01:48:49.0418902Z [ OK ] BasicTest.BasicStdTestCPU (220 ms) 2024-12-18T01:48:49.0442580Z [----------] 4 tests from BasicTest (2778 ms total) 2024-12-18T01:48:49.0443202Z 2024-12-18T01:48:49.0456616Z [----------] Global test environment tear-down 2024-12-18T01:48:49.0488308Z [==========] 4 tests from 1 test suite ran. (2804 ms total) 2024-12-18T01:48:49.0499341Z [ PASSED ] 4 tests. 2024-12-18T01:48:50.9304448Z ==5234== 2024-12-18T01:48:50.9308447Z ==5234== HEAP SUMMARY: 2024-12-18T01:48:50.9308814Z ==5234== in use at exit: 240,168 bytes in 3,998 blocks 2024-12-18T01:48:50.9309325Z ==5234== total heap usage: 740,937 allocs, 736,939 frees, 214,854,752 bytes allocated 2024-12-18T01:48:50.9309823Z ==5234== 2024-12-18T01:48:50.9683581Z ==5234== LEAK SUMMARY: 2024-12-18T01:48:50.9683924Z ==5234== definitely lost: 0 bytes in 0 blocks 2024-12-18T01:48:50.9684310Z ==5234== indirectly lost: 0 bytes in 0 blocks 2024-12-18T01:48:50.9684671Z ==5234== possibly lost: 0 bytes in 0 blocks 2024-12-18T01:48:50.9685328Z ==5234== still reachable: 240,168 bytes in 3,998 blocks 2024-12-18T01:48:50.9685734Z ==5234== suppressed: 0 bytes in 0 blocks 2024-12-18T01:48:50.9686193Z ==5234== Rerun with --leak-check=full to see details of leaked memory 2024-12-18T01:48:50.9686602Z ==5234== 2024-12-18T01:48:50.9686918Z ==5234== For lists of detected and suppressed errors, rerun with: -s 2024-12-18T01:48:50.9687445Z ==5234== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2024-12-18T01:48:51.0140671Z + [[ -x build/bin/tensor_interop_test ]] 2024-12-18T01:48:51.0143038Z + [[ -n '' ]] 2024-12-18T01:48:51.0143381Z + assert_git_not_dirty 2024-12-18T01:48:51.0143706Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2024-12-18T01:48:51.0144134Z + [[ linux-focal-py3.13-clang10 != *xla* ]] 2024-12-18T01:48:51.0148930Z ++ git status --porcelain 2024-12-18T01:48:51.0150106Z ++ grep -v '?? third_party' 2024-12-18T01:48:51.2103503Z ++ true 2024-12-18T01:48:51.2104144Z + git_status= 2024-12-18T01:48:51.2104493Z + [[ -n '' ]] 2024-12-18T01:48:51.2105969Z + cleanup_workspace 2024-12-18T01:48:51.2106814Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2024-12-18T01:48:51.2107640Z sudo may print the following warning message that can be ignored. The chown command will still run. 2024-12-18T01:48:51.2108282Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2024-12-18T01:48:51.2108757Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2024-12-18T01:48:51.2109327Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2024-12-18T01:48:51.2110036Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2024-12-18T01:48:51.2110533Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2024-12-18T01:48:53.9735890Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@release/2.6 2024-12-18T01:48:53.9736423Z with: 2024-12-18T01:48:53.9736698Z benchmark-results-dir: test/test-reports 2024-12-18T01:48:53.9737050Z dry-run: false 2024-12-18T01:48:53.9737305Z schema-version: v3 2024-12-18T01:48:53.9737750Z github-token: *** 2024-12-18T01:48:53.9738011Z env: 2024-12-18T01:48:53.9738228Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:53.9738707Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:53.9739219Z ##[endgroup] 2024-12-18T01:48:53.9773150Z ##[group]Run set -eux 2024-12-18T01:48:53.9773438Z set -eux 2024-12-18T01:48:53.9773713Z python3 -mpip install boto3==1.35.33 2024-12-18T01:48:53.9824560Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:53.9824965Z env: 2024-12-18T01:48:53.9825197Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:53.9825732Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:53.9826231Z ##[endgroup] 2024-12-18T01:48:53.9855233Z + python3 -mpip install boto3==1.35.33 2024-12-18T01:48:54.6394311Z Defaulting to user installation because normal site-packages is not writeable 2024-12-18T01:48:54.6707148Z Requirement already satisfied: boto3==1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (1.35.33) 2024-12-18T01:48:54.6760626Z Requirement already satisfied: botocore<1.36.0,>=1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (1.35.83) 2024-12-18T01:48:54.6765096Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.4) 2024-12-18T01:48:54.6769556Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0) 2024-12-18T01:48:54.6821859Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.1) 2024-12-18T01:48:54.6833562Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10) 2024-12-18T01:48:54.6878983Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0) 2024-12-18T01:48:54.8347423Z ##[group]Run set -eux 2024-12-18T01:48:54.8347736Z set -eux 2024-12-18T01:48:54.8347985Z  2024-12-18T01:48:54.8348246Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2024-12-18T01:48:54.8348618Z  echo "Missing github-token input" 2024-12-18T01:48:54.8348938Z  exit 1 2024-12-18T01:48:54.8349174Z fi 2024-12-18T01:48:54.8354745Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:54.8355143Z env: 2024-12-18T01:48:54.8355373Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:54.8355980Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:54.8356784Z GITHUB_TOKEN: *** 2024-12-18T01:48:54.8357037Z ##[endgroup] 2024-12-18T01:48:54.8380561Z + [[ -z *** ]] 2024-12-18T01:48:54.8451310Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2024-12-18T01:48:54.8451764Z with: 2024-12-18T01:48:54.8452145Z github-token: *** 2024-12-18T01:48:54.8452390Z env: 2024-12-18T01:48:54.8452613Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:54.8453082Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:54.8453588Z ##[endgroup] 2024-12-18T01:48:54.8477448Z ##[group]Run set -eux 2024-12-18T01:48:54.8477729Z set -eux 2024-12-18T01:48:54.8477970Z  2024-12-18T01:48:54.8478450Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2024-12-18T01:48:54.8484171Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:54.8484563Z env: 2024-12-18T01:48:54.8484791Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:54.8485268Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:54.8486010Z GITHUB_TOKEN: *** 2024-12-18T01:48:54.8486262Z ##[endgroup] 2024-12-18T01:48:54.8509614Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 12383255652 i-0897f70f52bdfd343 2024-12-18T01:48:57.5009427Z setting job-id=34566067022 2024-12-18T01:48:57.5010464Z setting job-name=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-18T01:48:57.5127694Z ##[group]Run set -eux 2024-12-18T01:48:57.5128002Z set -eux 2024-12-18T01:48:57.5128245Z  2024-12-18T01:48:57.5128651Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2024-12-18T01:48:57.5129198Z  --schema-version "${SCHEMA_VERSION}" \ 2024-12-18T01:48:57.5129562Z  --repo "${REPO}" \ 2024-12-18T01:48:57.5129882Z  --head-branch "${HEAD_BRANCH}" \ 2024-12-18T01:48:57.5130231Z  --head-sha "${HEAD_SHA}" \ 2024-12-18T01:48:57.5130595Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2024-12-18T01:48:57.5130955Z  --run-attempt "${RUN_ATTEMPT}" \ 2024-12-18T01:48:57.5131407Z  --job-id "${JOB_ID}" \ 2024-12-18T01:48:57.5131722Z  --job-name "${JOB_NAME}" 2024-12-18T01:48:57.5137406Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:57.5137803Z env: 2024-12-18T01:48:57.5138040Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:57.5138507Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:57.5139026Z SCHEMA_VERSION: v3 2024-12-18T01:48:57.5139295Z REPO: pytorch/pytorch 2024-12-18T01:48:57.5139583Z HEAD_BRANCH: refs/heads/release/2.6 2024-12-18T01:48:57.5139968Z HEAD_SHA: 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 2024-12-18T01:48:57.5140330Z WORKFLOW_RUN_ID: 12383255652 2024-12-18T01:48:57.5140616Z RUN_ATTEMPT: 1 2024-12-18T01:48:57.5140862Z JOB_ID: 34566067022 2024-12-18T01:48:57.5141369Z JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge) 2024-12-18T01:48:57.5141852Z ##[endgroup] 2024-12-18T01:48:57.5167137Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/release/2.6/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/release/2.6 --head-sha 0cdf8b1d09254cfda66191d1bd01e3041c3c76f7 --workflow-id 12383255652 --run-attempt 1 --job-id 34566067022 --job-name 'linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)' 2024-12-18T01:48:57.5498592Z ##[group]Run set -eux 2024-12-18T01:48:57.5498888Z set -eux 2024-12-18T01:48:57.5499121Z  2024-12-18T01:48:57.5499405Z # TODO (huydhn): Implement this part 2024-12-18T01:48:57.5499795Z echo "runners=[]" >> "${GITHUB_OUTPUT}" 2024-12-18T01:48:57.5505350Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:57.5505756Z env: 2024-12-18T01:48:57.5505993Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:57.5506461Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:57.5506979Z ##[endgroup] 2024-12-18T01:48:57.5528613Z + echo 'runners=[]' 2024-12-18T01:48:57.5566212Z ##[group]Run set -eux 2024-12-18T01:48:57.5566680Z set -eux 2024-12-18T01:48:57.5567062Z  2024-12-18T01:48:57.5567498Z # TODO (huydhn): Implement this part 2024-12-18T01:48:57.5568164Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2024-12-18T01:48:57.5575837Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:57.5576472Z env: 2024-12-18T01:48:57.5576822Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:57.5577797Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:57.5578708Z ##[endgroup] 2024-12-18T01:48:57.5606178Z + echo 'dependencies={}' 2024-12-18T01:48:57.5915209Z ##[group]Run set -eux 2024-12-18T01:48:57.5915526Z set -eux 2024-12-18T01:48:57.5915866Z  2024-12-18T01:48:57.5916152Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2024-12-18T01:48:57.5916627Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2024-12-18T01:48:57.5917118Z  # We don't want the job to fail if the directory doesn't exist 2024-12-18T01:48:57.5936410Z  exit 0 2024-12-18T01:48:57.5936728Z fi 2024-12-18T01:48:57.5936974Z  2024-12-18T01:48:57.5937235Z if [[ "${DRY_RUN}" == "true" ]]; then 2024-12-18T01:48:57.5937749Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2024-12-18T01:48:57.5938325Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2024-12-18T01:48:57.5938784Z  --metadata "${BENCHMARK_METADATA}" \ 2024-12-18T01:48:57.5939157Z  --runners "${RUNNER_INFO}" \ 2024-12-18T01:48:57.5939525Z  --dependencies "${DEPENDENCIES}" \ 2024-12-18T01:48:57.5939882Z  --dry-run 2024-12-18T01:48:57.5940151Z else 2024-12-18T01:48:57.5940547Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2024-12-18T01:48:57.5941221Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2024-12-18T01:48:57.5941666Z  --metadata "${BENCHMARK_METADATA}" \ 2024-12-18T01:48:57.5942037Z  --runners "${RUNNER_INFO}" \ 2024-12-18T01:48:57.5942395Z  --dependencies "${DEPENDENCIES}" 2024-12-18T01:48:57.5942730Z fi 2024-12-18T01:48:57.5947896Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:57.5948279Z env: 2024-12-18T01:48:57.5948519Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:57.5949007Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:57.5949564Z BENCHMARK_RESULTS_DIR: test/test-reports 2024-12-18T01:48:57.5949904Z DRY_RUN: false 2024-12-18T01:48:57.5951315Z BENCHMARK_METADATA: {"timestamp": 1734486537, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/release/2.6", "head_sha": "0cdf8b1d09254cfda66191d1bd01e3041c3c76f7", "workflow_id": 12383255652, "run_attempt": 1, "job_id": 34566067022} 2024-12-18T01:48:57.5952714Z RUNNER_INFO: [] 2024-12-18T01:48:57.5952960Z DEPENDENCIES: {} 2024-12-18T01:48:57.5953218Z ##[endgroup] 2024-12-18T01:48:57.5974859Z + [[ ! -d test/test-reports ]] 2024-12-18T01:48:57.5975429Z + [[ false == \t\r\u\e ]] 2024-12-18T01:48:57.5978597Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/release/2.6/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1734486537, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/release/2.6", "head_sha": "0cdf8b1d09254cfda66191d1bd01e3041c3c76f7", "workflow_id": 12383255652, "run_attempt": 1, "job_id": 34566067022}' --runners '[]' --dependencies '{}' 2024-12-18T01:48:57.8696380Z ##[group]Run cat test/**/*_toprint.log || true 2024-12-18T01:48:57.8696807Z cat test/**/*_toprint.log || true 2024-12-18T01:48:57.8702669Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:57.8703066Z env: 2024-12-18T01:48:57.8703295Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:57.8703772Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:57.8704284Z ##[endgroup] 2024-12-18T01:48:57.8769179Z cat: 'test/**/*_toprint.log': No such file or directory 2024-12-18T01:48:57.8806449Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2024-12-18T01:48:57.8806831Z kill "$MONITOR_SCRIPT_PID" 2024-12-18T01:48:57.8812511Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:57.8812916Z env: 2024-12-18T01:48:57.8813140Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:57.8813620Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:57.8814156Z MONITOR_SCRIPT_PID: 231719 2024-12-18T01:48:57.8814445Z ##[endgroup] 2024-12-18T01:48:57.8992268Z Prepare all required actions 2024-12-18T01:48:57.8992978Z Getting action download info 2024-12-18T01:48:58.0500974Z Download action repository 'actions/upload-artifact@v4' (SHA:6f51ac03b9356f520e9adb1b1b7802705f340c2b) 2024-12-18T01:48:58.3631771Z ##[group]Run ./.github/actions/upload-test-artifacts 2024-12-18T01:48:58.3632166Z with: 2024-12-18T01:48:58.3632492Z file-suffix: test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-18T01:48:58.3632935Z s3-bucket: gha-artifacts 2024-12-18T01:48:58.3633212Z env: 2024-12-18T01:48:58.3633439Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:58.3633914Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:58.3634424Z ##[endgroup] 2024-12-18T01:48:58.3662216Z ##[group]Run # Remove any previous test jsons if they exist 2024-12-18T01:48:58.3662698Z # Remove any previous test jsons if they exist 2024-12-18T01:48:58.3663186Z rm -f test-jsons-*.zip 2024-12-18T01:48:58.3663673Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2024-12-18T01:48:58.3669313Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:58.3669697Z env: 2024-12-18T01:48:58.3669928Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:58.3670410Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:58.3671025Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-18T01:48:58.3671429Z ##[endgroup] 2024-12-18T01:48:58.3806656Z adding: test/test-reports/td_exclusions-f6d8e74afd18f6266420.json (deflated 81%) 2024-12-18T01:48:58.3807744Z adding: test/test-reports/td_exclusions-873c57749b3f385edec2.json (deflated 73%) 2024-12-18T01:48:58.3851454Z ##[group]Run # Remove any previous test reports if they exist 2024-12-18T01:48:58.3851955Z # Remove any previous test reports if they exist 2024-12-18T01:48:58.3852363Z rm -f test-reports-*.zip 2024-12-18T01:48:58.3852864Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2024-12-18T01:48:58.3858580Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:58.3858957Z env: 2024-12-18T01:48:58.3859190Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:58.3859663Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:58.3860277Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-18T01:48:58.3860679Z ##[endgroup] 2024-12-18T01:48:58.4007798Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-cb40866da58c9eaf.xml (deflated 98%) 2024-12-18T01:48:58.4008743Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-af91440e1dacc574.xml (deflated 36%) 2024-12-18T01:48:58.4013417Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-fe3893e38ff375cb.xml (deflated 94%) 2024-12-18T01:48:58.4014839Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-859afbcdc1188212.xml (deflated 90%) 2024-12-18T01:48:58.4022217Z adding: test/test-reports/python-pytest/test_spectral_ops/test_spectral_ops-81eee51950a1c871.xml (deflated 95%) 2024-12-18T01:48:58.4023691Z adding: test/test-reports/python-pytest/test_cpp_extensions_aot_no_ninja/test_cpp_extensions_aot_no_ninja-0e369ca3dd481e10.xml (deflated 90%) 2024-12-18T01:48:58.4024836Z adding: test/test-reports/python-pytest/test_show_pickle/test_show_pickle-6daf7eecf9e5d047.xml (deflated 37%) 2024-12-18T01:48:58.4025884Z adding: test/test-reports/python-pytest/test_namedtuple_return_api/test_namedtuple_return_api-37e9ad3d2e28250a.xml (deflated 72%) 2024-12-18T01:48:58.4026868Z adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-2e6290bf7462bef6.xml (deflated 56%) 2024-12-18T01:48:58.4027760Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-7bf33ebdf0701641.xml (deflated 86%) 2024-12-18T01:48:58.4060056Z adding: test/test-reports/python-pytest/test_torch/test_torch-15e074ccb9d745a2.xml (deflated 95%) 2024-12-18T01:48:58.4062146Z adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-df282b42c1919e05.xml (deflated 89%) 2024-12-18T01:48:58.4063853Z adding: test/test-reports/python-pytest/test_native_mha/test_native_mha-2cb7fba020d350ba.xml (deflated 95%) 2024-12-18T01:48:58.4066519Z adding: test/test-reports/python-pytest/test_sort_and_select/test_sort_and_select-970481b0b255db8d.xml (deflated 90%) 2024-12-18T01:48:58.4068861Z adding: test/test-reports/python-pytest/nn.test_pooling/nn.test_pooling-c5130c1ff98d2be6.xml (deflated 89%) 2024-12-18T01:48:58.4073446Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-68a026d3ffd7dc52.xml (deflated 92%) 2024-12-18T01:48:58.4074438Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-3b2068afe02e9cd4.xml (deflated 37%) 2024-12-18T01:48:58.4078173Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-6e7bf2ceb3aacf81.xml (deflated 94%) 2024-12-18T01:48:58.4079266Z adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-cfea2239d9acb712.xml (deflated 58%) 2024-12-18T01:48:58.4096088Z adding: test/test-reports/python-pytest/nn.test_convolution/nn.test_convolution-a21a31871266ca30.xml (deflated 97%) 2024-12-18T01:48:58.4131145Z adding: test/test-reports/python-pytest/test_nn/test_nn-e7dc4eaceee2b574.xml (deflated 95%) 2024-12-18T01:48:58.4161761Z adding: test/test-reports/python-pytest/test_nn/test_nn-b44eace188f53098.xml (deflated 96%) 2024-12-18T01:48:58.4163627Z adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-b52fb6f021c3dedb.xml (deflated 94%) 2024-12-18T01:48:58.4187550Z adding: test/test-reports/python-pytest/test_overrides/test_overrides-21ec1ce97df8b7a8.xml (deflated 96%) 2024-12-18T01:48:58.4190741Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-1a69e74e38b20ed6.xml (deflated 94%) 2024-12-18T01:48:58.4193185Z adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-7c8328720f0bd3ec.xml (deflated 93%) 2024-12-18T01:48:58.4194275Z adding: test/test-reports/python-pytest/test_quantization/test_quantization-3910bdf5924440c1.xml (deflated 28%) 2024-12-18T01:48:58.4218060Z adding: test/test-reports/python-pytest/test_quantization/test_quantization-f04575a0920a9f2f.xml (deflated 94%) 2024-12-18T01:48:58.4219193Z adding: test/test-reports/python-pytest/profiler.test_record_function/profiler.test_record_function-a8a20a4018f0cf28.xml (deflated 28%) 2024-12-18T01:48:58.4220465Z adding: test/test-reports/python-pytest/profiler.test_record_function/profiler.test_record_function-6bf499001e32e0ee.xml (deflated 88%) 2024-12-18T01:48:58.4221801Z adding: test/test-reports/python-pytest/profiler.test_execution_trace/profiler.test_execution_trace-4e99dda898f8620e.xml (deflated 28%) 2024-12-18T01:48:58.4223152Z adding: test/test-reports/python-pytest/profiler.test_execution_trace/profiler.test_execution_trace-f20f5e0798f48d7d.xml (deflated 86%) 2024-12-18T01:48:58.4224295Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-07e8f8257bfa05b5.xml (deflated 29%) 2024-12-18T01:48:58.4225299Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-0cf67d2b2d9bd743.xml (deflated 29%) 2024-12-18T01:48:58.4226222Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-49ae1d9f0caae99b.xml (deflated 29%) 2024-12-18T01:48:58.4227096Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-62be609d6ebf6105.xml (deflated 29%) 2024-12-18T01:48:58.4228034Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-c0026bdba2822882.xml (deflated 29%) 2024-12-18T01:48:58.4228988Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-9794a65a332d78e5.xml (deflated 29%) 2024-12-18T01:48:58.4230085Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-9d6c1f41f4073df6.xml (deflated 29%) 2024-12-18T01:48:58.4231009Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-3e4a15dbaf0075a9.xml (deflated 29%) 2024-12-18T01:48:58.4231973Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-65b235cead2fed73.xml (deflated 29%) 2024-12-18T01:48:58.4232924Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-19b645e4c81ce976.xml (deflated 29%) 2024-12-18T01:48:58.4233803Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-36c7ce86ad2d4720.xml (deflated 29%) 2024-12-18T01:48:58.4234765Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-17faaa8dcb8b397e.xml (deflated 29%) 2024-12-18T01:48:58.4235610Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-4f4f336f1699b85d.xml (deflated 29%) 2024-12-18T01:48:58.4236613Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-77b1aefa763fd783.xml (deflated 29%) 2024-12-18T01:48:58.4237458Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-00aff770cda2435e.xml (deflated 29%) 2024-12-18T01:48:58.4238576Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-d57dbb5619269bcb.xml (deflated 29%) 2024-12-18T01:48:58.4239931Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-a72851e64a83845e.xml (deflated 30%) 2024-12-18T01:48:58.4240762Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-bb519014e0ab6ecd.xml (deflated 29%) 2024-12-18T01:48:58.4241606Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-53499286572a52e9.xml (deflated 29%) 2024-12-18T01:48:58.4242439Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-526a55a3a422a790.xml (deflated 57%) 2024-12-18T01:48:58.4243345Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-02e6a01783ab6228.xml (deflated 72%) 2024-12-18T01:48:58.4244195Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-8ec06e550667530f.xml (deflated 84%) 2024-12-18T01:48:58.4245043Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-70a1ef44c1af3192.xml (deflated 67%) 2024-12-18T01:48:58.4245890Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-c895dcaaf0ce6ab3.xml (deflated 78%) 2024-12-18T01:48:58.4246728Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e157b46c79354b41.xml (deflated 61%) 2024-12-18T01:48:58.4247570Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-adfea19654baf9cf.xml (deflated 37%) 2024-12-18T01:48:58.4248414Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-5d253eb605605f8b.xml (deflated 79%) 2024-12-18T01:48:58.4249251Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-5559cbb9873551ce.xml (deflated 50%) 2024-12-18T01:48:58.4250083Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-4c454623909855f9.xml (deflated 35%) 2024-12-18T01:48:58.4250924Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-24b7f50ef9aa35d6.xml (deflated 46%) 2024-12-18T01:48:58.4251763Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-64c3a2856b3e40d4.xml (deflated 46%) 2024-12-18T01:48:58.4252589Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-3ce0bda5632033d3.xml (deflated 83%) 2024-12-18T01:48:58.4253444Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-cd8a3112aef415a4.xml (deflated 58%) 2024-12-18T01:48:58.4254280Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-54d0345979f29ec0.xml (deflated 57%) 2024-12-18T01:48:58.4255119Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-8ae594807f7e9ba2.xml (deflated 59%) 2024-12-18T01:48:58.4255957Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e64d821265a79e23.xml (deflated 37%) 2024-12-18T01:48:58.4256866Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-df025e85fad1dc52.xml (deflated 37%) 2024-12-18T01:48:58.4257704Z adding: test/test-reports/python-pytest/test.run_test/test.run_test-e06455d76e8a5db9.xml (deflated 90%) 2024-12-18T01:48:58.4258652Z adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241218013643.xml (deflated 42%) 2024-12-18T01:48:58.4259721Z adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241218013706.xml (deflated 43%) 2024-12-18T01:48:58.4284370Z ##[group]Run # Remove any previous usage logs if they exist 2024-12-18T01:48:58.4284846Z # Remove any previous usage logs if they exist 2024-12-18T01:48:58.4285225Z rm -f logs-*.zip 2024-12-18T01:48:58.4285727Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2024-12-18T01:48:58.4286283Z # so check to see if the file exists first 2024-12-18T01:48:58.4286697Z if [ -f 'usage_log.txt' ]; then 2024-12-18T01:48:58.4287098Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2024-12-18T01:48:58.4287459Z fi 2024-12-18T01:48:58.4287841Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then 2024-12-18T01:48:58.4288405Z  zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' 2024-12-18T01:48:58.4288815Z fi 2024-12-18T01:48:58.4294138Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:58.4294533Z env: 2024-12-18T01:48:58.4294774Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:58.4295255Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:58.4295874Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-18T01:48:58.4296328Z ##[endgroup] 2024-12-18T01:48:58.4466980Z adding: usage_log.txt (deflated 98%) 2024-12-18T01:48:58.4643828Z adding: test/test-reports/test_reductions_1.1_7af9e76d08b9736e_.log (deflated 96%) 2024-12-18T01:48:58.4645105Z adding: test/test-reports/test_cuda_nvml_based_avail_1.1_ccfb4ec03e3cabba_.log (deflated 11%) 2024-12-18T01:48:58.4645821Z adding: test/test-reports/test_cuda_primary_ctx_1.1_d44231bce6f346af_.log (deflated 11%) 2024-12-18T01:48:58.4646556Z adding: test/test-reports/test_cpp_extensions_aot_ninja_1.1_26639d44ab7aa4ea_.log (deflated 75%) 2024-12-18T01:48:58.4652483Z adding: test/test-reports/test_spectral_ops_1.1_7c0c9529e06ef087_.log (deflated 93%) 2024-12-18T01:48:58.4653809Z adding: test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_4cbfa79f4be90361_.log (deflated 76%) 2024-12-18T01:48:58.4655212Z adding: test/test-reports/test_show_pickle_1.1_8ccadbdb210ccbf7_.log (deflated 50%) 2024-12-18T01:48:58.4656358Z adding: test/test-reports/test_namedtuple_return_api_1.1_41ef5f9a1245ff5c_.log (deflated 64%) 2024-12-18T01:48:58.4657063Z adding: test/test-reports/test_jit_disabled_1.1_c62a55a63828c53e_.log (deflated 57%) 2024-12-18T01:48:58.4657707Z adding: test/test-reports/test_autocast_1.1_e14ad2157391298e_.log (deflated 77%) 2024-12-18T01:48:58.4694711Z adding: test/test-reports/test_torch_1.1_e6fbf3520a3b0c44_.log (deflated 92%) 2024-12-18T01:48:58.4696315Z adding: test/test-reports/test_multiprocessing_1.1_22da63aa03784453_.log (deflated 84%) 2024-12-18T01:48:58.4697566Z adding: test/test-reports/test_native_mha_1.1_7541010acb5e9bfd_.log (deflated 90%) 2024-12-18T01:48:58.4704745Z adding: test/test-reports/test_sort_and_select_1.1_8404e660e11ba3fe_.log (deflated 93%) 2024-12-18T01:48:58.4709249Z adding: test/test-reports/nn.test_pooling_1.1_ee9bb53316ce7ead_.log (deflated 88%) 2024-12-18T01:48:58.4717783Z adding: test/test-reports/test_python_dispatch_1.1_94b4652ac9ba6f1f_.log (deflated 87%) 2024-12-18T01:48:58.4718925Z adding: test/test-reports/test_mobile_optimizer_1.1_2747b671e8e74458_.log (deflated 66%) 2024-12-18T01:48:58.4733962Z adding: test/test-reports/nn.test_convolution_1.1_03fd51b60c87c27d_.log (deflated 95%) 2024-12-18T01:48:58.4769416Z adding: test/test-reports/test_nn_1.2_b81b739b728fcfaa_.log (deflated 93%) 2024-12-18T01:48:58.4802582Z adding: test/test-reports/test_nn_2.2_045b646911bad259_.log (deflated 94%) 2024-12-18T01:48:58.4803867Z adding: test/test-reports/test_multiprocessing_spawn_1.1_2ce599322796c149_.log (deflated 82%) 2024-12-18T01:48:58.4831583Z adding: test/test-reports/test_overrides_1.1_e7f29296011a3936_.log (deflated 92%) 2024-12-18T01:48:58.4835394Z adding: test/test-reports/distributions.test_distributions_1.2_95697e8b5c2a0833_.log (deflated 90%) 2024-12-18T01:48:58.4838359Z adding: test/test-reports/distributions.test_distributions_2.2_439243b68ee6db67_.log (deflated 89%) 2024-12-18T01:48:58.4839722Z adding: test/test-reports/test_cuda_expandable_segments_1.1_cd18d0a83354f153_.log (stored 0%) 2024-12-18T01:48:58.4840999Z adding: test/test-reports/dynamo.test_higher_order_ops_1.1_e4b4bb8d1e7036cf_.log (stored 0%) 2024-12-18T01:48:58.4842381Z adding: test/test-reports/dynamo.test_misc_1.1_f2508ff12b5630c9_.log (stored 0%) 2024-12-18T01:48:58.4843643Z adding: test/test-reports/dynamo.test_frame_init_1.1_bf31cb386694ec65_.log (stored 0%) 2024-12-18T01:48:58.4844779Z adding: test/test-reports/dynamo.test_nops_1.1_bb28e85f83b5c09f_.log (stored 0%) 2024-12-18T01:48:58.4846033Z adding: test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_8d01458ab9aaf8f8_.log (stored 0%) 2024-12-18T01:48:58.4847019Z adding: test/test-reports/dynamo.test_skip_non_tensor_1.1_13e1bb7a1cc8ca22_.log (stored 0%) 2024-12-18T01:48:58.4847854Z adding: test/test-reports/dynamo.test_reconstruct_1.1_d9ed2f749ba54c4d_.log (stored 0%) 2024-12-18T01:48:58.4849078Z adding: test/test-reports/dynamo.test_sdpa_1.1_99688460facd71ca_.log (stored 0%) 2024-12-18T01:48:58.4850239Z adding: test/test-reports/dynamo.test_recompiles_1.1_df546cbc9b80e3dc_.log (stored 0%) 2024-12-18T01:48:58.4851603Z adding: test/test-reports/dynamo.test_pre_dispatch_1.1_1c1f26b5ae986dde_.log (stored 0%) 2024-12-18T01:48:58.4852712Z adding: test/test-reports/dynamo.test_cudagraphs_1.1_781b2edcc0504a06_.log (stored 0%) 2024-12-18T01:48:58.4853781Z adding: test/test-reports/dynamo.test_graph_region_tracker_1.1_07c2318493b93ad7_.log (stored 0%) 2024-12-18T01:48:58.4854550Z adding: test/test-reports/dynamo.test_deviceguard_1.1_d790fc3c87ae2b32_.log (stored 0%) 2024-12-18T01:48:58.4855217Z adding: test/test-reports/dynamo.test_sources_1.1_ecb4f7559435a6f2_.log (stored 0%) 2024-12-18T01:48:58.4855913Z adding: test/test-reports/dynamo.test_structured_trace_1.1_a3145b0245e182b3_.log (stored 0%) 2024-12-18T01:48:58.4856598Z adding: test/test-reports/dynamo.test_modes_1.1_a47af9d805e784d0_.log (stored 0%) 2024-12-18T01:48:58.4857304Z adding: test/test-reports/dynamo.test_graph_deduplication_1.1_fc5d8404dff63cad_.log (stored 0%) 2024-12-18T01:48:58.4858033Z adding: test/test-reports/dynamo.test_ctx_manager_1.1_7c8ef2215af123af_.log (stored 0%) 2024-12-18T01:48:58.4858782Z adding: test/test-reports/dynamo.test_activation_checkpointing_1.1_81df5271d66401c7_.log (stored 0%) 2024-12-18T01:48:58.4859539Z adding: test/test-reports/dynamo.test_trace_rules_1.1_5ad3c73feb19e482_.log (stored 0%) 2024-12-18T01:48:58.4860213Z adding: test/test-reports/dynamo.test_debug_utils_1.1_6a320cb3ec0a6016_.log (stored 0%) 2024-12-18T01:48:58.4860908Z adding: test/test-reports/dynamo.test_bytecode_utils_1.1_0221595e634cc158_.log (stored 0%) 2024-12-18T01:48:58.4861608Z adding: test/test-reports/dynamo.test_recompile_ux_1.1_3293a8238459cb03_.log (stored 0%) 2024-12-18T01:48:58.4862291Z adding: test/test-reports/dynamo.test_minifier_1.1_ec701c6d16eda61d_.log (stored 0%) 2024-12-18T01:48:58.4862959Z adding: test/test-reports/dynamo.test_comptime_1.1_0dfda0c1b758430f_.log (stored 0%) 2024-12-18T01:48:58.4863582Z adding: test/test-reports/test_hub_1.1_0bb1cc5ca2a6542c_.log (stored 0%) 2024-12-18T01:48:58.4864201Z adding: test/test-reports/optim.test_swa_utils_1.1_e00fbeffeec5c2e0_.log (deflated 7%) 2024-12-18T01:48:58.4864976Z adding: test/test-reports/test_quantization_1.4_7050267e5ff70181_.log (deflated 58%) 2024-12-18T01:48:58.4865711Z adding: test/test-reports/profiler.test_record_function_1.1_d62e0cebe0f32883_.log (deflated 52%) 2024-12-18T01:48:58.4866493Z adding: test/test-reports/profiler.test_execution_trace_1.1_065edbdfece9d482_.log (deflated 52%) 2024-12-18T01:48:58.4867243Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_95b64905adfe80a6_.log (deflated 60%) 2024-12-18T01:48:58.4867973Z adding: test/test-reports/dynamo.test_higher_order_ops_1.1_57177bf2f642a2b7_.log (stored 0%) 2024-12-18T01:48:58.4868649Z adding: test/test-reports/dynamo.test_misc_1.1_e935ac09fcf6cdca_.log (stored 0%) 2024-12-18T01:48:58.4869348Z adding: test/test-reports/test_cuda_expandable_segments_1.1_16fbd9c961deda42_.log (stored 0%) 2024-12-18T01:48:58.4870066Z adding: test/test-reports/dynamo.test_frame_init_1.1_0382141dcf6e8e77_.log (stored 0%) 2024-12-18T01:48:58.4870726Z adding: test/test-reports/dynamo.test_nops_1.1_7580fdef940fa74a_.log (stored 0%) 2024-12-18T01:48:58.4871450Z adding: test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_8631972e8af55ee2_.log (stored 0%) 2024-12-18T01:48:58.4872718Z adding: test/test-reports/dynamo.test_skip_non_tensor_1.1_0e031f6df6711327_.log (stored 0%) 2024-12-18T01:48:58.4874019Z adding: test/test-reports/dynamo.test_reconstruct_1.1_c47f76cd70529572_.log (stored 0%) 2024-12-18T01:48:58.4875252Z adding: test/test-reports/dynamo.test_sdpa_1.1_cc61707e92a90f03_.log (stored 0%) 2024-12-18T01:48:58.4876613Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_e9c870b39a50f551_.log (deflated 50%) 2024-12-18T01:48:58.4877950Z adding: test/test-reports/dynamo.test_recompiles_1.1_bd40e72860a2eda5_.log (stored 0%) 2024-12-18T01:48:58.4879241Z adding: test/test-reports/dynamo.test_pre_dispatch_1.1_b076d25b23fbc8ce_.log (stored 0%) 2024-12-18T01:48:58.4880606Z adding: test/test-reports/dynamo.test_cudagraphs_1.1_d57524e3e1262838_.log (stored 0%) 2024-12-18T01:48:58.4881487Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_9e66800b2933afba_.log (deflated 88%) 2024-12-18T01:48:58.4882585Z adding: test/test-reports/dynamo.test_graph_region_tracker_1.1_407b5017edad1fb3_.log (stored 0%) 2024-12-18T01:48:58.4883965Z adding: test/test-reports/dynamo.test_deviceguard_1.1_07fbdb2e3af6318a_.log (stored 0%) 2024-12-18T01:48:58.4885247Z adding: test/test-reports/dynamo.test_sources_1.1_c48d9c27d796b204_.log (stored 0%) 2024-12-18T01:48:58.4886488Z adding: test/test-reports/cpp.scalar_test_1.1_5b42c7785fa33ef2_.log (deflated 59%) 2024-12-18T01:48:58.4887689Z adding: test/test-reports/dynamo.test_structured_trace_1.1_a162f4523b20b9ee_.log (stored 0%) 2024-12-18T01:48:58.4888858Z adding: test/test-reports/dynamo.test_modes_1.1_fbb29f96ca81abeb_.log (stored 0%) 2024-12-18T01:48:58.4889552Z adding: test/test-reports/dynamo.test_graph_deduplication_1.1_1e1a681f1d7b1d48_.log (stored 0%) 2024-12-18T01:48:58.4890264Z adding: test/test-reports/cpp.wrapdim_test_1.1_9bc3d522d48454f9_.log (deflated 50%) 2024-12-18T01:48:58.4890947Z adding: test/test-reports/dynamo.test_ctx_manager_1.1_46b0a5e65c2fe9ba_.log (stored 0%) 2024-12-18T01:48:58.4892056Z adding: test/test-reports/dynamo.test_activation_checkpointing_1.1_83ec69ea48fb62a2_.log (stored 0%) 2024-12-18T01:48:58.4893305Z adding: test/test-reports/dynamo.test_trace_rules_1.1_0eb89d4006f39b30_.log (stored 0%) 2024-12-18T01:48:58.4893991Z adding: test/test-reports/dynamo.test_debug_utils_1.1_04cbdd2a18249f09_.log (stored 0%) 2024-12-18T01:48:58.4894680Z adding: test/test-reports/dynamo.test_bytecode_utils_1.1_2fcc1373f0ec57bd_.log (stored 0%) 2024-12-18T01:48:58.4895394Z adding: test/test-reports/dynamo.test_recompile_ux_1.1_78dddf6d395cd6aa_.log (stored 0%) 2024-12-18T01:48:58.4896074Z adding: test/test-reports/dynamo.test_minifier_1.1_61883a91e0a05794_.log (stored 0%) 2024-12-18T01:48:58.4896745Z adding: test/test-reports/dynamo.test_comptime_1.1_328e5985dede106c_.log (stored 0%) 2024-12-18T01:48:58.4897363Z adding: test/test-reports/test_hub_1.1_32709423bb8ab6fa_.log (stored 0%) 2024-12-18T01:48:58.4898280Z adding: test/test-reports/optim.test_swa_utils_1.1_1caeceef681f7ebe_.log (deflated 7%) 2024-12-18T01:48:58.4899047Z adding: test/test-reports/profiler.test_record_function_1.1_9910e7d737320886_.log (deflated 68%) 2024-12-18T01:48:58.4899811Z adding: test/test-reports/profiler.test_execution_trace_1.1_e8738ac528d4c5a6_.log (deflated 81%) 2024-12-18T01:48:58.4900536Z adding: test/test-reports/test_quantization_1.4_eb97359331d1a3a2_.log (deflated 88%) 2024-12-18T01:48:58.4901188Z adding: test/test-reports/cpp.Dict_test_1.1_6cc502b647f7ca3d_.log (deflated 49%) 2024-12-18T01:48:58.4901847Z adding: test/test-reports/cpp.Dimname_test_1.1_9fd77c52cc88b84f_.log (deflated 49%) 2024-12-18T01:48:58.4902536Z adding: test/test-reports/cpp.NamedTensor_test_1.1_134a0a465f601d46_.log (deflated 49%) 2024-12-18T01:48:58.4903237Z adding: test/test-reports/cpp.apply_utils_test_1.1_ff8e4dd46daef9d2_.log (deflated 49%) 2024-12-18T01:48:58.4903917Z adding: test/test-reports/cpp.atest_1.1_0451150a69a911dc_.log (deflated 49%) 2024-12-18T01:48:58.4904520Z adding: test/test-reports/cpp.basic_1.1_88666cc8dcd02c84_.log (deflated 48%) 2024-12-18T01:48:58.4905160Z adding: test/test-reports/cpp.broadcast_test_1.1_fc9b06d520cefe9f_.log (deflated 49%) 2024-12-18T01:48:58.4905856Z adding: test/test-reports/cpp.cpu_generator_test_1.1_8ac2c290d06a40cb_.log (deflated 49%) 2024-12-18T01:48:58.4906566Z adding: test/test-reports/cpp.dlconvertor_test_1.1_b9418f183bf4f252_.log (deflated 49%) 2024-12-18T01:48:58.4907288Z adding: test/test-reports/cpp.extension_backend_test_1.1_d067c4d558177028_.log (deflated 49%) 2024-12-18T01:48:58.4907987Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_16c6120cfefbbba0_.log (deflated 49%) 2024-12-18T01:48:58.4908707Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_73963e723bc3255a_.log (deflated 49%) 2024-12-18T01:48:58.4909375Z adding: test/test-reports/cpp.native_test_1.1_9888f29e8e15beec_.log (deflated 48%) 2024-12-18T01:48:58.4910057Z adding: test/test-reports/cpp.operators_test_1.1_08fe341eba8df6ed_.log (deflated 49%) 2024-12-18T01:48:58.4910752Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_5d3e44d6262b51a8_.log (deflated 49%) 2024-12-18T01:48:58.4911429Z adding: test/test-reports/cpp.scalar_test_1.1_12741a57dcb2ca88_.log (deflated 49%) 2024-12-18T01:48:58.4912100Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_a9273be29e48b704_.log (deflated 49%) 2024-12-18T01:48:58.4912834Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_b8bed8e6df4956b3_.log (deflated 49%) 2024-12-18T01:48:58.4913536Z adding: test/test-reports/cpp.wrapdim_test_1.1_bd70b902d4f8c446_.log (deflated 48%) 2024-12-18T01:48:58.4914197Z adding: test/test-reports/cpp.Dimname_test_1.1_45d99f9a9720fda1_.log (deflated 59%) 2024-12-18T01:48:58.4914877Z adding: test/test-reports/cpp.NamedTensor_test_1.1_36bbab4e8aba4113_.log (deflated 71%) 2024-12-18T01:48:58.4915544Z adding: test/test-reports/cpp.Dict_test_1.1_b60727342cb2ea77_.log (deflated 84%) 2024-12-18T01:48:58.4916274Z adding: test/test-reports/cpp.apply_utils_test_1.1_60f0ffd6b77f6bea_.log (deflated 65%) 2024-12-18T01:48:58.4916921Z adding: test/test-reports/cpp.atest_1.1_495741ebc07ca351_.log (deflated 73%) 2024-12-18T01:48:58.4917534Z adding: test/test-reports/cpp.basic_1.1_c6fa46c82593b3f9_.log (deflated 61%) 2024-12-18T01:48:58.4918180Z adding: test/test-reports/cpp.broadcast_test_1.1_b44d7fa47e75e6f8_.log (deflated 50%) 2024-12-18T01:48:58.4918878Z adding: test/test-reports/cpp.cpu_generator_test_1.1_9c5ecad2e7b508f0_.log (deflated 78%) 2024-12-18T01:48:58.4919583Z adding: test/test-reports/cpp.dlconvertor_test_1.1_18bca9de20e25fb7_.log (deflated 56%) 2024-12-18T01:48:58.4920310Z adding: test/test-reports/cpp.extension_backend_test_1.1_310a0cb5e3e952d9_.log (deflated 50%) 2024-12-18T01:48:58.4921009Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_31a72bde64f9fd52_.log (deflated 54%) 2024-12-18T01:48:58.4921677Z adding: test/test-reports/cpp.native_test_1.1_003646f521862b84_.log (deflated 54%) 2024-12-18T01:48:58.4922403Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_d5fe2bf6075cbfe4_.log (deflated 80%) 2024-12-18T01:48:58.4923087Z adding: test/test-reports/cpp.operators_test_1.1_925fbae30087838b_.log (deflated 60%) 2024-12-18T01:48:58.4947339Z ##[group]Run # Remove any previous debugging artifacts if they exist 2024-12-18T01:48:58.4947880Z # Remove any previous debugging artifacts if they exist 2024-12-18T01:48:58.4948291Z rm -f debug-*.zip 2024-12-18T01:48:58.4948580Z if [ -d 'test/debug' ]; then 2024-12-18T01:48:58.4948947Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2024-12-18T01:48:58.4949299Z fi 2024-12-18T01:48:58.4954686Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:48:58.4955086Z env: 2024-12-18T01:48:58.4955316Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:58.4955887Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:58.4956574Z FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_34566067022 2024-12-18T01:48:58.4956984Z ##[endgroup] 2024-12-18T01:48:58.5032759Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:48:58.5033120Z with: 2024-12-18T01:48:58.5033352Z s3-bucket: gha-artifacts 2024-12-18T01:48:58.5033680Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:48:58.5034053Z retention-days: 14 2024-12-18T01:48:58.5034323Z if-no-files-found: warn 2024-12-18T01:48:58.5034608Z path: test-jsons-*.zip 2024-12-18T01:48:58.5034883Z name: artifact 2024-12-18T01:48:58.5035113Z region: us-east-1 2024-12-18T01:48:58.5035356Z env: 2024-12-18T01:48:58.5035580Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:58.5036176Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:58.5036776Z ##[endgroup] 2024-12-18T01:48:58.8646872Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T01:48:58.8647400Z With the provided path, there will be 1 file uploaded 2024-12-18T01:48:58.8647894Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:48:59.0362713Z Starting upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_34566067022.zip 2024-12-18T01:48:59.1749870Z Finished upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_34566067022.zip 2024-12-18T01:48:59.1941770Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:48:59.1942136Z with: 2024-12-18T01:48:59.1942369Z s3-bucket: gha-artifacts 2024-12-18T01:48:59.1942716Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:48:59.1943086Z retention-days: 14 2024-12-18T01:48:59.1943354Z if-no-files-found: error 2024-12-18T01:48:59.1943644Z path: test-reports-*.zip 2024-12-18T01:48:59.1943921Z name: artifact 2024-12-18T01:48:59.1944163Z region: us-east-1 2024-12-18T01:48:59.1944401Z env: 2024-12-18T01:48:59.1944627Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:59.1945104Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:59.1945615Z ##[endgroup] 2024-12-18T01:48:59.5239105Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T01:48:59.5239582Z With the provided path, there will be 1 file uploaded 2024-12-18T01:48:59.5240048Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:48:59.5280049Z Starting upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_34566067022.zip 2024-12-18T01:48:59.7640707Z Finished upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_34566067022.zip 2024-12-18T01:48:59.7831855Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:48:59.7832212Z with: 2024-12-18T01:48:59.7832432Z s3-bucket: gha-artifacts 2024-12-18T01:48:59.7832765Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:48:59.7833163Z retention-days: 14 2024-12-18T01:48:59.7833435Z if-no-files-found: ignore 2024-12-18T01:48:59.7833725Z path: logs-*.zip 2024-12-18T01:48:59.7833981Z name: artifact 2024-12-18T01:48:59.7834215Z region: us-east-1 2024-12-18T01:48:59.7834603Z env: 2024-12-18T01:48:59.7834834Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:48:59.7835314Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:48:59.7835934Z ##[endgroup] 2024-12-18T01:49:00.1129019Z NOTE: s3-prefix specified, ignoring name parameter 2024-12-18T01:49:00.1129507Z With the provided path, there will be 1 file uploaded 2024-12-18T01:49:00.1129978Z Uploading to s3 prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:49:00.1169103Z Starting upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_34566067022.zip 2024-12-18T01:49:00.4207748Z Finished upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_34566067022.zip 2024-12-18T01:49:00.4423818Z ##[group]Run seemethere/upload-artifact-s3@v5 2024-12-18T01:49:00.4424428Z with: 2024-12-18T01:49:00.4424835Z s3-bucket: gha-artifacts 2024-12-18T01:49:00.4425409Z s3-prefix: pytorch/pytorch/12383255652/1/artifact 2024-12-18T01:49:00.4426175Z retention-days: 14 2024-12-18T01:49:00.4426645Z if-no-files-found: ignore 2024-12-18T01:49:00.4427139Z path: debug-*.zip 2024-12-18T01:49:00.4427573Z name: artifact 2024-12-18T01:49:00.4427965Z region: us-east-1 2024-12-18T01:49:00.4428377Z env: 2024-12-18T01:49:00.4428745Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:49:00.4429592Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:49:00.4430492Z ##[endgroup] 2024-12-18T01:49:00.7649865Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2024-12-18T01:49:00.7847336Z ##[group]Run # shellcheck disable=SC2156 2024-12-18T01:49:00.7847725Z # shellcheck disable=SC2156 2024-12-18T01:49:00.7848319Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2024-12-18T01:49:00.7854229Z shell: /usr/bin/bash -e {0} 2024-12-18T01:49:00.7854532Z env: 2024-12-18T01:49:00.7854766Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:49:00.7855244Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:49:00.7855745Z ##[endgroup] 2024-12-18T01:49:01.0111481Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@release/2.6 2024-12-18T01:49:01.0111963Z with: 2024-12-18T01:49:01.0112182Z env: 2024-12-18T01:49:01.0112396Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:49:01.0112874Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:49:01.0113383Z ##[endgroup] 2024-12-18T01:49:01.0137920Z ##[group]Run set -eou pipefail 2024-12-18T01:49:01.0138260Z set -eou pipefail 2024-12-18T01:49:01.0138545Z  2024-12-18T01:49:01.0138916Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2024-12-18T01:49:01.0139404Z for _ in $(seq 1440); do 2024-12-18T01:49:01.0139750Z  # Break if no ssh session exists anymore 2024-12-18T01:49:01.0140122Z  if [ "$(who)" = "" ]; then 2024-12-18T01:49:01.0140438Z  break 2024-12-18T01:49:01.0140675Z  fi 2024-12-18T01:49:01.0140962Z  echo "." 2024-12-18T01:49:01.0141216Z  sleep 5 2024-12-18T01:49:01.0141453Z done 2024-12-18T01:49:01.0146946Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:49:01.0147335Z env: 2024-12-18T01:49:01.0147570Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:49:01.0148044Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:49:01.0148544Z ##[endgroup] 2024-12-18T01:49:01.0171420Z Holding runner for 2 hours until all ssh sessions have logged out 2024-12-18T01:49:01.0258584Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2024-12-18T01:49:01.0259619Z # ignore expansion of "docker ps -q" since it could be empty 2024-12-18T01:49:01.0260341Z # shellcheck disable=SC2046 2024-12-18T01:49:01.0260873Z docker stop $(docker ps -q) || true 2024-12-18T01:49:01.0261445Z # Prune all of the docker images 2024-12-18T01:49:01.0261991Z docker system prune -af 2024-12-18T01:49:01.0269855Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:49:01.0270542Z env: 2024-12-18T01:49:01.0270927Z GIT_DEFAULT_BRANCH: main 2024-12-18T01:49:01.0271767Z DOCKER_CONTAINER_ID: 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:49:01.0272690Z ##[endgroup] 2024-12-18T01:49:01.6230446Z 6707fe6f6119 2024-12-18T01:49:02.1605713Z Deleted Containers: 2024-12-18T01:49:02.1606247Z 6707fe6f6119b708677b2dcbfe5576700de53e33ad95b6a457ee50a58439d1b4 2024-12-18T01:49:02.1606673Z 2024-12-18T01:49:09.8171075Z Deleted Images: 2024-12-18T01:49:09.8172187Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:45e1356b47a284893081276eff3000b7b534f3b1 2024-12-18T01:49:09.8173862Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10@sha256:061c030ced34ec2531cb2d0841ec9a10875e6b82f7a9a932b0a62f77f1c9910a 2024-12-18T01:49:09.8175159Z deleted: sha256:bca7896c0150bd62bb439228906ce331a02b4d3cd9ef140e4946794ede2ac12d 2024-12-18T01:49:09.8175826Z deleted: sha256:b37cb25b49b213a74963f9e9acec4cb57db2d0a6b6bd4fe46f1fb7448c2f485b 2024-12-18T01:49:09.8176497Z deleted: sha256:0684fc485e9f2dba4d5119f3e50d4f5f4d614268138293188d9de1162d0f862f 2024-12-18T01:49:09.8177165Z deleted: sha256:e2fa7e8a0f5c853bc371c599dfdf7c3c4eb45d56465095c920bf0bd6764ce98b 2024-12-18T01:49:09.8177827Z deleted: sha256:5f66e21b786f4832bc27c816f2908df50c288ea31161474c330f3383e1dc876a 2024-12-18T01:49:09.8178486Z deleted: sha256:13591c8c538e9eae81be5428b9ae1b313114631edbf3db3ed73bcf4d0ad49d47 2024-12-18T01:49:09.8179294Z deleted: sha256:4610568507b96a9b10bf7ecdef54f3d51ce8813dc4a8058de31c909ba7732e0b 2024-12-18T01:49:09.8179945Z deleted: sha256:e6c87f0755eea55573a59e98a785edd1e263315ed5c64e3278c5e7354f6f5f1f 2024-12-18T01:49:09.8180619Z deleted: sha256:957e1aff11d7dcbb5ba48c9a1f706f79c9d5473ce88ef0a6653ccbc8722b8b97 2024-12-18T01:49:09.8181425Z deleted: sha256:5fa536fe4b6bd28a2135d5a912adcf6b2550dd19ed48c3293d835f9fb0d125a2 2024-12-18T01:49:09.8182102Z deleted: sha256:b7c3cf49eb34310d0ba8106a6a3b84f773f33498eb690a20b5e7c628b7a5e344 2024-12-18T01:49:09.8182770Z deleted: sha256:135c4c36758fb415aac8b6e58de2fe4fa44510f36a9980cbcf829fd55de7dd93 2024-12-18T01:49:09.8183441Z deleted: sha256:00f772fec24f208a7a37bacc451c68e56ef21370404ed4f714e4d0b2dc07821a 2024-12-18T01:49:09.8184102Z deleted: sha256:5ec1b8360a46cfa9ffa29c8e7a93a9a1c18ec7bfcb70928fe96e00ae1adf60f8 2024-12-18T01:49:09.8184781Z deleted: sha256:1a0666d2183f3a73759abc9b3be5cb035ceb319077ee7c144dbd3e56bd217422 2024-12-18T01:49:09.8185454Z deleted: sha256:1a42affd9fb06ea4433d641165e2c4a19b2b9d51b08bde2c3d54cc68e95875cb 2024-12-18T01:49:09.8186120Z deleted: sha256:f4f01894b6627bc6909fd69c2682f777c05959399bb8c1429e04f6434168bd7b 2024-12-18T01:49:09.8186772Z deleted: sha256:74f71c4712284774d10004d1d47dc81024859cced1ea43ce47e9a8d8619668b0 2024-12-18T01:49:09.8187427Z deleted: sha256:8613680f8d03dbaba911bf1a9b377a378aac008dcf8405d6b080e27207dc96e3 2024-12-18T01:49:09.8188081Z deleted: sha256:8f8088e54ff5acdd8a67c4951932c0088d0463f92770ca9a8403da3426316608 2024-12-18T01:49:09.8188732Z deleted: sha256:1658bfa7f7d94b767585208f5233a5125731e0e6159c222091b4231dea0d7eea 2024-12-18T01:49:09.8189388Z deleted: sha256:8d15c9cf0c1bd45aa914daad8b1d3249c88707649ef582963c58a6e9266b1ff6 2024-12-18T01:49:09.8190064Z deleted: sha256:b29e07a048f5eb88574b907fd4e5b4cc946bf6e2ae244fa8baeef5931cf835e8 2024-12-18T01:49:09.8190747Z deleted: sha256:47fa1ba52a838dfe2e711bfc65c67fdbebdddde96e06722be42608652249abad 2024-12-18T01:49:09.8191414Z deleted: sha256:d639597541d83dc8996b25a23dbbf1be54ce2be7ddba49656b13ebd79f8facb3 2024-12-18T01:49:09.8192094Z deleted: sha256:4f7ae62efb3ef05d0fe654ef1e619b08c87d4f959eacc314ef096e97d26a4d31 2024-12-18T01:49:09.8192780Z deleted: sha256:9df1a3e0bd3bb93245bfec55c16b9755e7342e14a482af9874b9754eeaef8806 2024-12-18T01:49:09.8193454Z deleted: sha256:3d1ad0145bbeaa0b4d2bb5d38f83193033c2369b3b1229977fb77f8cd40d2903 2024-12-18T01:49:09.8194121Z deleted: sha256:a4251f423ea3b244b206949229a69a052a42a8dcf0cb2a673ea75d4e8d0ea8ee 2024-12-18T01:49:09.8194781Z deleted: sha256:54fd5685aea3484962dfa8ffeb27a798056aad5144c5581ce5e1554d56ae724f 2024-12-18T01:49:09.8195426Z deleted: sha256:b4351f6345641ef798e391c381d0d584d8682ee130e0b1826042735e593c3df1 2024-12-18T01:49:09.8196176Z deleted: sha256:5e63f6e3074aed29218aa5f50e72cdf10adcb627bef66d0cc6caeb2e8327902d 2024-12-18T01:49:09.8196866Z deleted: sha256:c8be5cef2b9e2589aa24a004aef49a882a8ee1c529a5c8adbc83f9cef3dd96a3 2024-12-18T01:49:09.8197564Z deleted: sha256:bc57d96cfcef7043d1aa5de0c3a6bea5e87de86ce0c8895aafc279f4f5a87eec 2024-12-18T01:49:09.8198481Z deleted: sha256:e36bf7691dd3ddeabadfd36f50112b342982f19ff9912ef83c00eb7f4ce738fd 2024-12-18T01:49:09.8199154Z deleted: sha256:114342eff9454d148c5a019e69c2f42d698228859fd4c25cf83f5b5f1d81d58a 2024-12-18T01:49:09.8200115Z deleted: sha256:f1c21e46ee7ec2808b4e264a00acc424b74c5e15e50a8da81b2d0f59f3784b3f 2024-12-18T01:49:09.8201147Z deleted: sha256:a511a53904c79732b2485433258c094b07081f5ed90dc00e632bcc94bffc170b 2024-12-18T01:49:09.8202137Z deleted: sha256:3d3d0b7e785d4973741041f02b04b13b58c54a27f25490d0186452e7c4ba0af4 2024-12-18T01:49:09.8203151Z deleted: sha256:cf8be6d811850df0a90fcab45316003c0056d825e8e8ad0cbec7c2a7efb31423 2024-12-18T01:49:09.8204320Z deleted: sha256:0c6c758d64fecc3eced1d1ce34176ffe1a3c71fb09a77074d506b3ff3d3aa60b 2024-12-18T01:49:09.8205601Z deleted: sha256:54fd22deb54cc673f6886e1b298e4a9fe9f76844be8712fe72879d729e65ec75 2024-12-18T01:49:09.8206775Z deleted: sha256:bdd6d797917884c136991177bd5fd41a720ecf4ea3fe0dbbf423c5c1eace9938 2024-12-18T01:49:09.8207803Z deleted: sha256:f554d528bdefe81efd95b679ad31e9c286b079ab5f268aa82830b2d1cc4875ba 2024-12-18T01:49:09.8209065Z deleted: sha256:fe6e5d64f8b4138b74bb92057e1f2576b467a0540871527f3724fe75e0228217 2024-12-18T01:49:09.8210198Z deleted: sha256:12c6fda4f7b00c293e6633e24cee50459988ffff4702d1238edd08f68a5531eb 2024-12-18T01:49:09.8211333Z deleted: sha256:bf9cdd0047a691c8596c7e60bde2e2da4858fa68f951714a007502ac698bb80b 2024-12-18T01:49:09.8212737Z deleted: sha256:5d028afdc2f216f5f95a92a09ead4cea468bfd340215af977f1ff2f6ac0eb2c3 2024-12-18T01:49:09.8213944Z deleted: sha256:8c0f49a116caa3fb4201719d3316d85af8c6d0ba0be571a5098a482f64339d7b 2024-12-18T01:49:09.8215025Z deleted: sha256:6722ef2d8fe730f95f0ca7b984e2725ab956f43760d5d5fec57069df9da31d02 2024-12-18T01:49:09.8216051Z deleted: sha256:b3fbc3e36e9cb0c3e256102fe0ab977b17f4110070b2a6178aa808b538c081ac 2024-12-18T01:49:09.8216908Z deleted: sha256:aecbc4849a9322d15680968f3579cc592288b2ce21a6ec1ccbdcf20a473ee825 2024-12-18T01:49:09.8217584Z deleted: sha256:d294303b24b45985ecbd15fd83dac2e19223fcb044a50805ad3edbdc8d559dfd 2024-12-18T01:49:09.8218259Z deleted: sha256:163c9d1ba102357b9772f35b3e4d8b1a14e93ff9b7f891e350edeba2942e89e3 2024-12-18T01:49:09.8218976Z deleted: sha256:31ddf2b87b7a15f642bc2c9bea40e21da6169db8f7ebd77a3fc46b4b8b8cd065 2024-12-18T01:49:09.8219650Z deleted: sha256:9f25b6a89dea3598bb500a7a8c2e97c1e7add213839a4ed29ba912a4239c4ee2 2024-12-18T01:49:09.8220327Z deleted: sha256:ae0e0d9be61d07a09ee7247abf5f86e9335d8409ffb1a182ac31a624d0b3b8db 2024-12-18T01:49:09.8220997Z deleted: sha256:82a9988c33e343e70e1a4bf89279ac17e38e28732341ecc1e3c2cc14edae25fc 2024-12-18T01:49:09.8221671Z deleted: sha256:3ac4f1d2c8c2990d7edcfe5e786c83bdedcd59b141df781a45ab7d5d643bdc54 2024-12-18T01:49:09.8222350Z deleted: sha256:bb742c221aea6ea21ad3b7d44085c41339698eebedd91e12130937c1c8716813 2024-12-18T01:49:09.8223016Z deleted: sha256:5523f3cb8cd3665f227498a26cc21170f2ac122f5099e5280340c452018ef56a 2024-12-18T01:49:09.8223665Z deleted: sha256:9b07a891b6bf8f422ef8b19bd27b790ec424e7bb586b2a8c557b4abe2dbeee92 2024-12-18T01:49:09.8224325Z deleted: sha256:17995465f7161359d0635c3ef5ee0ee64f9fb751b97328ba310b51d6d682799c 2024-12-18T01:49:09.8224979Z deleted: sha256:96eee0090871a5c2bf690fb26831cc9765ad450b12f2fc9030d5cc4a1e821e57 2024-12-18T01:49:09.8225642Z deleted: sha256:6b7fe0f72838fe227e1fc8f3753d776bd6be98e1447355a271d6ef16528580a3 2024-12-18T01:49:09.8226299Z deleted: sha256:0276b4644a39d24114fe45ad79696577283513e0b6b9f382ff031c4bdd7eed0b 2024-12-18T01:49:09.8226965Z deleted: sha256:fb7baad13b0a0e3ff56ea7c0f92a6c2032ef03991b6d7ce731a211bf9e134cc9 2024-12-18T01:49:09.8227636Z deleted: sha256:bb28378fdb72ee73a2b4bcb061eee51057d1c4c57942ed7b7dd139bbe1ac7629 2024-12-18T01:49:09.8228315Z deleted: sha256:ed0c737f69a9a67c2bc2a519d5ad74772c6a9a39eaf5a2432c02b4be63f987ef 2024-12-18T01:49:09.8228966Z deleted: sha256:2215300c802d63cc45db580e2434435f9354d10f28b46d175784879788be74c4 2024-12-18T01:49:09.8229620Z deleted: sha256:b80736c9a73cd77a98184b02eb7b7bcdce069c4ffa279c3e8c13c82fdbea198c 2024-12-18T01:49:09.8230288Z deleted: sha256:08febf2a84dc9866b0f16f1f59ad4449831f620e338a861595baa5d64ea55a0f 2024-12-18T01:49:09.8230940Z deleted: sha256:3225296834e4f84b5d29b7c3999083f08395967aacd8b386987f91204cdc0657 2024-12-18T01:49:09.8231641Z deleted: sha256:31ec55d92304f5b6740011ed790bce05383ff8c1a1da9c25798925945d638fa9 2024-12-18T01:49:09.8232295Z deleted: sha256:0bf8e13700663d9a55252013c5e7b63b37f02fba36b4c4fabb1b7c42618ac021 2024-12-18T01:49:09.8232964Z deleted: sha256:f35eb9c54d7b0be447f8292b8b52f9cd032f04c66c6562f96e7f0d5e9544a63c 2024-12-18T01:49:09.8233630Z deleted: sha256:216388bc0240b9bef43944e220c2efe1e902f7daf0b5ab2730d190b0b8e402c8 2024-12-18T01:49:09.8234304Z deleted: sha256:0b5c3bdadb21861c1147bed576b264270cc52ca0e1fe1c6ae5bbca5751f5071c 2024-12-18T01:49:09.8234964Z deleted: sha256:600a1b2d7757a23063cab7c86fd7db4e25823014289f5dac6a5c3a7e788e7d89 2024-12-18T01:49:09.8235634Z deleted: sha256:1126bf4bc636967f9f8fbb93ebe70c0e3f71d5a68597a83afcd6b4afa031ef9a 2024-12-18T01:49:09.8236379Z deleted: sha256:2c9686e3f473696c2274e06a0fa7c5bfc6515299b8c57151cc4047d77b3f9901 2024-12-18T01:49:09.8237081Z deleted: sha256:a7938be85754ac8950fc86040301dfa1575a21ffd6f3ea68294fbad21eac54a3 2024-12-18T01:49:09.8237751Z deleted: sha256:da72a3758cc04ebef513c89f167f6fad7ce9cf23359534f3fdbd8de1c816dc52 2024-12-18T01:49:09.8238437Z deleted: sha256:959c63f9941d563e22cd6e5ba1bcdeaa5ebca40dc7df9d78a745ab1c123402b1 2024-12-18T01:49:09.8239180Z deleted: sha256:0aadfae3730253d054cf6cf1c36f88fc4a455d59e647db9e78c19cbb685e36e2 2024-12-18T01:49:09.8239843Z deleted: sha256:4414e052fe3e6a0d3d616f041348251b2dfe163f7770468d72d36495ea992085 2024-12-18T01:49:09.8240501Z deleted: sha256:c5450805a9c1bf12c7facbc6a89870e92c002af1640346b2183fec0fde0e599f 2024-12-18T01:49:09.8241174Z deleted: sha256:d3b938aec2539dea39a79c8c96b8beade30273fe7cb18432ec50088de1b4e7a3 2024-12-18T01:49:09.8241859Z deleted: sha256:8eaf93f341e8027bb255d1551415ab1ed58efa1452d9fcedd137bbff35d0a570 2024-12-18T01:49:09.8242529Z deleted: sha256:a1015f201351c557371d4ad8d4d514dba6edc844272110518243a8aee195bb0d 2024-12-18T01:49:09.8243168Z deleted: sha256:115477b423ff9b314eb934298c90575c8d59ad38a5e9e6a94962d05600bea675 2024-12-18T01:49:09.8243829Z deleted: sha256:fffe76c64ef2dee2d80a8bb3ad13d65d596d04a45510b1956a976a69215dae92 2024-12-18T01:49:09.8244238Z 2024-12-18T01:49:09.8244357Z Total reclaimed space: 10.73GB 2024-12-18T01:49:09.8346498Z Post job cleanup. 2024-12-18T01:49:09.8420035Z Post job cleanup. 2024-12-18T01:49:09.9209374Z [command]/usr/bin/git version 2024-12-18T01:49:09.9257861Z git version 2.40.1 2024-12-18T01:49:09.9299959Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/b84464c1-8cca-4b1c-b4a0-333a9c786c8e' before making global git config changes 2024-12-18T01:49:09.9301701Z Adding repository directory to the temporary git global config as a safe directory 2024-12-18T01:49:09.9305498Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2024-12-18T01:49:09.9333609Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2024-12-18T01:49:09.9359810Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :" 2024-12-18T01:49:09.9653378Z Entering 'android/libs/fbjni' 2024-12-18T01:49:09.9705387Z Entering 'third_party/FP16' 2024-12-18T01:49:09.9754254Z Entering 'third_party/FXdiv' 2024-12-18T01:49:09.9803649Z Entering 'third_party/NNPACK' 2024-12-18T01:49:09.9853330Z Entering 'third_party/NVTX' 2024-12-18T01:49:09.9903669Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-18T01:49:09.9953043Z Entering 'third_party/XNNPACK' 2024-12-18T01:49:10.0018837Z Entering 'third_party/benchmark' 2024-12-18T01:49:10.0069415Z Entering 'third_party/composable_kernel' 2024-12-18T01:49:10.0126734Z Entering 'third_party/cpp-httplib' 2024-12-18T01:49:10.0176104Z Entering 'third_party/cpuinfo' 2024-12-18T01:49:10.0225620Z Entering 'third_party/cudnn_frontend' 2024-12-18T01:49:10.0274326Z Entering 'third_party/cutlass' 2024-12-18T01:49:10.0332730Z Entering 'third_party/eigen' 2024-12-18T01:49:10.0383090Z Entering 'third_party/fbgemm' 2024-12-18T01:49:10.0432350Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-18T01:49:10.0481863Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-18T01:49:10.0532798Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-18T01:49:10.0587643Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-18T01:49:10.0636869Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-18T01:49:10.0686518Z Entering 'third_party/flatbuffers' 2024-12-18T01:49:10.0740752Z Entering 'third_party/fmt' 2024-12-18T01:49:10.0790531Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-18T01:49:10.0840617Z Entering 'third_party/gloo' 2024-12-18T01:49:10.0889273Z Entering 'third_party/googletest' 2024-12-18T01:49:10.0938239Z Entering 'third_party/ideep' 2024-12-18T01:49:10.0987058Z Entering 'third_party/ideep/mkl-dnn' 2024-12-18T01:49:10.1044887Z Entering 'third_party/ittapi' 2024-12-18T01:49:10.1093720Z Entering 'third_party/kineto' 2024-12-18T01:49:10.1143151Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-18T01:49:10.1191211Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-18T01:49:10.1242373Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-18T01:49:10.1291289Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-18T01:49:10.1340968Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-18T01:49:10.1388421Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-18T01:49:10.1438619Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-18T01:49:10.1487275Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-18T01:49:10.1538268Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-18T01:49:10.1588524Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-18T01:49:10.1638937Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-18T01:49:10.1687122Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-18T01:49:10.1737988Z Entering 'third_party/mimalloc' 2024-12-18T01:49:10.1787763Z Entering 'third_party/nccl/nccl' 2024-12-18T01:49:10.1837716Z Entering 'third_party/nlohmann' 2024-12-18T01:49:10.1887983Z Entering 'third_party/onnx' 2024-12-18T01:49:10.1956351Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-18T01:49:10.2008099Z Entering 'third_party/opentelemetry-cpp' 2024-12-18T01:49:10.2059005Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-18T01:49:10.2109005Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-18T01:49:10.2158462Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-18T01:49:10.2206632Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-18T01:49:10.2256593Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-18T01:49:10.2305273Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-18T01:49:10.2353163Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-18T01:49:10.2402750Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-18T01:49:10.2453376Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-18T01:49:10.2503591Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-18T01:49:10.2573028Z Entering 'third_party/pocketfft' 2024-12-18T01:49:10.2624876Z Entering 'third_party/protobuf' 2024-12-18T01:49:10.2678220Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-18T01:49:10.2727774Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-18T01:49:10.2778228Z Entering 'third_party/psimd' 2024-12-18T01:49:10.2831690Z Entering 'third_party/pthreadpool' 2024-12-18T01:49:10.2881614Z Entering 'third_party/pybind11' 2024-12-18T01:49:10.2930956Z Entering 'third_party/python-peachpy' 2024-12-18T01:49:10.2980058Z Entering 'third_party/sleef' 2024-12-18T01:49:10.3030736Z Entering 'third_party/tensorpipe' 2024-12-18T01:49:10.3081269Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-18T01:49:10.3131054Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-18T01:49:10.3179114Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-18T01:49:10.3227767Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-18T01:49:10.3274751Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-18T01:49:10.3340003Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2024-12-18T01:49:10.3356632Z http.https://github.com/.extraheader 2024-12-18T01:49:10.3365717Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2024-12-18T01:49:10.3391564Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :" 2024-12-18T01:49:10.3652178Z Entering 'android/libs/fbjni' 2024-12-18T01:49:10.3699706Z http.https://github.com/.extraheader 2024-12-18T01:49:10.3717688Z Entering 'third_party/FP16' 2024-12-18T01:49:10.3751215Z http.https://github.com/.extraheader 2024-12-18T01:49:10.3780069Z Entering 'third_party/FXdiv' 2024-12-18T01:49:10.3814064Z http.https://github.com/.extraheader 2024-12-18T01:49:10.3844621Z Entering 'third_party/NNPACK' 2024-12-18T01:49:10.3877618Z http.https://github.com/.extraheader 2024-12-18T01:49:10.3907927Z Entering 'third_party/NVTX' 2024-12-18T01:49:10.3941230Z http.https://github.com/.extraheader 2024-12-18T01:49:10.3971393Z Entering 'third_party/VulkanMemoryAllocator' 2024-12-18T01:49:10.4006940Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4036839Z Entering 'third_party/XNNPACK' 2024-12-18T01:49:10.4069620Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4114340Z Entering 'third_party/benchmark' 2024-12-18T01:49:10.4147460Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4177701Z Entering 'third_party/composable_kernel' 2024-12-18T01:49:10.4214417Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4251056Z Entering 'third_party/cpp-httplib' 2024-12-18T01:49:10.4284240Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4314204Z Entering 'third_party/cpuinfo' 2024-12-18T01:49:10.4347263Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4377435Z Entering 'third_party/cudnn_frontend' 2024-12-18T01:49:10.4410786Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4441017Z Entering 'third_party/cutlass' 2024-12-18T01:49:10.4473874Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4511742Z Entering 'third_party/eigen' 2024-12-18T01:49:10.4545010Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4577329Z Entering 'third_party/fbgemm' 2024-12-18T01:49:10.4610471Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4640800Z Entering 'third_party/fbgemm/third_party/asmjit' 2024-12-18T01:49:10.4673677Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4705573Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2024-12-18T01:49:10.4739251Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4769607Z Entering 'third_party/fbgemm/third_party/cutlass' 2024-12-18T01:49:10.4803153Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4840651Z Entering 'third_party/fbgemm/third_party/googletest' 2024-12-18T01:49:10.4873182Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4904404Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2024-12-18T01:49:10.4938631Z http.https://github.com/.extraheader 2024-12-18T01:49:10.4970064Z Entering 'third_party/flatbuffers' 2024-12-18T01:49:10.5005043Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5038787Z Entering 'third_party/fmt' 2024-12-18T01:49:10.5072541Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5103897Z Entering 'third_party/gemmlowp/gemmlowp' 2024-12-18T01:49:10.5138234Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5168842Z Entering 'third_party/gloo' 2024-12-18T01:49:10.5204182Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5234444Z Entering 'third_party/googletest' 2024-12-18T01:49:10.5267610Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5297814Z Entering 'third_party/ideep' 2024-12-18T01:49:10.5332017Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5360585Z Entering 'third_party/ideep/mkl-dnn' 2024-12-18T01:49:10.5393575Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5431884Z Entering 'third_party/ittapi' 2024-12-18T01:49:10.5465681Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5495609Z Entering 'third_party/kineto' 2024-12-18T01:49:10.5529683Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5559584Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2024-12-18T01:49:10.5593435Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5625044Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2024-12-18T01:49:10.5657919Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5690100Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2024-12-18T01:49:10.5724421Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5755106Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2024-12-18T01:49:10.5787892Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5820362Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2024-12-18T01:49:10.5854293Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5884936Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2024-12-18T01:49:10.5919643Z http.https://github.com/.extraheader 2024-12-18T01:49:10.5951464Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2024-12-18T01:49:10.5984471Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6015226Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2024-12-18T01:49:10.6047967Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6079933Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2024-12-18T01:49:10.6113210Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6144833Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2024-12-18T01:49:10.6178652Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6211262Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2024-12-18T01:49:10.6244029Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6273525Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2024-12-18T01:49:10.6307321Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6338667Z Entering 'third_party/mimalloc' 2024-12-18T01:49:10.6372407Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6403729Z Entering 'third_party/nccl/nccl' 2024-12-18T01:49:10.6436748Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6466740Z Entering 'third_party/nlohmann' 2024-12-18T01:49:10.6500277Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6532083Z Entering 'third_party/onnx' 2024-12-18T01:49:10.6565393Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6612173Z Entering 'third_party/onnx/third_party/pybind11' 2024-12-18T01:49:10.6645524Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6677988Z Entering 'third_party/opentelemetry-cpp' 2024-12-18T01:49:10.6711505Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6744411Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2024-12-18T01:49:10.6776532Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6807652Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2024-12-18T01:49:10.6839191Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6868769Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2024-12-18T01:49:10.6901849Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6931380Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2024-12-18T01:49:10.6962864Z http.https://github.com/.extraheader 2024-12-18T01:49:10.6993636Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2024-12-18T01:49:10.7025692Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7055214Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2024-12-18T01:49:10.7086930Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7116702Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2024-12-18T01:49:10.7148474Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7177676Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2024-12-18T01:49:10.7211209Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7244506Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2024-12-18T01:49:10.7276375Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7308553Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2024-12-18T01:49:10.7340875Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7391268Z Entering 'third_party/pocketfft' 2024-12-18T01:49:10.7424778Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7454963Z Entering 'third_party/protobuf' 2024-12-18T01:49:10.7487917Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7522101Z Entering 'third_party/protobuf/third_party/benchmark' 2024-12-18T01:49:10.7554566Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7584396Z Entering 'third_party/protobuf/third_party/googletest' 2024-12-18T01:49:10.7616927Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7647821Z Entering 'third_party/psimd' 2024-12-18T01:49:10.7681446Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7712819Z Entering 'third_party/pthreadpool' 2024-12-18T01:49:10.7746696Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7776812Z Entering 'third_party/pybind11' 2024-12-18T01:49:10.7810276Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7840618Z Entering 'third_party/python-peachpy' 2024-12-18T01:49:10.7874129Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7908402Z Entering 'third_party/sleef' 2024-12-18T01:49:10.7941589Z http.https://github.com/.extraheader 2024-12-18T01:49:10.7971522Z Entering 'third_party/tensorpipe' 2024-12-18T01:49:10.8004285Z http.https://github.com/.extraheader 2024-12-18T01:49:10.8034324Z Entering 'third_party/tensorpipe/third_party/googletest' 2024-12-18T01:49:10.8067363Z http.https://github.com/.extraheader 2024-12-18T01:49:10.8096885Z Entering 'third_party/tensorpipe/third_party/libnop' 2024-12-18T01:49:10.8130257Z http.https://github.com/.extraheader 2024-12-18T01:49:10.8160116Z Entering 'third_party/tensorpipe/third_party/libuv' 2024-12-18T01:49:10.8192984Z http.https://github.com/.extraheader 2024-12-18T01:49:10.8223612Z Entering 'third_party/tensorpipe/third_party/pybind11' 2024-12-18T01:49:10.8256450Z http.https://github.com/.extraheader 2024-12-18T01:49:10.8286392Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2024-12-18T01:49:10.8321393Z http.https://github.com/.extraheader 2024-12-18T01:49:10.8429413Z A job completed hook has been configured by the self-hosted runner administrator 2024-12-18T01:49:10.8454629Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2024-12-18T01:49:10.8459536Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2024-12-18T01:49:10.8459923Z ##[endgroup] 2024-12-18T01:49:18.6860401Z Cleaning up orphan processes