2024-11-01T16:29:42.1546262Z Current runner version: '2.320.0'
2024-11-01T16:29:42.1553071Z Runner name: 'i-00163257c71a08003'
2024-11-01T16:29:42.1553737Z Runner group name: 'Default'
2024-11-01T16:29:42.1554679Z Machine name: 'ip-10-0-72-177'
2024-11-01T16:29:42.1573175Z Testing runner upgrade compatibility
2024-11-01T16:29:42.4248949Z ##[group]GITHUB_TOKEN Permissions
2024-11-01T16:29:42.4251430Z Actions: read
2024-11-01T16:29:42.4251842Z Attestations: read
2024-11-01T16:29:42.4252239Z Checks: read
2024-11-01T16:29:42.4252608Z Contents: read
2024-11-01T16:29:42.4253005Z Deployments: read
2024-11-01T16:29:42.4253409Z Discussions: read
2024-11-01T16:29:42.4253792Z Issues: read
2024-11-01T16:29:42.4254160Z Metadata: read
2024-11-01T16:29:42.4254544Z Packages: read
2024-11-01T16:29:42.4254938Z Pages: read
2024-11-01T16:29:42.4255306Z PullRequests: read
2024-11-01T16:29:42.4255732Z RepositoryProjects: read
2024-11-01T16:29:42.4256216Z SecurityEvents: read
2024-11-01T16:29:42.4256640Z Statuses: read
2024-11-01T16:29:42.4257223Z ##[endgroup]
2024-11-01T16:29:42.4261120Z Secret source: Actions
2024-11-01T16:29:42.4261755Z Prepare workflow directory
2024-11-01T16:29:42.5292810Z Prepare all required actions
2024-11-01T16:29:42.5473047Z Getting action download info
2024-11-01T16:29:42.7353042Z Download action repository 'pytorch/test-infra@main' (SHA:49fb39b5efb49007791d74b09885044cd64544bf)
2024-11-01T16:29:43.1094019Z Download action repository 'pytorch/pytorch@main' (SHA:b57b4b7f9b168389def15ea06a4dcf9e5f6f4f04)
2024-11-01T16:29:46.3429551Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0)
2024-11-01T16:29:46.4846336Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2024-11-01T16:29:46.8374435Z Getting action download info
2024-11-01T16:29:46.9478400Z Download action repository 'malfet/checkout@silent-checkout' (SHA:e07af140b3ccefc05679e3755b9db68f4ee4589c)
2024-11-01T16:29:47.1021837Z Getting action download info
2024-11-01T16:29:47.2524569Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e)
2024-11-01T16:29:47.4200142Z Getting action download info
2024-11-01T16:29:47.5648967Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2024-11-01T16:29:47.7451520Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/138766/merge (368da7510f5d755034681777a4e5ae6d33c07b38)
2024-11-01T16:29:47.7454697Z ##[group] Inputs
2024-11-01T16:29:47.7455172Z   build-environment: linux-focal-py3.12-clang10
2024-11-01T16:29:47.7458471Z   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"}]}
2024-11-01T16:29:47.7462281Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:29:47.7463352Z   sync-tag: 
2024-11-01T16:29:47.7464231Z   timeout-minutes: 600
2024-11-01T16:29:47.7464582Z   use-gha: 
2024-11-01T16:29:47.7464878Z   dashboard-tag: 
2024-11-01T16:29:47.7465193Z   s3-bucket: gha-artifacts
2024-11-01T16:29:47.7465565Z   aws-role-to-assume: 
2024-11-01T16:29:47.7465908Z ##[endgroup]
2024-11-01T16:29:47.7466739Z Complete job name: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:29:47.8157082Z A job started hook has been configured by the self-hosted runner administrator
2024-11-01T16:29:47.8318910Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh'
2024-11-01T16:29:47.8329721Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:29:47.8330294Z ##[endgroup]
2024-11-01T16:29:49.5179177Z Runner Type: linux.2xlarge
2024-11-01T16:29:49.5179714Z Instance Type: c5.2xlarge
2024-11-01T16:29:49.5180084Z AMI Name: unknown
2024-11-01T16:29:49.5180618Z AMI ID: ami-0fff1b9a61dec8a5f
2024-11-01T16:29:56.3116301Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2024-11-01T16:29:56.3117326Z with:
2024-11-01T16:29:56.3118360Z   github-secret: ***
2024-11-01T16:29:56.3119803Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2024-11-01T16:29:56.3121234Z   activate-with-label: false
2024-11-01T16:29:56.3121601Z   label: with-ssh
2024-11-01T16:29:56.3121934Z   remove-existing-keys: true
2024-11-01T16:29:56.3122311Z   fail-silently: true
2024-11-01T16:29:56.3122663Z env:
2024-11-01T16:29:56.3122939Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:29:56.3123282Z ##[endgroup]
2024-11-01T16:29:56.4068497Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info.
2024-11-01T16:29:56.6786024Z Grabbing public ssh keys from https://github.com/c00w.keys
2024-11-01T16:29:56.7714398Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh
2024-11-01T16:29:56.7737533Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys
2024-11-01T16:29:56.7768202Z Login using: ssh ec2-user@ec2-34-231-247-108.compute-1.amazonaws.com
2024-11-01T16:29:56.7769169Z All testing is done inside the container, to start an interactive session run:
2024-11-01T16:29:56.7770061Z   docker exec -it $(docker container ps --format '{{.ID}}') bash
2024-11-01T16:29:56.7896699Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main
2024-11-01T16:29:56.7897289Z with:
2024-11-01T16:29:56.7897577Z   submodules: recursive
2024-11-01T16:29:56.7897923Z   fetch-depth: 0
2024-11-01T16:29:56.7898213Z env:
2024-11-01T16:29:56.7898481Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:29:56.7898832Z ##[endgroup]
2024-11-01T16:29:56.8117573Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2024-11-01T16:29:56.8119015Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2024-11-01T16:29:56.8129795Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:29:56.8130341Z env:
2024-11-01T16:29:56.8130628Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:29:56.8130991Z ##[endgroup]
2024-11-01T16:29:56.8228908Z ##[group]Run retry () {
2024-11-01T16:29:56.8229362Z retry () {
2024-11-01T16:29:56.8229887Z   $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
2024-11-01T16:29:56.8230484Z }
2024-11-01T16:29:56.8230799Z echo "${GITHUB_WORKSPACE}"
2024-11-01T16:29:56.8231246Z if [ -z "${NO_SUDO}" ]; then
2024-11-01T16:29:56.8231746Z   retry sudo rm -rf "${GITHUB_WORKSPACE}"
2024-11-01T16:29:56.8232220Z else
2024-11-01T16:29:56.8232570Z   retry rm -rf "${GITHUB_WORKSPACE}"
2024-11-01T16:29:56.8233000Z fi
2024-11-01T16:29:56.8233313Z mkdir "${GITHUB_WORKSPACE}"
2024-11-01T16:29:56.8239592Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:29:56.8240126Z env:
2024-11-01T16:29:56.8240402Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:29:56.8240927Z   NO_SUDO: 
2024-11-01T16:29:56.8241200Z ##[endgroup]
2024-11-01T16:29:56.8267336Z /home/ec2-user/actions-runner/_work/pytorch/pytorch
2024-11-01T16:29:56.9381029Z ##[group]Run malfet/checkout@silent-checkout
2024-11-01T16:29:56.9381493Z with:
2024-11-01T16:29:56.9381832Z   ref: d1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea
2024-11-01T16:29:56.9382555Z   fetch-depth: 0
2024-11-01T16:29:56.9382874Z   submodules: recursive
2024-11-01T16:29:56.9383223Z   quiet-checkout: true
2024-11-01T16:29:56.9383585Z   repository: pytorch/pytorch
2024-11-01T16:29:56.9384126Z   token: ***
2024-11-01T16:29:56.9384406Z   ssh-strict: true
2024-11-01T16:29:56.9384738Z   persist-credentials: true
2024-11-01T16:29:56.9385107Z   clean: true
2024-11-01T16:29:56.9385431Z   sparse-checkout-cone-mode: true
2024-11-01T16:29:56.9385834Z   lfs: false
2024-11-01T16:29:56.9386138Z   set-safe-directory: true
2024-11-01T16:29:56.9386469Z env:
2024-11-01T16:29:56.9386740Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:29:56.9387088Z ##[endgroup]
2024-11-01T16:29:57.0434569Z Syncing repository: pytorch/pytorch
2024-11-01T16:29:57.0436520Z ##[group]Getting Git version info
2024-11-01T16:29:57.0437487Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2024-11-01T16:29:57.0438417Z [command]/usr/bin/git version
2024-11-01T16:29:57.0438840Z git version 2.40.1
2024-11-01T16:29:57.0440548Z ##[endgroup]
2024-11-01T16:29:57.0454911Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/320598b2-6ab9-4365-b6eb-bafbd75dbbdf' before making global git config changes
2024-11-01T16:29:57.0456310Z Adding repository directory to the temporary git global config as a safe directory
2024-11-01T16:29:57.0457557Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2024-11-01T16:29:57.0500439Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2024-11-01T16:29:57.0504807Z ##[group]Initializing the repository
2024-11-01T16:29:57.0508908Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch
2024-11-01T16:29:57.0545760Z hint: Using 'master' as the name for the initial branch. This default branch name
2024-11-01T16:29:57.0547687Z hint: is subject to change. To configure the initial branch name to use in all
2024-11-01T16:29:57.0549252Z hint: of your new repositories, which will suppress this warning, call:
2024-11-01T16:29:57.0549963Z hint: 
2024-11-01T16:29:57.0550629Z hint: 	git config --global init.defaultBranch <name>
2024-11-01T16:29:57.0551216Z hint: 
2024-11-01T16:29:57.0551876Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2024-11-01T16:29:57.0552970Z hint: 'development'. The just-created branch can be renamed via this command:
2024-11-01T16:29:57.0554156Z hint: 
2024-11-01T16:29:57.0554590Z hint: 	git branch -m <name>
2024-11-01T16:29:57.0555543Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/
2024-11-01T16:29:57.0557908Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch
2024-11-01T16:29:57.0592042Z ##[endgroup]
2024-11-01T16:29:57.0592803Z ##[group]Disabling automatic garbage collection
2024-11-01T16:29:57.0595474Z [command]/usr/bin/git config --local gc.auto 0
2024-11-01T16:29:57.0631338Z ##[endgroup]
2024-11-01T16:29:57.0632121Z ##[group]Setting up auth
2024-11-01T16:29:57.0639360Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2024-11-01T16:29:57.0679039Z [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' || :"
2024-11-01T16:29:57.1000488Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2024-11-01T16:29:57.1038539Z [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' || :"
2024-11-01T16:29:57.1350770Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2024-11-01T16:29:57.1402193Z ##[endgroup]
2024-11-01T16:29:57.1402818Z ##[group]Fetching the repository
2024-11-01T16:29:57.1409966Z [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/*
2024-11-01T16:30:00.2402434Z remote: Enumerating objects: 1053425        
2024-11-01T16:30:00.2403573Z remote: Enumerating objects: 1053892, done.        
2024-11-01T16:30:00.2404749Z remote: Counting objects:   0% (1/467)        
2024-11-01T16:30:00.2405278Z remote: Counting objects:   1% (5/467)        
2024-11-01T16:30:00.2405805Z remote: Counting objects:   2% (10/467)        
2024-11-01T16:30:00.2406324Z remote: Counting objects:   3% (15/467)        
2024-11-01T16:30:00.2407441Z remote: Counting objects:   4% (19/467)        
2024-11-01T16:30:00.2408330Z remote: Counting objects:   5% (24/467)        
2024-11-01T16:30:00.2409150Z remote: Counting objects:   6% (29/467)        
2024-11-01T16:30:00.2410132Z remote: Counting objects:   7% (33/467)        
2024-11-01T16:30:00.2410967Z remote: Counting objects:   8% (38/467)        
2024-11-01T16:30:00.2411621Z remote: Counting objects:   9% (43/467)        
2024-11-01T16:30:00.2412544Z remote: Counting objects:  10% (47/467)        
2024-11-01T16:30:00.2413488Z remote: Counting objects:  11% (52/467)        
2024-11-01T16:30:00.2414320Z remote: Counting objects:  12% (57/467)        
2024-11-01T16:30:00.2415044Z remote: Counting objects:  13% (61/467)        
2024-11-01T16:30:00.2415555Z remote: Counting objects:  14% (66/467)        
2024-11-01T16:30:00.2416154Z remote: Counting objects:  15% (71/467)        
2024-11-01T16:30:00.2417103Z remote: Counting objects:  16% (75/467)        
2024-11-01T16:30:00.2418012Z remote: Counting objects:  17% (80/467)        
2024-11-01T16:30:00.2418529Z remote: Counting objects:  18% (85/467)        
2024-11-01T16:30:00.2419226Z remote: Counting objects:  19% (89/467)        
2024-11-01T16:30:00.2420067Z remote: Counting objects:  20% (94/467)        
2024-11-01T16:30:00.2421407Z remote: Counting objects:  21% (99/467)        
2024-11-01T16:30:00.2422339Z remote: Counting objects:  22% (103/467)        
2024-11-01T16:30:00.2423253Z remote: Counting objects:  23% (108/467)        
2024-11-01T16:30:00.2423965Z remote: Counting objects:  24% (113/467)        
2024-11-01T16:30:00.2424905Z remote: Counting objects:  25% (117/467)        
2024-11-01T16:30:00.2425868Z remote: Counting objects:  26% (122/467)        
2024-11-01T16:30:00.2426550Z remote: Counting objects:  27% (127/467)        
2024-11-01T16:30:00.2427072Z remote: Counting objects:  28% (131/467)        
2024-11-01T16:30:00.2427599Z remote: Counting objects:  29% (136/467)        
2024-11-01T16:30:00.2428121Z remote: Counting objects:  30% (141/467)        
2024-11-01T16:30:00.2428645Z remote: Counting objects:  31% (145/467)        
2024-11-01T16:30:00.2429154Z remote: Counting objects:  32% (150/467)        
2024-11-01T16:30:00.2429679Z remote: Counting objects:  33% (155/467)        
2024-11-01T16:30:00.2430204Z remote: Counting objects:  34% (159/467)        
2024-11-01T16:30:00.2430727Z remote: Counting objects:  35% (164/467)        
2024-11-01T16:30:00.2431248Z remote: Counting objects:  36% (169/467)        
2024-11-01T16:30:00.2431774Z remote: Counting objects:  37% (173/467)        
2024-11-01T16:30:00.2432279Z remote: Counting objects:  38% (178/467)        
2024-11-01T16:30:00.2432803Z remote: Counting objects:  39% (183/467)        
2024-11-01T16:30:00.2433322Z remote: Counting objects:  40% (187/467)        
2024-11-01T16:30:00.2433949Z remote: Counting objects:  41% (192/467)        
2024-11-01T16:30:00.2434474Z remote: Counting objects:  42% (197/467)        
2024-11-01T16:30:00.2434994Z remote: Counting objects:  43% (201/467)        
2024-11-01T16:30:00.2435499Z remote: Counting objects:  44% (206/467)        
2024-11-01T16:30:00.2436018Z remote: Counting objects:  45% (211/467)        
2024-11-01T16:30:00.2436538Z remote: Counting objects:  46% (215/467)        
2024-11-01T16:30:00.2437063Z remote: Counting objects:  47% (220/467)        
2024-11-01T16:30:00.2437580Z remote: Counting objects:  48% (225/467)        
2024-11-01T16:30:00.2438087Z remote: Counting objects:  49% (229/467)        
2024-11-01T16:30:00.2438786Z remote: Counting objects:  50% (234/467)        
2024-11-01T16:30:00.2439307Z remote: Counting objects:  51% (239/467)        
2024-11-01T16:30:00.2439829Z remote: Counting objects:  52% (243/467)        
2024-11-01T16:30:00.2440380Z remote: Counting objects:  53% (248/467)        
2024-11-01T16:30:00.2440904Z remote: Counting objects:  54% (253/467)        
2024-11-01T16:30:00.2441424Z remote: Counting objects:  55% (257/467)        
2024-11-01T16:30:00.2441928Z remote: Counting objects:  56% (262/467)        
2024-11-01T16:30:00.2442448Z remote: Counting objects:  57% (267/467)        
2024-11-01T16:30:00.2442966Z remote: Counting objects:  58% (271/467)        
2024-11-01T16:30:00.2443485Z remote: Counting objects:  59% (276/467)        
2024-11-01T16:30:00.2444007Z remote: Counting objects:  60% (281/467)        
2024-11-01T16:30:00.2444509Z remote: Counting objects:  61% (285/467)        
2024-11-01T16:30:00.2445023Z remote: Counting objects:  62% (290/467)        
2024-11-01T16:30:00.2445567Z remote: Counting objects:  63% (295/467)        
2024-11-01T16:30:00.2446091Z remote: Counting objects:  64% (299/467)        
2024-11-01T16:30:00.2446613Z remote: Counting objects:  65% (304/467)        
2024-11-01T16:30:00.2447134Z remote: Counting objects:  66% (309/467)        
2024-11-01T16:30:00.2447653Z remote: Counting objects:  67% (313/467)        
2024-11-01T16:30:00.2448169Z remote: Counting objects:  68% (318/467)        
2024-11-01T16:30:00.2448672Z remote: Counting objects:  69% (323/467)        
2024-11-01T16:30:00.2449189Z remote: Counting objects:  70% (327/467)        
2024-11-01T16:30:00.2449713Z remote: Counting objects:  71% (332/467)        
2024-11-01T16:30:00.2450230Z remote: Counting objects:  72% (337/467)        
2024-11-01T16:30:00.2450813Z remote: Counting objects:  73% (341/467)        
2024-11-01T16:30:00.2451319Z remote: Counting objects:  74% (346/467)        
2024-11-01T16:30:00.2451834Z remote: Counting objects:  75% (351/467)        
2024-11-01T16:30:00.2452364Z remote: Counting objects:  76% (355/467)        
2024-11-01T16:30:00.2452883Z remote: Counting objects:  77% (360/467)        
2024-11-01T16:30:00.2453401Z remote: Counting objects:  78% (365/467)        
2024-11-01T16:30:00.2453915Z remote: Counting objects:  79% (369/467)        
2024-11-01T16:30:00.2454417Z remote: Counting objects:  80% (374/467)        
2024-11-01T16:30:00.2454939Z remote: Counting objects:  81% (379/467)        
2024-11-01T16:30:00.2455462Z remote: Counting objects:  82% (383/467)        
2024-11-01T16:30:00.2455984Z remote: Counting objects:  83% (388/467)        
2024-11-01T16:30:00.2456500Z remote: Counting objects:  84% (393/467)        
2024-11-01T16:30:00.2457000Z remote: Counting objects:  85% (397/467)        
2024-11-01T16:30:00.2457517Z remote: Counting objects:  86% (402/467)        
2024-11-01T16:30:00.2458041Z remote: Counting objects:  87% (407/467)        
2024-11-01T16:30:00.2458566Z remote: Counting objects:  88% (411/467)        
2024-11-01T16:30:00.2459088Z remote: Counting objects:  89% (416/467)        
2024-11-01T16:30:00.2459607Z remote: Counting objects:  90% (421/467)        
2024-11-01T16:30:00.2460107Z remote: Counting objects:  91% (425/467)        
2024-11-01T16:30:00.2460627Z remote: Counting objects:  92% (430/467)        
2024-11-01T16:30:00.2461143Z remote: Counting objects:  93% (435/467)        
2024-11-01T16:30:00.2461659Z remote: Counting objects:  94% (439/467)        
2024-11-01T16:30:00.2462174Z remote: Counting objects:  95% (444/467)        
2024-11-01T16:30:00.2462689Z remote: Counting objects:  96% (449/467)        
2024-11-01T16:30:00.2463192Z remote: Counting objects:  97% (453/467)        
2024-11-01T16:30:00.2463712Z remote: Counting objects:  98% (458/467)        
2024-11-01T16:30:00.2464230Z remote: Counting objects:  99% (463/467)        
2024-11-01T16:30:00.2464754Z remote: Counting objects: 100% (467/467)        
2024-11-01T16:30:00.2465624Z remote: Counting objects: 100% (467/467), done.        
2024-11-01T16:30:00.2466324Z remote: Compressing objects:   0% (1/251)        
2024-11-01T16:30:00.2467224Z remote: Compressing objects:   1% (3/251)        
2024-11-01T16:30:00.2489536Z remote: Compressing objects:   2% (6/251)        
2024-11-01T16:30:00.2709623Z remote: Compressing objects:   3% (8/251)        
2024-11-01T16:30:00.2810460Z remote: Compressing objects:   4% (11/251)        
2024-11-01T16:30:00.2960294Z remote: Compressing objects:   5% (13/251)        
2024-11-01T16:30:00.3026057Z remote: Compressing objects:   6% (16/251)        
2024-11-01T16:30:00.3225111Z remote: Compressing objects:   7% (18/251)        
2024-11-01T16:30:00.3346678Z remote: Compressing objects:   8% (21/251)        
2024-11-01T16:30:00.3521980Z remote: Compressing objects:   9% (23/251)        
2024-11-01T16:30:00.3592533Z remote: Compressing objects:  10% (26/251)        
2024-11-01T16:30:00.3652867Z remote: Compressing objects:  11% (28/251)        
2024-11-01T16:30:00.3664873Z remote: Compressing objects:  12% (31/251)        
2024-11-01T16:30:00.3665728Z remote: Compressing objects:  13% (33/251)        
2024-11-01T16:30:00.3666299Z remote: Compressing objects:  14% (36/251)        
2024-11-01T16:30:00.3667219Z remote: Compressing objects:  15% (38/251)        
2024-11-01T16:30:00.3667841Z remote: Compressing objects:  16% (41/251)        
2024-11-01T16:30:00.3668396Z remote: Compressing objects:  17% (43/251)        
2024-11-01T16:30:00.3668950Z remote: Compressing objects:  18% (46/251)        
2024-11-01T16:30:00.3669503Z remote: Compressing objects:  19% (48/251)        
2024-11-01T16:30:00.3673620Z remote: Compressing objects:  20% (51/251)        
2024-11-01T16:30:00.3679445Z remote: Compressing objects:  21% (53/251)        
2024-11-01T16:30:00.3680227Z remote: Compressing objects:  22% (56/251)        
2024-11-01T16:30:00.3686319Z remote: Compressing objects:  23% (58/251)        
2024-11-01T16:30:00.3686894Z remote: Compressing objects:  24% (61/251)        
2024-11-01T16:30:00.3691399Z remote: Compressing objects:  25% (63/251)        
2024-11-01T16:30:00.3692288Z remote: Compressing objects:  26% (66/251)        
2024-11-01T16:30:00.3692844Z remote: Compressing objects:  27% (68/251)        
2024-11-01T16:30:00.3693403Z remote: Compressing objects:  28% (71/251)        
2024-11-01T16:30:00.3693937Z remote: Compressing objects:  29% (73/251)        
2024-11-01T16:30:00.3697584Z remote: Compressing objects:  30% (76/251)        
2024-11-01T16:30:00.3698411Z remote: Compressing objects:  31% (78/251)        
2024-11-01T16:30:00.3698965Z remote: Compressing objects:  32% (81/251)        
2024-11-01T16:30:00.3699522Z remote: Compressing objects:  33% (83/251)        
2024-11-01T16:30:00.3703481Z remote: Compressing objects:  34% (86/251)        
2024-11-01T16:30:00.3709027Z remote: Compressing objects:  35% (88/251)        
2024-11-01T16:30:00.3715594Z remote: Compressing objects:  36% (91/251)        
2024-11-01T16:30:00.3716376Z remote: Compressing objects:  37% (93/251)        
2024-11-01T16:30:00.3721276Z remote: Compressing objects:  38% (96/251)        
2024-11-01T16:30:00.3722083Z remote: Compressing objects:  39% (98/251)        
2024-11-01T16:30:00.3728030Z remote: Compressing objects:  40% (101/251)        
2024-11-01T16:30:00.3733657Z remote: Compressing objects:  41% (103/251)        
2024-11-01T16:30:00.3737948Z remote: Compressing objects:  42% (106/251)        
2024-11-01T16:30:00.3738711Z remote: Compressing objects:  43% (108/251)        
2024-11-01T16:30:00.3739282Z remote: Compressing objects:  44% (111/251)        
2024-11-01T16:30:00.3739847Z remote: Compressing objects:  45% (113/251)        
2024-11-01T16:30:00.3741884Z remote: Compressing objects:  46% (116/251)        
2024-11-01T16:30:00.3742459Z remote: Compressing objects:  47% (118/251)        
2024-11-01T16:30:00.3745039Z remote: Compressing objects:  48% (121/251)        
2024-11-01T16:30:00.3749388Z remote: Compressing objects:  49% (123/251)        
2024-11-01T16:30:00.3751418Z remote: Compressing objects:  50% (126/251)        
2024-11-01T16:30:00.3756840Z remote: Compressing objects:  51% (129/251)        
2024-11-01T16:30:00.3759341Z remote: Compressing objects:  52% (131/251)        
2024-11-01T16:30:00.3761635Z remote: Compressing objects:  53% (134/251)        
2024-11-01T16:30:00.3762799Z remote: Compressing objects:  54% (136/251)        
2024-11-01T16:30:00.3765088Z remote: Compressing objects:  55% (139/251)        
2024-11-01T16:30:00.3766059Z remote: Compressing objects:  56% (141/251)        
2024-11-01T16:30:00.3766846Z remote: Compressing objects:  57% (144/251)        
2024-11-01T16:30:00.3767429Z remote: Compressing objects:  58% (146/251)        
2024-11-01T16:30:00.3768453Z remote: Compressing objects:  59% (149/251)        
2024-11-01T16:30:00.3769154Z remote: Compressing objects:  60% (151/251)        
2024-11-01T16:30:00.3769742Z remote: Compressing objects:  61% (154/251)        
2024-11-01T16:30:00.3770725Z remote: Compressing objects:  62% (156/251)        
2024-11-01T16:30:00.3771648Z remote: Compressing objects:  63% (159/251)        
2024-11-01T16:30:00.3772684Z remote: Compressing objects:  64% (161/251)        
2024-11-01T16:30:00.3773648Z remote: Compressing objects:  65% (164/251)        
2024-11-01T16:30:00.3774207Z remote: Compressing objects:  66% (166/251)        
2024-11-01T16:30:00.3774751Z remote: Compressing objects:  67% (169/251)        
2024-11-01T16:30:00.3775303Z remote: Compressing objects:  68% (171/251)        
2024-11-01T16:30:00.3775935Z remote: Compressing objects:  69% (174/251)        
2024-11-01T16:30:00.3776586Z remote: Compressing objects:  70% (176/251)        
2024-11-01T16:30:00.3777620Z remote: Compressing objects:  71% (179/251)        
2024-11-01T16:30:00.3778423Z remote: Compressing objects:  72% (181/251)        
2024-11-01T16:30:00.3779126Z remote: Compressing objects:  73% (184/251)        
2024-11-01T16:30:00.3780510Z remote: Compressing objects:  74% (186/251)        
2024-11-01T16:30:00.3781308Z remote: Compressing objects:  75% (189/251)        
2024-11-01T16:30:00.3782347Z remote: Compressing objects:  76% (191/251)        
2024-11-01T16:30:00.3783236Z remote: Compressing objects:  77% (194/251)        
2024-11-01T16:30:00.3784062Z remote: Compressing objects:  78% (196/251)        
2024-11-01T16:30:00.3784674Z remote: Compressing objects:  79% (199/251)        
2024-11-01T16:30:00.3785234Z remote: Compressing objects:  80% (201/251)        
2024-11-01T16:30:00.3786254Z remote: Compressing objects:  81% (204/251)        
2024-11-01T16:30:00.3787221Z remote: Compressing objects:  82% (206/251)        
2024-11-01T16:30:00.3788041Z remote: Compressing objects:  83% (209/251)        
2024-11-01T16:30:00.3788904Z remote: Compressing objects:  84% (211/251)        
2024-11-01T16:30:00.3789950Z remote: Compressing objects:  85% (214/251)        
2024-11-01T16:30:00.3790991Z remote: Compressing objects:  86% (216/251)        
2024-11-01T16:30:00.3792043Z remote: Compressing objects:  87% (219/251)        
2024-11-01T16:30:00.3792874Z remote: Compressing objects:  88% (221/251)        
2024-11-01T16:30:00.3794011Z remote: Compressing objects:  89% (224/251)        
2024-11-01T16:30:00.3794801Z remote: Compressing objects:  90% (226/251)        
2024-11-01T16:30:00.3795682Z remote: Compressing objects:  91% (229/251)        
2024-11-01T16:30:00.3796249Z remote: Compressing objects:  92% (231/251)        
2024-11-01T16:30:00.3796807Z remote: Compressing objects:  93% (234/251)        
2024-11-01T16:30:00.3797366Z remote: Compressing objects:  94% (236/251)        
2024-11-01T16:30:00.3797931Z remote: Compressing objects:  95% (239/251)        
2024-11-01T16:30:00.3798574Z remote: Compressing objects:  96% (241/251)        
2024-11-01T16:30:00.3799114Z remote: Compressing objects:  97% (244/251)        
2024-11-01T16:30:00.3799850Z remote: Compressing objects:  98% (246/251)        
2024-11-01T16:30:00.3800540Z remote: Compressing objects:  99% (249/251)        
2024-11-01T16:30:00.3801196Z remote: Compressing objects: 100% (251/251)        
2024-11-01T16:30:00.3801944Z remote: Compressing objects: 100% (251/251), done.        
2024-11-01T16:30:23.1947480Z remote: Total 1053892 (delta 268), reused 369 (delta 215), pack-reused 1053425 (from 1)        
2024-11-01T16:30:54.8644465Z [command]/usr/bin/git rev-parse --verify --quiet d1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea^{object}
2024-11-01T16:30:54.8673938Z d1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea
2024-11-01T16:30:54.8679569Z ##[endgroup]
2024-11-01T16:30:54.8680188Z ##[group]Determining the checkout info
2024-11-01T16:30:54.8681814Z ##[endgroup]
2024-11-01T16:30:54.8682382Z ##[group]Checking out the ref
2024-11-01T16:30:54.8683738Z [command]/usr/bin/git checkout --quiet --force d1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea
2024-11-01T16:30:56.3414762Z ##[endgroup]
2024-11-01T16:30:56.3415461Z ##[group]Setting up auth for fetching submodules
2024-11-01T16:30:56.3419074Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2024-11-01T16:30:56.3468011Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2024-11-01T16:30:56.3500806Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2024-11-01T16:30:56.3535165Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2024-11-01T16:30:56.3570424Z ##[endgroup]
2024-11-01T16:30:56.3571076Z ##[group]Fetching submodules
2024-11-01T16:30:56.3574613Z [command]/usr/bin/git submodule sync --recursive
2024-11-01T16:30:56.3884028Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2024-11-01T16:30:56.4177512Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni'
2024-11-01T16:30:56.4179558Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16'
2024-11-01T16:30:56.4181972Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv'
2024-11-01T16:30:56.4183730Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK'
2024-11-01T16:30:56.4185329Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX'
2024-11-01T16:30:56.4187558Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator'
2024-11-01T16:30:56.4189779Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK'
2024-11-01T16:30:56.4192601Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark'
2024-11-01T16:30:56.4195871Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel'
2024-11-01T16:30:56.4198832Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib'
2024-11-01T16:30:56.4201881Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo'
2024-11-01T16:30:56.4205403Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend'
2024-11-01T16:30:56.4209148Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass'
2024-11-01T16:30:56.4212651Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen'
2024-11-01T16:30:56.4216158Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm'
2024-11-01T16:30:56.4219801Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers'
2024-11-01T16:30:56.4224930Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'
2024-11-01T16:30:56.4229167Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp'
2024-11-01T16:30:56.4232847Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo'
2024-11-01T16:30:56.4237234Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest'
2024-11-01T16:30:56.4241150Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep'
2024-11-01T16:30:56.4245348Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi'
2024-11-01T16:30:56.4249580Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto'
2024-11-01T16:30:56.4253884Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc'
2024-11-01T16:30:56.4258263Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl'
2024-11-01T16:30:56.4262899Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann'
2024-11-01T16:30:56.4267432Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx'
2024-11-01T16:30:56.4272509Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp'
2024-11-01T16:30:56.4277191Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft'
2024-11-01T16:30:56.4282257Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf'
2024-11-01T16:30:56.4287110Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd'
2024-11-01T16:30:56.4292249Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool'
2024-11-01T16:30:56.4297306Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11'
2024-11-01T16:30:56.4304237Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy'
2024-11-01T16:30:56.4310989Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef'
2024-11-01T16:30:56.4316819Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe'
2024-11-01T16:30:56.4348295Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'...
2024-11-01T16:30:56.7390959Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'...
2024-11-01T16:30:56.9327664Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'...
2024-11-01T16:30:57.1240980Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'...
2024-11-01T16:30:57.4099088Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'...
2024-11-01T16:30:57.8179738Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'...
2024-11-01T16:31:00.0894058Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'...
2024-11-01T16:31:14.5885226Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'...
2024-11-01T16:31:15.0738991Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'...
2024-11-01T16:31:17.0822009Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'...
2024-11-01T16:31:17.7369867Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'...
2024-11-01T16:31:18.4042887Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'...
2024-11-01T16:31:19.6541686Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'...
2024-11-01T16:31:21.9305281Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'...
2024-11-01T16:31:27.8829272Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'...
2024-11-01T16:31:29.8107263Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'...
2024-11-01T16:31:31.7476816Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'...
2024-11-01T16:31:33.2072913Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'...
2024-11-01T16:31:33.6836630Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'...
2024-11-01T16:31:34.0569587Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'...
2024-11-01T16:31:35.2213402Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'...
2024-11-01T16:31:35.6187810Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'...
2024-11-01T16:31:35.8785682Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'...
2024-11-01T16:31:37.4141404Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'...
2024-11-01T16:31:38.2710037Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'...
2024-11-01T16:31:38.6578650Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'...
2024-11-01T16:31:45.3772536Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'...
2024-11-01T16:31:47.8560084Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'...
2024-11-01T16:31:58.1592927Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'...
2024-11-01T16:31:58.4396578Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'...
2024-11-01T16:32:09.7599582Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'...
2024-11-01T16:32:09.9587106Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'...
2024-11-01T16:32:10.1957506Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'...
2024-11-01T16:32:11.3443435Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'...
2024-11-01T16:32:11.6602140Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'...
2024-11-01T16:32:12.4711646Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'...
2024-11-01T16:32:12.9028332Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f'
2024-11-01T16:32:12.9161719Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3'
2024-11-01T16:32:12.9264874Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1'
2024-11-01T16:32:12.9524102Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73'
2024-11-01T16:32:12.9846202Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1'
2024-11-01T16:32:13.0258238Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191'
2024-11-01T16:32:13.8659561Z Submodule path 'third_party/XNNPACK': checked out '87ee0b46b834f67bad9025d4a82ed5654f3403d3'
2024-11-01T16:32:13.8911397Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415'
2024-11-01T16:32:14.1048901Z Submodule path 'third_party/composable_kernel': checked out 'cedccd59c94cb0c74e7ec0d0f6c791aed081febc'
2024-11-01T16:32:14.1541469Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006'
2024-11-01T16:32:14.2547902Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99'
2024-11-01T16:32:14.2910759Z Submodule path 'third_party/cudnn_frontend': checked out '936021bfed8c91dc416af1588b2c4eca631a9e45'
2024-11-01T16:32:14.8083142Z Submodule path 'third_party/cutlass': checked out 'bbe579a9e3beb6ea6626d9227ec32d0dae119a49'
2024-11-01T16:32:15.0700566Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c'
2024-11-01T16:32:15.1649128Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e'
2024-11-01T16:32:15.1667267Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit'
2024-11-01T16:32:15.1669613Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T16:32:15.1672700Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass'
2024-11-01T16:32:15.1675470Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest'
2024-11-01T16:32:15.1678427Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T16:32:15.1708151Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'...
2024-11-01T16:32:16.3267994Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'...
2024-11-01T16:32:16.9601924Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'...
2024-11-01T16:32:19.2189216Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'...
2024-11-01T16:32:20.4240861Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'...
2024-11-01T16:32:20.7642754Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81'
2024-11-01T16:32:20.8633998Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3'
2024-11-01T16:32:21.2751960Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6'
2024-11-01T16:32:21.3396764Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796'
2024-11-01T16:32:21.3537511Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1'
2024-11-01T16:32:21.4750736Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b'
2024-11-01T16:32:21.5158392Z Submodule path 'third_party/fmt': checked out '0c9fce2ffefecfdce794e1859584e25877b7b592'
2024-11-01T16:32:21.5592703Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350'
2024-11-01T16:32:21.5871463Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509'
2024-11-01T16:32:21.6358639Z Submodule path 'third_party/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929'
2024-11-01T16:32:21.6494846Z Submodule path 'third_party/ideep': checked out '41d636c2bbcea6bff0faf97cdb65a48cdde987af'
2024-11-01T16:32:21.6514005Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn'
2024-11-01T16:32:21.6539977Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'...
2024-11-01T16:32:36.7709289Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af'
2024-11-01T16:32:36.7911829Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42'
2024-11-01T16:32:36.8838887Z Submodule path 'third_party/kineto': checked out 'ed052ea024b9468908d558b15cd3f7584fb0f492'
2024-11-01T16:32:36.8858659Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T16:32:36.8861030Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T16:32:36.8864405Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T16:32:36.8894208Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'...
2024-11-01T16:32:37.5120638Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'...
2024-11-01T16:32:38.9912069Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'...
2024-11-01T16:32:40.2645310Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e'
2024-11-01T16:32:40.2663480Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T16:32:40.2665804Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T16:32:40.2668104Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T16:32:40.2670930Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T16:32:40.2673964Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T16:32:40.2677228Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T16:32:40.2680115Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T16:32:40.2683151Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T16:32:40.2714647Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'...
2024-11-01T16:32:41.1698307Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'...
2024-11-01T16:32:41.5676771Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'...
2024-11-01T16:32:43.0550063Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'...
2024-11-01T16:32:43.3684704Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'...
2024-11-01T16:32:43.9952616Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'...
2024-11-01T16:32:45.1910372Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'...
2024-11-01T16:32:52.0074269Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'...
2024-11-01T16:32:52.4376779Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9'
2024-11-01T16:32:52.4578840Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400'
2024-11-01T16:32:52.4971795Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05'
2024-11-01T16:32:52.5111870Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067'
2024-11-01T16:32:52.5126887Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T16:32:52.5155039Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'...
2024-11-01T16:32:52.9017360Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4'
2024-11-01T16:32:52.9212201Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446'
2024-11-01T16:32:52.9652355Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850'
2024-11-01T16:32:53.0807221Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5'
2024-11-01T16:32:53.0980757Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150'
2024-11-01T16:32:53.1399776Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164'
2024-11-01T16:32:53.2016078Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347'
2024-11-01T16:32:53.2416958Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78'
2024-11-01T16:32:53.2737104Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290'
2024-11-01T16:32:53.3815265Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806'
2024-11-01T16:32:53.7545098Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c'
2024-11-01T16:32:53.7610421Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11'
2024-11-01T16:32:53.7614929Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'...
2024-11-01T16:32:54.9242946Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b'
2024-11-01T16:32:54.9955950Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878'
2024-11-01T16:32:54.9975568Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T16:32:54.9978342Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T16:32:54.9980695Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T16:32:54.9983974Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T16:32:54.9987147Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T16:32:54.9989839Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T16:32:54.9992720Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T16:32:54.9995904Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T16:32:55.0026585Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'...
2024-11-01T16:32:55.4699568Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'...
2024-11-01T16:32:56.6437414Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'...
2024-11-01T16:32:56.9861531Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'...
2024-11-01T16:33:03.6459152Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'...
2024-11-01T16:33:03.9323590Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'...
2024-11-01T16:33:04.1775523Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'...
2024-11-01T16:33:04.5428566Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'...
2024-11-01T16:33:11.6404739Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2'
2024-11-01T16:33:11.6840106Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1'
2024-11-01T16:33:11.7017093Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa'
2024-11-01T16:33:11.8055324Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d'
2024-11-01T16:33:11.8198216Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce'
2024-11-01T16:33:11.8355282Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5'
2024-11-01T16:33:11.8520730Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d'
2024-11-01T16:33:11.8537069Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T16:33:11.8539259Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T16:33:11.8569537Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'...
2024-11-01T16:33:14.1748958Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'...
2024-11-01T16:33:15.6413098Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4'
2024-11-01T16:33:15.6893136Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929'
2024-11-01T16:33:16.1725362Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50'
2024-11-01T16:33:16.1859317Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe'
2024-11-01T16:33:16.4821275Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a'
2024-11-01T16:33:16.4845225Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark'
2024-11-01T16:33:16.4847295Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest'
2024-11-01T16:33:16.4874572Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'...
2024-11-01T16:33:16.9460261Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'...
2024-11-01T16:33:18.1396986Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8'
2024-11-01T16:33:18.2150739Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081'
2024-11-01T16:33:18.2249031Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900'
2024-11-01T16:33:18.2383981Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8'
2024-11-01T16:33:18.2765271Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4'
2024-11-01T16:33:18.3069873Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67'
2024-11-01T16:33:18.3510723Z Submodule path 'third_party/sleef': checked out '60e76d2bce17d278b439d9da17177c8f957a9e9b'
2024-11-01T16:33:18.3798542Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e'
2024-11-01T16:33:18.3817417Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest'
2024-11-01T16:33:18.3819498Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop'
2024-11-01T16:33:18.3821733Z Submodule 'third_party/libuv' (https://github.com/libuv/libuv.git) registered for path 'third_party/tensorpipe/third_party/libuv'
2024-11-01T16:33:18.3824434Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T16:33:18.3854282Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/googletest'...
2024-11-01T16:33:19.6092247Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libnop'...
2024-11-01T16:33:19.8486286Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libuv'...
2024-11-01T16:33:21.2545194Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11'...
2024-11-01T16:33:22.4909322Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e'
2024-11-01T16:33:22.5075434Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281'
2024-11-01T16:33:22.5803575Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242'
2024-11-01T16:33:22.6112746Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef'
2024-11-01T16:33:22.6129748Z Submodule 'tools/clang' (https://github.com/wjakob/clang-cindex-python3) registered for path 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T16:33:22.6159623Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11/tools/clang'...
2024-11-01T16:33:22.8289923Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5'
2024-11-01T16:33:22.8326536Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0
2024-11-01T16:33:22.8652679Z Entering 'android/libs/fbjni'
2024-11-01T16:33:22.8702018Z Entering 'third_party/FP16'
2024-11-01T16:33:22.8749138Z Entering 'third_party/FXdiv'
2024-11-01T16:33:22.8795201Z Entering 'third_party/NNPACK'
2024-11-01T16:33:22.8843149Z Entering 'third_party/NVTX'
2024-11-01T16:33:22.8889833Z Entering 'third_party/VulkanMemoryAllocator'
2024-11-01T16:33:22.8936093Z Entering 'third_party/XNNPACK'
2024-11-01T16:33:22.8998857Z Entering 'third_party/benchmark'
2024-11-01T16:33:22.9047409Z Entering 'third_party/composable_kernel'
2024-11-01T16:33:22.9099930Z Entering 'third_party/cpp-httplib'
2024-11-01T16:33:22.9145647Z Entering 'third_party/cpuinfo'
2024-11-01T16:33:22.9192089Z Entering 'third_party/cudnn_frontend'
2024-11-01T16:33:22.9238592Z Entering 'third_party/cutlass'
2024-11-01T16:33:22.9290189Z Entering 'third_party/eigen'
2024-11-01T16:33:22.9334522Z Entering 'third_party/fbgemm'
2024-11-01T16:33:22.9381258Z Entering 'third_party/fbgemm/third_party/asmjit'
2024-11-01T16:33:22.9424495Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T16:33:22.9468721Z Entering 'third_party/fbgemm/third_party/cutlass'
2024-11-01T16:33:22.9518871Z Entering 'third_party/fbgemm/third_party/googletest'
2024-11-01T16:33:22.9560550Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T16:33:22.9604863Z Entering 'third_party/flatbuffers'
2024-11-01T16:33:22.9652784Z Entering 'third_party/fmt'
2024-11-01T16:33:22.9699528Z Entering 'third_party/gemmlowp/gemmlowp'
2024-11-01T16:33:22.9746295Z Entering 'third_party/gloo'
2024-11-01T16:33:22.9791792Z Entering 'third_party/googletest'
2024-11-01T16:33:22.9840908Z Entering 'third_party/ideep'
2024-11-01T16:33:22.9885798Z Entering 'third_party/ideep/mkl-dnn'
2024-11-01T16:33:22.9939813Z Entering 'third_party/ittapi'
2024-11-01T16:33:22.9984478Z Entering 'third_party/kineto'
2024-11-01T16:33:23.0030294Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T16:33:23.0076162Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T16:33:23.0125977Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T16:33:23.0173152Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T16:33:23.0220304Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T16:33:23.0264004Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T16:33:23.0312129Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T16:33:23.0358193Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T16:33:23.0401507Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T16:33:23.0444189Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T16:33:23.0487082Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T16:33:23.0529695Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T16:33:23.0574544Z Entering 'third_party/mimalloc'
2024-11-01T16:33:23.0627163Z Entering 'third_party/nccl/nccl'
2024-11-01T16:33:23.0674458Z Entering 'third_party/nlohmann'
2024-11-01T16:33:23.0723214Z Entering 'third_party/onnx'
2024-11-01T16:33:23.0787894Z Entering 'third_party/onnx/third_party/pybind11'
2024-11-01T16:33:23.0838948Z Entering 'third_party/opentelemetry-cpp'
2024-11-01T16:33:23.0889386Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T16:33:23.0935395Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T16:33:23.0981549Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T16:33:23.1027004Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T16:33:23.1074509Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T16:33:23.1120922Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T16:33:23.1164804Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T16:33:23.1206064Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T16:33:23.1250077Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T16:33:23.1293258Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T16:33:23.1355919Z Entering 'third_party/pocketfft'
2024-11-01T16:33:23.1399314Z Entering 'third_party/protobuf'
2024-11-01T16:33:23.1448048Z Entering 'third_party/protobuf/third_party/benchmark'
2024-11-01T16:33:23.1492689Z Entering 'third_party/protobuf/third_party/googletest'
2024-11-01T16:33:23.1540130Z Entering 'third_party/psimd'
2024-11-01T16:33:23.1586019Z Entering 'third_party/pthreadpool'
2024-11-01T16:33:23.1634541Z Entering 'third_party/pybind11'
2024-11-01T16:33:23.1681662Z Entering 'third_party/python-peachpy'
2024-11-01T16:33:23.1725470Z Entering 'third_party/sleef'
2024-11-01T16:33:23.1772162Z Entering 'third_party/tensorpipe'
2024-11-01T16:33:23.1816020Z Entering 'third_party/tensorpipe/third_party/googletest'
2024-11-01T16:33:23.1857381Z Entering 'third_party/tensorpipe/third_party/libnop'
2024-11-01T16:33:23.1898796Z Entering 'third_party/tensorpipe/third_party/libuv'
2024-11-01T16:33:23.1940709Z Entering 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T16:33:23.1980625Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T16:33:23.2038796Z ##[endgroup]
2024-11-01T16:33:23.2040478Z ##[group]Persisting credentials for submodules
2024-11-01T16:33:23.2044575Z [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' || :"
2024-11-01T16:33:23.2366512Z Entering 'android/libs/fbjni'
2024-11-01T16:33:23.2432929Z Entering 'third_party/FP16'
2024-11-01T16:33:23.2493057Z Entering 'third_party/FXdiv'
2024-11-01T16:33:23.2552861Z Entering 'third_party/NNPACK'
2024-11-01T16:33:23.2613039Z Entering 'third_party/NVTX'
2024-11-01T16:33:23.2674556Z Entering 'third_party/VulkanMemoryAllocator'
2024-11-01T16:33:23.2733189Z Entering 'third_party/XNNPACK'
2024-11-01T16:33:23.2812082Z Entering 'third_party/benchmark'
2024-11-01T16:33:23.2873978Z Entering 'third_party/composable_kernel'
2024-11-01T16:33:23.2940467Z Entering 'third_party/cpp-httplib'
2024-11-01T16:33:23.2996792Z Entering 'third_party/cpuinfo'
2024-11-01T16:33:23.3053023Z Entering 'third_party/cudnn_frontend'
2024-11-01T16:33:23.3105146Z Entering 'third_party/cutlass'
2024-11-01T16:33:23.3167702Z Entering 'third_party/eigen'
2024-11-01T16:33:23.3229339Z Entering 'third_party/fbgemm'
2024-11-01T16:33:23.3285073Z Entering 'third_party/fbgemm/third_party/asmjit'
2024-11-01T16:33:23.3339948Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T16:33:23.3392967Z Entering 'third_party/fbgemm/third_party/cutlass'
2024-11-01T16:33:23.3453463Z Entering 'third_party/fbgemm/third_party/googletest'
2024-11-01T16:33:23.3509656Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T16:33:23.3571367Z Entering 'third_party/flatbuffers'
2024-11-01T16:33:23.3634104Z Entering 'third_party/fmt'
2024-11-01T16:33:23.3689131Z Entering 'third_party/gemmlowp/gemmlowp'
2024-11-01T16:33:23.3744507Z Entering 'third_party/gloo'
2024-11-01T16:33:23.3799003Z Entering 'third_party/googletest'
2024-11-01T16:33:23.3855420Z Entering 'third_party/ideep'
2024-11-01T16:33:23.3912459Z Entering 'third_party/ideep/mkl-dnn'
2024-11-01T16:33:23.3977728Z Entering 'third_party/ittapi'
2024-11-01T16:33:23.4033963Z Entering 'third_party/kineto'
2024-11-01T16:33:23.4087037Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T16:33:23.4139297Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T16:33:23.4194099Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T16:33:23.4251662Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T16:33:23.4305302Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T16:33:23.4357556Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T16:33:23.4413885Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T16:33:23.4472025Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T16:33:23.4529824Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T16:33:23.4586449Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T16:33:23.4650017Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T16:33:23.4705276Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T16:33:23.4764911Z Entering 'third_party/mimalloc'
2024-11-01T16:33:23.4830115Z Entering 'third_party/nccl/nccl'
2024-11-01T16:33:23.4894306Z Entering 'third_party/nlohmann'
2024-11-01T16:33:23.4954396Z Entering 'third_party/onnx'
2024-11-01T16:33:23.5026484Z Entering 'third_party/onnx/third_party/pybind11'
2024-11-01T16:33:23.5088050Z Entering 'third_party/opentelemetry-cpp'
2024-11-01T16:33:23.5153336Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T16:33:23.5212853Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T16:33:23.5269389Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T16:33:23.5327497Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T16:33:23.5385518Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T16:33:23.5445009Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T16:33:23.5503609Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T16:33:23.5561334Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T16:33:23.5622828Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T16:33:23.5685554Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T16:33:23.5766821Z Entering 'third_party/pocketfft'
2024-11-01T16:33:23.5828731Z Entering 'third_party/protobuf'
2024-11-01T16:33:23.5893302Z Entering 'third_party/protobuf/third_party/benchmark'
2024-11-01T16:33:23.5953934Z Entering 'third_party/protobuf/third_party/googletest'
2024-11-01T16:33:23.6016045Z Entering 'third_party/psimd'
2024-11-01T16:33:23.6077213Z Entering 'third_party/pthreadpool'
2024-11-01T16:33:23.6135434Z Entering 'third_party/pybind11'
2024-11-01T16:33:23.6194088Z Entering 'third_party/python-peachpy'
2024-11-01T16:33:23.6254548Z Entering 'third_party/sleef'
2024-11-01T16:33:23.6307648Z Entering 'third_party/tensorpipe'
2024-11-01T16:33:23.6361868Z Entering 'third_party/tensorpipe/third_party/googletest'
2024-11-01T16:33:23.6414174Z Entering 'third_party/tensorpipe/third_party/libnop'
2024-11-01T16:33:23.6467385Z Entering 'third_party/tensorpipe/third_party/libuv'
2024-11-01T16:33:23.6525548Z Entering 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T16:33:23.6585727Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T16:33:23.6663856Z [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"
2024-11-01T16:33:23.6969152Z Entering 'android/libs/fbjni'
2024-11-01T16:33:23.7022103Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2024-11-01T16:33:23.7037910Z Entering 'third_party/FP16'
2024-11-01T16:33:23.7097149Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2024-11-01T16:33:23.7114758Z Entering 'third_party/FXdiv'
2024-11-01T16:33:23.7169926Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2024-11-01T16:33:23.7184675Z Entering 'third_party/NNPACK'
2024-11-01T16:33:23.7244248Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2024-11-01T16:33:23.7253336Z Entering 'third_party/NVTX'
2024-11-01T16:33:23.7309049Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config	remote.origin.url
2024-11-01T16:33:23.7327571Z Entering 'third_party/VulkanMemoryAllocator'
2024-11-01T16:33:23.7382816Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2024-11-01T16:33:23.7398368Z Entering 'third_party/XNNPACK'
2024-11-01T16:33:23.7454017Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
2024-11-01T16:33:23.7490110Z Entering 'third_party/benchmark'
2024-11-01T16:33:23.7548114Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
2024-11-01T16:33:23.7564484Z Entering 'third_party/composable_kernel'
2024-11-01T16:33:23.7621377Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config	remote.origin.url
2024-11-01T16:33:23.7644671Z Entering 'third_party/cpp-httplib'
2024-11-01T16:33:23.7702836Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config	remote.origin.url
2024-11-01T16:33:23.7720460Z Entering 'third_party/cpuinfo'
2024-11-01T16:33:23.7776744Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
2024-11-01T16:33:23.7793471Z Entering 'third_party/cudnn_frontend'
2024-11-01T16:33:23.7846748Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
2024-11-01T16:33:23.7861249Z Entering 'third_party/cutlass'
2024-11-01T16:33:23.7911640Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
2024-11-01T16:33:23.7934816Z Entering 'third_party/eigen'
2024-11-01T16:33:23.7985325Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
2024-11-01T16:33:23.8003304Z Entering 'third_party/fbgemm'
2024-11-01T16:33:23.8057380Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2024-11-01T16:33:23.8072747Z Entering 'third_party/fbgemm/third_party/asmjit'
2024-11-01T16:33:23.8128738Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
2024-11-01T16:33:23.8145119Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T16:33:23.8203657Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
2024-11-01T16:33:23.8220272Z Entering 'third_party/fbgemm/third_party/cutlass'
2024-11-01T16:33:23.8275724Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cutlass/config	remote.origin.url
2024-11-01T16:33:23.8300423Z Entering 'third_party/fbgemm/third_party/googletest'
2024-11-01T16:33:23.8355676Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
2024-11-01T16:33:23.8373732Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T16:33:23.8428352Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
2024-11-01T16:33:23.8446194Z Entering 'third_party/flatbuffers'
2024-11-01T16:33:23.8503644Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2024-11-01T16:33:23.8523590Z Entering 'third_party/fmt'
2024-11-01T16:33:23.8577149Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
2024-11-01T16:33:23.8593887Z Entering 'third_party/gemmlowp/gemmlowp'
2024-11-01T16:33:23.8651517Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2024-11-01T16:33:23.8667898Z Entering 'third_party/gloo'
2024-11-01T16:33:23.8726482Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
2024-11-01T16:33:23.8743082Z Entering 'third_party/googletest'
2024-11-01T16:33:23.8799651Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2024-11-01T16:33:23.8817801Z Entering 'third_party/ideep'
2024-11-01T16:33:23.8876013Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2024-11-01T16:33:23.8892453Z Entering 'third_party/ideep/mkl-dnn'
2024-11-01T16:33:23.8949893Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2024-11-01T16:33:23.8975684Z Entering 'third_party/ittapi'
2024-11-01T16:33:23.9038816Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
2024-11-01T16:33:23.9056489Z Entering 'third_party/kineto'
2024-11-01T16:33:23.9111877Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2024-11-01T16:33:23.9128620Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T16:33:23.9189377Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config	remote.origin.url
2024-11-01T16:33:23.9207012Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T16:33:23.9266026Z 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
2024-11-01T16:33:23.9285742Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T16:33:23.9346143Z 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
2024-11-01T16:33:23.9363944Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T16:33:23.9421289Z 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
2024-11-01T16:33:23.9438036Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T16:33:23.9494749Z 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
2024-11-01T16:33:23.9509605Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T16:33:23.9562230Z 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
2024-11-01T16:33:23.9579717Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T16:33:23.9631877Z 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
2024-11-01T16:33:23.9648134Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T16:33:23.9701755Z 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
2024-11-01T16:33:23.9718890Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T16:33:23.9770050Z 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
2024-11-01T16:33:23.9785840Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T16:33:23.9839558Z 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
2024-11-01T16:33:23.9858833Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T16:33:23.9910840Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config	remote.origin.url
2024-11-01T16:33:23.9926038Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T16:33:23.9976660Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config	remote.origin.url
2024-11-01T16:33:23.9993622Z Entering 'third_party/mimalloc'
2024-11-01T16:33:24.0047304Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config	remote.origin.url
2024-11-01T16:33:24.0062877Z Entering 'third_party/nccl/nccl'
2024-11-01T16:33:24.0115319Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nccl/nccl/config	remote.origin.url
2024-11-01T16:33:24.0131758Z Entering 'third_party/nlohmann'
2024-11-01T16:33:24.0185852Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config	remote.origin.url
2024-11-01T16:33:24.0203033Z Entering 'third_party/onnx'
2024-11-01T16:33:24.0262567Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config	remote.origin.url
2024-11-01T16:33:24.0298671Z Entering 'third_party/onnx/third_party/pybind11'
2024-11-01T16:33:24.0359962Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2024-11-01T16:33:24.0378320Z Entering 'third_party/opentelemetry-cpp'
2024-11-01T16:33:24.0439265Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config	remote.origin.url
2024-11-01T16:33:24.0458432Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T16:33:24.0526312Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config	remote.origin.url
2024-11-01T16:33:24.0541991Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T16:33:24.0602068Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config	remote.origin.url
2024-11-01T16:33:24.0621248Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T16:33:24.0679122Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config	remote.origin.url
2024-11-01T16:33:24.0695540Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T16:33:24.0752101Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config	remote.origin.url
2024-11-01T16:33:24.0769306Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T16:33:24.0825457Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config	remote.origin.url
2024-11-01T16:33:24.0842508Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T16:33:24.0896429Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config	remote.origin.url
2024-11-01T16:33:24.0913967Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T16:33:24.0973901Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config	remote.origin.url
2024-11-01T16:33:24.0989833Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T16:33:24.1050271Z 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
2024-11-01T16:33:24.1068772Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T16:33:24.1136084Z 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
2024-11-01T16:33:24.1161189Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T16:33:24.1220408Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config	remote.origin.url
2024-11-01T16:33:24.1282090Z Entering 'third_party/pocketfft'
2024-11-01T16:33:24.1342727Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config	remote.origin.url
2024-11-01T16:33:24.1360453Z Entering 'third_party/protobuf'
2024-11-01T16:33:24.1419011Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config	remote.origin.url
2024-11-01T16:33:24.1440425Z Entering 'third_party/protobuf/third_party/benchmark'
2024-11-01T16:33:24.1497114Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config	remote.origin.url
2024-11-01T16:33:24.1513433Z Entering 'third_party/protobuf/third_party/googletest'
2024-11-01T16:33:24.1570826Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config	remote.origin.url
2024-11-01T16:33:24.1591107Z Entering 'third_party/psimd'
2024-11-01T16:33:24.1646748Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config	remote.origin.url
2024-11-01T16:33:24.1662311Z Entering 'third_party/pthreadpool'
2024-11-01T16:33:24.1719302Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config	remote.origin.url
2024-11-01T16:33:24.1736332Z Entering 'third_party/pybind11'
2024-11-01T16:33:24.1793402Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config	remote.origin.url
2024-11-01T16:33:24.1812124Z Entering 'third_party/python-peachpy'
2024-11-01T16:33:24.1866939Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config	remote.origin.url
2024-11-01T16:33:24.1884693Z Entering 'third_party/sleef'
2024-11-01T16:33:24.1941863Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config	remote.origin.url
2024-11-01T16:33:24.1959432Z Entering 'third_party/tensorpipe'
2024-11-01T16:33:24.2017966Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config	remote.origin.url
2024-11-01T16:33:24.2034542Z Entering 'third_party/tensorpipe/third_party/googletest'
2024-11-01T16:33:24.2090159Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config	remote.origin.url
2024-11-01T16:33:24.2106817Z Entering 'third_party/tensorpipe/third_party/libnop'
2024-11-01T16:33:24.2161483Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config	remote.origin.url
2024-11-01T16:33:24.2178827Z Entering 'third_party/tensorpipe/third_party/libuv'
2024-11-01T16:33:24.2236826Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config	remote.origin.url
2024-11-01T16:33:24.2253310Z Entering 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T16:33:24.2310320Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config	remote.origin.url
2024-11-01T16:33:24.2325853Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T16:33:24.2385299Z 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
2024-11-01T16:33:24.3243582Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
2024-11-01T16:33:24.3579186Z Entering 'android/libs/fbjni'
2024-11-01T16:33:24.3630517Z Entering 'third_party/FP16'
2024-11-01T16:33:24.3678767Z Entering 'third_party/FXdiv'
2024-11-01T16:33:24.3724278Z Entering 'third_party/NNPACK'
2024-11-01T16:33:24.3771433Z Entering 'third_party/NVTX'
2024-11-01T16:33:24.3817867Z Entering 'third_party/VulkanMemoryAllocator'
2024-11-01T16:33:24.3863464Z Entering 'third_party/XNNPACK'
2024-11-01T16:33:24.3939271Z Entering 'third_party/benchmark'
2024-11-01T16:33:24.3990049Z Entering 'third_party/composable_kernel'
2024-11-01T16:33:24.4046641Z Entering 'third_party/cpp-httplib'
2024-11-01T16:33:24.4098036Z Entering 'third_party/cpuinfo'
2024-11-01T16:33:24.4149960Z Entering 'third_party/cudnn_frontend'
2024-11-01T16:33:24.4202182Z Entering 'third_party/cutlass'
2024-11-01T16:33:24.4260449Z Entering 'third_party/eigen'
2024-11-01T16:33:24.4314000Z Entering 'third_party/fbgemm'
2024-11-01T16:33:24.4362214Z Entering 'third_party/fbgemm/third_party/asmjit'
2024-11-01T16:33:24.4407335Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T16:33:24.4452466Z Entering 'third_party/fbgemm/third_party/cutlass'
2024-11-01T16:33:24.4509123Z Entering 'third_party/fbgemm/third_party/googletest'
2024-11-01T16:33:24.4556273Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T16:33:24.4604166Z Entering 'third_party/flatbuffers'
2024-11-01T16:33:24.4657623Z Entering 'third_party/fmt'
2024-11-01T16:33:24.4705724Z Entering 'third_party/gemmlowp/gemmlowp'
2024-11-01T16:33:24.4757271Z Entering 'third_party/gloo'
2024-11-01T16:33:24.4805180Z Entering 'third_party/googletest'
2024-11-01T16:33:24.4857183Z Entering 'third_party/ideep'
2024-11-01T16:33:24.4903596Z Entering 'third_party/ideep/mkl-dnn'
2024-11-01T16:33:24.4964464Z Entering 'third_party/ittapi'
2024-11-01T16:33:24.5014505Z Entering 'third_party/kineto'
2024-11-01T16:33:24.5065192Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T16:33:24.5111817Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T16:33:24.5159449Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T16:33:24.5204040Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T16:33:24.5252097Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T16:33:24.5296535Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T16:33:24.5345484Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T16:33:24.5393548Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T16:33:24.5443131Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T16:33:24.5495700Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T16:33:24.5549286Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T16:33:24.5597737Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T16:33:24.5646331Z Entering 'third_party/mimalloc'
2024-11-01T16:33:24.5694214Z Entering 'third_party/nccl/nccl'
2024-11-01T16:33:24.5742100Z Entering 'third_party/nlohmann'
2024-11-01T16:33:24.5790500Z Entering 'third_party/onnx'
2024-11-01T16:33:24.5856261Z Entering 'third_party/onnx/third_party/pybind11'
2024-11-01T16:33:24.5905791Z Entering 'third_party/opentelemetry-cpp'
2024-11-01T16:33:24.5956025Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T16:33:24.6000888Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T16:33:24.6048019Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T16:33:24.6093320Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T16:33:24.6141046Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T16:33:24.6186335Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T16:33:24.6232442Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T16:33:24.6277681Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T16:33:24.6324581Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T16:33:24.6369121Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T16:33:24.6436556Z Entering 'third_party/pocketfft'
2024-11-01T16:33:24.6485943Z Entering 'third_party/protobuf'
2024-11-01T16:33:24.6538179Z Entering 'third_party/protobuf/third_party/benchmark'
2024-11-01T16:33:24.6583962Z Entering 'third_party/protobuf/third_party/googletest'
2024-11-01T16:33:24.6630451Z Entering 'third_party/psimd'
2024-11-01T16:33:24.6677662Z Entering 'third_party/pthreadpool'
2024-11-01T16:33:24.6724577Z Entering 'third_party/pybind11'
2024-11-01T16:33:24.6767279Z Entering 'third_party/python-peachpy'
2024-11-01T16:33:24.6809365Z Entering 'third_party/sleef'
2024-11-01T16:33:24.6850439Z Entering 'third_party/tensorpipe'
2024-11-01T16:33:24.6891805Z Entering 'third_party/tensorpipe/third_party/googletest'
2024-11-01T16:33:24.6932269Z Entering 'third_party/tensorpipe/third_party/libnop'
2024-11-01T16:33:24.6971012Z Entering 'third_party/tensorpipe/third_party/libuv'
2024-11-01T16:33:24.7010643Z Entering 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T16:33:24.7050199Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T16:33:24.7108705Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:'
2024-11-01T16:33:24.7427204Z Entering 'android/libs/fbjni'
2024-11-01T16:33:24.7476108Z Entering 'third_party/FP16'
2024-11-01T16:33:24.7523211Z Entering 'third_party/FXdiv'
2024-11-01T16:33:24.7571046Z Entering 'third_party/NNPACK'
2024-11-01T16:33:24.7619095Z Entering 'third_party/NVTX'
2024-11-01T16:33:24.7664955Z Entering 'third_party/VulkanMemoryAllocator'
2024-11-01T16:33:24.7708268Z Entering 'third_party/XNNPACK'
2024-11-01T16:33:24.7769600Z Entering 'third_party/benchmark'
2024-11-01T16:33:24.7812697Z Entering 'third_party/composable_kernel'
2024-11-01T16:33:24.7860170Z Entering 'third_party/cpp-httplib'
2024-11-01T16:33:24.7903923Z Entering 'third_party/cpuinfo'
2024-11-01T16:33:24.7950757Z Entering 'third_party/cudnn_frontend'
2024-11-01T16:33:24.7997416Z Entering 'third_party/cutlass'
2024-11-01T16:33:24.8048857Z Entering 'third_party/eigen'
2024-11-01T16:33:24.8091951Z Entering 'third_party/fbgemm'
2024-11-01T16:33:24.8135271Z Entering 'third_party/fbgemm/third_party/asmjit'
2024-11-01T16:33:24.8178070Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T16:33:24.8220833Z Entering 'third_party/fbgemm/third_party/cutlass'
2024-11-01T16:33:24.8268664Z Entering 'third_party/fbgemm/third_party/googletest'
2024-11-01T16:33:24.8309795Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T16:33:24.8351593Z Entering 'third_party/flatbuffers'
2024-11-01T16:33:24.8396709Z Entering 'third_party/fmt'
2024-11-01T16:33:24.8440573Z Entering 'third_party/gemmlowp/gemmlowp'
2024-11-01T16:33:24.8532493Z Entering 'third_party/gloo'
2024-11-01T16:33:24.8536377Z Entering 'third_party/googletest'
2024-11-01T16:33:24.8583596Z Entering 'third_party/ideep'
2024-11-01T16:33:24.8630200Z Entering 'third_party/ideep/mkl-dnn'
2024-11-01T16:33:24.8681389Z Entering 'third_party/ittapi'
2024-11-01T16:33:24.8723729Z Entering 'third_party/kineto'
2024-11-01T16:33:24.8765954Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T16:33:24.8808029Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T16:33:24.8856496Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T16:33:24.8903101Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T16:33:24.8950019Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T16:33:24.8995847Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T16:33:24.9042832Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T16:33:24.9084532Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T16:33:24.9124002Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T16:33:24.9164340Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T16:33:24.9205114Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T16:33:24.9247981Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T16:33:24.9293035Z Entering 'third_party/mimalloc'
2024-11-01T16:33:24.9336912Z Entering 'third_party/nccl/nccl'
2024-11-01T16:33:24.9380376Z Entering 'third_party/nlohmann'
2024-11-01T16:33:24.9425470Z Entering 'third_party/onnx'
2024-11-01T16:33:24.9482626Z Entering 'third_party/onnx/third_party/pybind11'
2024-11-01T16:33:24.9526720Z Entering 'third_party/opentelemetry-cpp'
2024-11-01T16:33:24.9569117Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T16:33:24.9608883Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T16:33:24.9649610Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T16:33:24.9692614Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T16:33:24.9739402Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T16:33:24.9783581Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T16:33:24.9827080Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T16:33:24.9869365Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T16:33:24.9917470Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T16:33:24.9963612Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T16:33:25.0032588Z Entering 'third_party/pocketfft'
2024-11-01T16:33:25.0079059Z Entering 'third_party/protobuf'
2024-11-01T16:33:25.0129170Z Entering 'third_party/protobuf/third_party/benchmark'
2024-11-01T16:33:25.0174610Z Entering 'third_party/protobuf/third_party/googletest'
2024-11-01T16:33:25.0220369Z Entering 'third_party/psimd'
2024-11-01T16:33:25.0267922Z Entering 'third_party/pthreadpool'
2024-11-01T16:33:25.0310608Z Entering 'third_party/pybind11'
2024-11-01T16:33:25.0355967Z Entering 'third_party/python-peachpy'
2024-11-01T16:33:25.0402335Z Entering 'third_party/sleef'
2024-11-01T16:33:25.0446730Z Entering 'third_party/tensorpipe'
2024-11-01T16:33:25.0487479Z Entering 'third_party/tensorpipe/third_party/googletest'
2024-11-01T16:33:25.0528248Z Entering 'third_party/tensorpipe/third_party/libnop'
2024-11-01T16:33:25.0572208Z Entering 'third_party/tensorpipe/third_party/libuv'
2024-11-01T16:33:25.0616004Z Entering 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T16:33:25.0655262Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T16:33:25.0712429Z ##[endgroup]
2024-11-01T16:33:25.0755756Z [command]/usr/bin/git log -1 --format='%H'
2024-11-01T16:33:25.0786120Z 'd1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea'
2024-11-01T16:33:25.1011865Z Prepare all required actions
2024-11-01T16:33:25.1012438Z Getting action download info
2024-11-01T16:33:25.2546430Z ##[group]Run ./.github/actions/setup-linux
2024-11-01T16:33:25.2546864Z env:
2024-11-01T16:33:25.2547152Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:25.2547510Z ##[endgroup]
2024-11-01T16:33:25.2620742Z ##[group]Run set -euo pipefail
2024-11-01T16:33:25.2621226Z set -euo pipefail
2024-11-01T16:33:25.2621616Z function get_ec2_metadata() {
2024-11-01T16:33:25.2622182Z   # Pulled from instance metadata endpoint for EC2
2024-11-01T16:33:25.2623154Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2024-11-01T16:33:25.2623991Z   category=$1
2024-11-01T16:33:25.2624548Z   # If it is GCP runner (runner name contains gcp), do not run this
2024-11-01T16:33:25.2625217Z   runner_name_str=i-00163257c71a08003
2024-11-01T16:33:25.2625739Z   if [[ -f /.inarc ]]; then
2024-11-01T16:33:25.2626254Z     echo "ARC Runner, no info on ec2 metadata"
2024-11-01T16:33:25.2626839Z   elif [[ $runner_name_str == *"gcp"* ]]; then
2024-11-01T16:33:25.2627577Z     echo "Runner is from Google Cloud Platform, No info on ec2 metadata"
2024-11-01T16:33:25.2628217Z   else
2024-11-01T16:33:25.2629547Z     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}"
2024-11-01T16:33:25.2630966Z   fi
2024-11-01T16:33:25.2631253Z }
2024-11-01T16:33:25.2631607Z echo "ami-id: $(get_ec2_metadata ami-id)"
2024-11-01T16:33:25.2632224Z echo "instance-id: $(get_ec2_metadata instance-id)"
2024-11-01T16:33:25.2632913Z echo "instance-type: $(get_ec2_metadata instance-type)"
2024-11-01T16:33:25.2633513Z echo "system info $(uname -a)"
2024-11-01T16:33:25.2644151Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:25.2644685Z env:
2024-11-01T16:33:25.2644956Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:25.2645317Z ##[endgroup]
2024-11-01T16:33:25.2812508Z ami-id: ami-0fff1b9a61dec8a5f
2024-11-01T16:33:25.2929796Z instance-id: i-00163257c71a08003
2024-11-01T16:33:25.3030194Z instance-type: c5.2xlarge
2024-11-01T16:33:25.3042428Z system info Linux ip-10-0-72-177.ec2.internal 6.1.109-118.189.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Sep 10 08:59:12 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
2024-11-01T16:33:25.3068299Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2024-11-01T16:33:25.3070070Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2024-11-01T16:33:25.3077722Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:25.3078249Z env:
2024-11-01T16:33:25.3078536Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:25.3078897Z ##[endgroup]
2024-11-01T16:33:25.3136978Z ##[group]Run if systemctl is-active --quiet docker; then
2024-11-01T16:33:25.3137619Z if systemctl is-active --quiet docker; then
2024-11-01T16:33:25.3138175Z     echo "Docker daemon is running...";
2024-11-01T16:33:25.3138780Z else
2024-11-01T16:33:25.3139295Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2024-11-01T16:33:25.3139913Z fi
2024-11-01T16:33:25.3145844Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:25.3146370Z env:
2024-11-01T16:33:25.3147190Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:25.3147612Z ##[endgroup]
2024-11-01T16:33:25.3234326Z Docker daemon is running...
2024-11-01T16:33:25.3523346Z ##[group]Run nick-fields/retry@v3.0.0
2024-11-01T16:33:25.3523784Z with:
2024-11-01T16:33:25.3524054Z   shell: bash
2024-11-01T16:33:25.3524575Z   timeout_minutes: 5
2024-11-01T16:33:25.3524908Z   max_attempts: 3
2024-11-01T16:33:25.3525211Z   retry_wait_seconds: 30
2024-11-01T16:33:25.3528548Z   command: AWS_ACCOUNT_ID=$(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

2024-11-01T16:33:25.3532011Z   polling_interval_seconds: 1
2024-11-01T16:33:25.3532401Z   warning_on_retry: true
2024-11-01T16:33:25.3532747Z   continue_on_error: false
2024-11-01T16:33:25.3533091Z env:
2024-11-01T16:33:25.3533367Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:25.3533725Z   AWS_RETRY_MODE: standard
2024-11-01T16:33:25.3534084Z   AWS_MAX_ATTEMPTS: 5
2024-11-01T16:33:25.3534415Z   AWS_DEFAULT_REGION: us-east-1
2024-11-01T16:33:25.3534793Z ##[endgroup]
2024-11-01T16:33:26.7269497Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2024-11-01T16:33:26.7270404Z Configure a credential helper to remove this warning. See
2024-11-01T16:33:26.7271479Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2024-11-01T16:33:26.7272075Z 
2024-11-01T16:33:26.7272188Z Login Succeeded
2024-11-01T16:33:27.4392849Z Command completed after 1 attempt(s).
2024-11-01T16:33:27.4477924Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2024-11-01T16:33:27.4479179Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2024-11-01T16:33:27.4480326Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2024-11-01T16:33:27.4491890Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:27.4492734Z env:
2024-11-01T16:33:27.4493194Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:27.4493776Z ##[endgroup]
2024-11-01T16:33:27.4598125Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2024-11-01T16:33:27.4599019Z # ignore expansion of "docker ps -q" since it could be empty
2024-11-01T16:33:27.4599744Z # shellcheck disable=SC2046
2024-11-01T16:33:27.4600258Z docker stop $(docker ps -q) || true
2024-11-01T16:33:27.4600767Z # Prune all of the docker images
2024-11-01T16:33:27.4601239Z docker system prune -af
2024-11-01T16:33:27.4608190Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:27.4608713Z env:
2024-11-01T16:33:27.4608997Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:27.4609358Z ##[endgroup]
2024-11-01T16:33:27.4878725Z "docker stop" requires at least 1 argument.
2024-11-01T16:33:27.4879514Z See 'docker stop --help'.
2024-11-01T16:33:27.4879765Z 
2024-11-01T16:33:27.4880033Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2024-11-01T16:33:27.4880432Z 
2024-11-01T16:33:27.4880589Z Stop one or more running containers
2024-11-01T16:33:27.5050691Z Total reclaimed space: 0B
2024-11-01T16:33:27.5091619Z ##[group]Run set +e
2024-11-01T16:33:27.5092042Z set +e
2024-11-01T16:33:27.5092378Z set -x
2024-11-01T16:33:27.5092850Z 
2024-11-01T16:33:27.5093181Z PT_DOMAIN=download.pytorch.org
2024-11-01T16:33:27.5094033Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400,
2024-11-01T16:33:27.5095215Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last
2024-11-01T16:33:27.5096023Z # one is returned at random
2024-11-01T16:33:27.5096598Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1)
2024-11-01T16:33:27.5097125Z 
2024-11-01T16:33:27.5097601Z if [ -z "${RESOLVED_IP}" ]; then
2024-11-01T16:33:27.5098264Z   echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..."
2024-11-01T16:33:27.5099074Z   RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1)
2024-11-01T16:33:27.5099669Z 
2024-11-01T16:33:27.5099981Z   if [ -z "${RESOLVED_IP}" ]; then
2024-11-01T16:33:27.5100561Z     echo "Couldn't resolve ${PT_DOMAIN}, exiting..."
2024-11-01T16:33:27.5101104Z     exit 1
2024-11-01T16:33:27.5101421Z   fi
2024-11-01T16:33:27.5101702Z fi
2024-11-01T16:33:27.5101977Z 
2024-11-01T16:33:27.5102327Z if grep -r "${PT_DOMAIN}" /etc/hosts; then
2024-11-01T16:33:27.5102868Z   # Clean up any old records first
2024-11-01T16:33:27.5103398Z   sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts
2024-11-01T16:33:27.5103873Z fi
2024-11-01T16:33:27.5104150Z 
2024-11-01T16:33:27.5104588Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts
2024-11-01T16:33:27.5105169Z cat /etc/hosts
2024-11-01T16:33:27.5112615Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:27.5113150Z env:
2024-11-01T16:33:27.5113435Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:27.5113930Z ##[endgroup]
2024-11-01T16:33:27.5140757Z + PT_DOMAIN=download.pytorch.org
2024-11-01T16:33:27.5147398Z ++ dig -4 +short download.pytorch.org
2024-11-01T16:33:27.5147904Z ++ tail -n1
2024-11-01T16:33:27.5428943Z + RESOLVED_IP=18.160.10.22
2024-11-01T16:33:27.5429604Z + '[' -z 18.160.10.22 ']'
2024-11-01T16:33:27.5430069Z + grep -r download.pytorch.org /etc/hosts
2024-11-01T16:33:27.5445577Z + echo '18.160.10.22 download.pytorch.org'
2024-11-01T16:33:27.5446148Z + sudo tee -a /etc/hosts
2024-11-01T16:33:27.8422437Z 18.160.10.22 download.pytorch.org
2024-11-01T16:33:27.8440976Z + cat /etc/hosts
2024-11-01T16:33:27.8451385Z 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
2024-11-01T16:33:27.8464897Z ::1         localhost6 localhost6.localdomain6
2024-11-01T16:33:27.8465526Z 18.160.10.22 download.pytorch.org
2024-11-01T16:33:27.8679558Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main
2024-11-01T16:33:27.8680228Z with:
2024-11-01T16:33:27.8681209Z   docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8682357Z   docker-build-dir: .ci/docker
2024-11-01T16:33:27.8682747Z   working-directory: .
2024-11-01T16:33:27.8683225Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:27.8683788Z   force-push: false
2024-11-01T16:33:27.8684090Z env:
2024-11-01T16:33:27.8684389Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:27.8684743Z ##[endgroup]
2024-11-01T16:33:27.8710496Z ##[group]Run set -ex
2024-11-01T16:33:27.8710954Z set -ex
2024-11-01T16:33:27.8711340Z 
2024-11-01T16:33:27.8711926Z # If the docker build directory or the build script doesn't exist, the action will
2024-11-01T16:33:27.8713067Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2024-11-01T16:33:27.8714040Z # job could then download the pre-built image as usual
2024-11-01T16:33:27.8714821Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then
2024-11-01T16:33:27.8715716Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8716395Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8716992Z 
2024-11-01T16:33:27.8717530Z   echo "There is no Docker build script in ${REPO_NAME} repo, skipping..."
2024-11-01T16:33:27.8718200Z   exit 0
2024-11-01T16:33:27.8718491Z else
2024-11-01T16:33:27.8718865Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8719333Z fi
2024-11-01T16:33:27.8719609Z 
2024-11-01T16:33:27.8720090Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2024-11-01T16:33:27.8748256Z   # The docker image name already includes the ECR prefix and tag, so we can just
2024-11-01T16:33:27.8749128Z   # use it as it is, but first let's extract the tag
2024-11-01T16:33:27.8749874Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2024-11-01T16:33:27.8750666Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8751414Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8752013Z else
2024-11-01T16:33:27.8752465Z   DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2024-11-01T16:33:27.8753171Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8754288Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8755086Z fi
2024-11-01T16:33:27.8765157Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:27.8765674Z env:
2024-11-01T16:33:27.8765959Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:27.8766324Z   REPO_NAME: pytorch
2024-11-01T16:33:27.8767338Z   DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8768453Z   DOCKER_BUILD_DIR: .ci/docker
2024-11-01T16:33:27.8768981Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:27.8769513Z ##[endgroup]
2024-11-01T16:33:27.8800053Z + [[ ! -d .ci/docker ]]
2024-11-01T16:33:27.8800640Z + [[ ! -f .ci/docker/build.sh ]]
2024-11-01T16:33:27.8801183Z + echo skip=false
2024-11-01T16:33:27.8803165Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941 == *\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* ]]
2024-11-01T16:33:27.8809258Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8810405Z ++ awk -F '[:,]' '{print $2}'
2024-11-01T16:33:27.8836962Z + DOCKER_TAG=bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8837854Z + echo docker-tag=bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8840127Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8874327Z ##[group]Run set +e
2024-11-01T16:33:27.8874715Z set +e
2024-11-01T16:33:27.8875023Z set -x
2024-11-01T16:33:27.8875313Z 
2024-11-01T16:33:27.8875590Z login() {
2024-11-01T16:33:27.8876298Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2024-11-01T16:33:27.8877052Z }
2024-11-01T16:33:27.8877327Z 
2024-11-01T16:33:27.8877601Z retry () {
2024-11-01T16:33:27.8878008Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2024-11-01T16:33:27.8878475Z }
2024-11-01T16:33:27.8878743Z 
2024-11-01T16:33:27.8879050Z retry login "${DOCKER_REGISTRY}"
2024-11-01T16:33:27.8879484Z 
2024-11-01T16:33:27.8879977Z # Check if image already exists, if it does then skip building it
2024-11-01T16:33:27.8880930Z if docker manifest inspect "${DOCKER_IMAGE}"; then
2024-11-01T16:33:27.8881464Z   exit 0
2024-11-01T16:33:27.8881758Z fi
2024-11-01T16:33:27.8882039Z 
2024-11-01T16:33:27.8882570Z # NB: This part requires a full checkout. Otherwise, the merge base will
2024-11-01T16:33:27.8883474Z # be empty.  The default action would be to continue rebuild the image
2024-11-01T16:33:27.8884262Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then
2024-11-01T16:33:27.8884982Z   # if we're on the base branch then use the parent commit
2024-11-01T16:33:27.8885600Z   MERGE_BASE=$(git rev-parse HEAD~)
2024-11-01T16:33:27.8886057Z else
2024-11-01T16:33:27.8886547Z   # otherwise we're on a PR, so use the most recent base commit
2024-11-01T16:33:27.8887284Z   MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION")
2024-11-01T16:33:27.8887823Z fi
2024-11-01T16:33:27.8888102Z 
2024-11-01T16:33:27.8888409Z if [[ -z "${MERGE_BASE}" ]]; then
2024-11-01T16:33:27.8888937Z   echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8889415Z 
2024-11-01T16:33:27.8890105Z   echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..."
2024-11-01T16:33:27.8890924Z   exit 0
2024-11-01T16:33:27.8891225Z fi
2024-11-01T16:33:27.8891482Z 
2024-11-01T16:33:27.8891928Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then
2024-11-01T16:33:27.8892978Z   echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit"
2024-11-01T16:33:27.8893843Z   exit 1
2024-11-01T16:33:27.8894144Z fi
2024-11-01T16:33:27.8894404Z 
2024-11-01T16:33:27.8894911Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}")
2024-11-01T16:33:27.8895943Z # If no image exists but the hash is the same as the previous hash then we should error out here
2024-11-01T16:33:27.8896876Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then
2024-11-01T16:33:27.8897920Z   echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch"
2024-11-01T16:33:27.8899109Z   echo "         Will re-build docker image to store in local cache, TTS may be longer"
2024-11-01T16:33:27.8899792Z fi
2024-11-01T16:33:27.8900055Z 
2024-11-01T16:33:27.8900572Z echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2024-11-01T16:33:27.8907907Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:27.8908436Z env:
2024-11-01T16:33:27.8908712Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:27.8909087Z   DOCKER_BUILD_DIR: .ci/docker
2024-11-01T16:33:27.8909565Z   BASE_REVISION: 2055d3c8dc8b93c52eda6b9c07d716448d9e28c9
2024-11-01T16:33:27.8910758Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8911906Z   DOCKER_TAG: bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:27.8912542Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:27.8913083Z ##[endgroup]
2024-11-01T16:33:27.8941566Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:27.8942277Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:27.8944788Z + aws ecr get-login-password --region us-east-1
2024-11-01T16:33:27.8946341Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:28.5593242Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2024-11-01T16:33:28.5594536Z Configure a credential helper to remove this warning. See
2024-11-01T16:33:28.5595732Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2024-11-01T16:33:28.5596312Z 
2024-11-01T16:33:28.5596426Z Login Succeeded
2024-11-01T16:33:28.5606315Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:28.7872382Z {
2024-11-01T16:33:28.7872872Z 	"schemaVersion": 2,
2024-11-01T16:33:28.7873624Z 	"mediaType": "application/vnd.docker.distribution.manifest.v2+json",
2024-11-01T16:33:28.7874723Z 	"config": {
2024-11-01T16:33:28.7875410Z 		"mediaType": "application/vnd.docker.container.image.v1+json",
2024-11-01T16:33:28.7875991Z 		"size": 45573,
2024-11-01T16:33:28.7876857Z 		"digest": "sha256:8ec759243a9a0c0eef32c1eac9ea7bf9cba4aae49fa39ea946910b2e949f2405"
2024-11-01T16:33:28.7878055Z 	},
2024-11-01T16:33:28.7878315Z 	"layers": [
2024-11-01T16:33:28.7878590Z 		{
2024-11-01T16:33:28.7879106Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7880176Z 			"size": 28583948,
2024-11-01T16:33:28.7880798Z 			"digest": "sha256:86e5016c269355b382c9cabab4f6646d56d75914f20d545289970436dae431b1"
2024-11-01T16:33:28.7881454Z 		},
2024-11-01T16:33:28.7881697Z 		{
2024-11-01T16:33:28.7882155Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7882744Z 			"size": 1823,
2024-11-01T16:33:28.7883326Z 			"digest": "sha256:f48a1ee812be6e26709b12dd12d522faec763e090597867b74271f4ac59123c2"
2024-11-01T16:33:28.7883973Z 		},
2024-11-01T16:33:28.7886719Z + exit 0
2024-11-01T16:33:28.7887570Z 		{
2024-11-01T16:33:28.7889403Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7894465Z 			"size": 313382168,
2024-11-01T16:33:28.7895209Z 			"digest": "sha256:01f11ae3947b14d70d123f98abb9c35268df716c4176938a07a5cfc0f6519ae9"
2024-11-01T16:33:28.7896159Z 		},
2024-11-01T16:33:28.7896595Z 		{
2024-11-01T16:33:28.7897388Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7898511Z 			"size": 863,
2024-11-01T16:33:28.7899418Z 			"digest": "sha256:30e3c9199e557655e3796fa7188b887764b8d7826c3222d0a6dfcfabd992d2f8"
2024-11-01T16:33:28.7900620Z 		},
2024-11-01T16:33:28.7901082Z 		{
2024-11-01T16:33:28.7901915Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7902887Z 			"size": 79404799,
2024-11-01T16:33:28.7903856Z 			"digest": "sha256:52eeee609856e7df2232c174fd80d10f70c0e4403b3e43c97ef6c107957cc11e"
2024-11-01T16:33:28.7904920Z 		},
2024-11-01T16:33:28.7905311Z 		{
2024-11-01T16:33:28.7906064Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7909677Z 			"size": 703,
2024-11-01T16:33:28.7910526Z 			"digest": "sha256:0ea45f9f1d46e003ac0aee9bdd23c0c3db50a4b4d7b88ffd6574a834f7386840"
2024-11-01T16:33:28.7911253Z 		},
2024-11-01T16:33:28.7911519Z 		{
2024-11-01T16:33:28.7911986Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7912622Z 			"size": 1255,
2024-11-01T16:33:28.7913241Z 			"digest": "sha256:9b8709086551bc6cc1eac02559285442f267fedf2dcb9ab238e825891b5b8b49"
2024-11-01T16:33:28.7914039Z 		},
2024-11-01T16:33:28.7914305Z 		{
2024-11-01T16:33:28.7914775Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7915384Z 			"size": 486,
2024-11-01T16:33:28.7915964Z 			"digest": "sha256:d78418420831521826f697a89640fbbf6190b0d23a61e51172b7c25552819a47"
2024-11-01T16:33:28.7916624Z 		},
2024-11-01T16:33:28.7917208Z 		{
2024-11-01T16:33:28.7917673Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7918279Z 			"size": 110,
2024-11-01T16:33:28.7918883Z 			"digest": "sha256:8ed9113f548599251f63e8829aa26bbb5bc62fce12baab4c9176bcf42c6c166f"
2024-11-01T16:33:28.7919562Z 		},
2024-11-01T16:33:28.7919807Z 		{
2024-11-01T16:33:28.7920251Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7920856Z 			"size": 4120,
2024-11-01T16:33:28.7921455Z 			"digest": "sha256:14ebba734113bfc292f18acd93664e81a2b7baaa1e6017d5c2b0cafb055801f0"
2024-11-01T16:33:28.7922334Z 		},
2024-11-01T16:33:28.7922593Z 		{
2024-11-01T16:33:28.7923066Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7923659Z 			"size": 1801,
2024-11-01T16:33:28.7924255Z 			"digest": "sha256:3cf490ac95a791ab489a901d634ff6245683286e431ea4db421ececebdd8eab0"
2024-11-01T16:33:28.7924926Z 		},
2024-11-01T16:33:28.7925182Z 		{
2024-11-01T16:33:28.7925659Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7926244Z 			"size": 702,
2024-11-01T16:33:28.7926826Z 			"digest": "sha256:bd5175f3c54832248bf6f92544afe9e2c6f989282c24ea086a7e6c0dc4049671"
2024-11-01T16:33:28.7927490Z 		},
2024-11-01T16:33:28.7927737Z 		{
2024-11-01T16:33:28.7928193Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7928772Z 			"size": 2639513208,
2024-11-01T16:33:28.7929379Z 			"digest": "sha256:ea78d06f93636efc7ab249fa0d9963c77a849294ab8195292c43b94a1fa240c1"
2024-11-01T16:33:28.7930047Z 		},
2024-11-01T16:33:28.7930292Z 		{
2024-11-01T16:33:28.7930753Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7931330Z 			"size": 32,
2024-11-01T16:33:28.7931908Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.7932579Z 		},
2024-11-01T16:33:28.7932824Z 		{
2024-11-01T16:33:28.7933284Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7933869Z 			"size": 380,
2024-11-01T16:33:28.7934436Z 			"digest": "sha256:15991ce77c64c1e8c60c32bb743d25c37dc1013170cfda29bc2e90e2b8d99ef8"
2024-11-01T16:33:28.7935105Z 		},
2024-11-01T16:33:28.7935351Z 		{
2024-11-01T16:33:28.7935803Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7936393Z 			"size": 104,
2024-11-01T16:33:28.7936961Z 			"digest": "sha256:f3764307c26ba5aaa8380ad1cc69a58a6cf74925a302e754e5898f6b3ccbab8c"
2024-11-01T16:33:28.7937617Z 		},
2024-11-01T16:33:28.7937863Z 		{
2024-11-01T16:33:28.7938317Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7938907Z 			"size": 231,
2024-11-01T16:33:28.7939476Z 			"digest": "sha256:39a462aa048f1e063074cf5761ebef8c7f19636a7b4abe19c8bca9469390cb56"
2024-11-01T16:33:28.7940141Z 		},
2024-11-01T16:33:28.7940389Z 		{
2024-11-01T16:33:28.7940842Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7941431Z 			"size": 3234332,
2024-11-01T16:33:28.7942013Z 			"digest": "sha256:dbdd556fb5b1dfc59180dc1cc211265380286117a00dc0953e61ca9e38301210"
2024-11-01T16:33:28.7942677Z 		},
2024-11-01T16:33:28.7942917Z 		{
2024-11-01T16:33:28.7943527Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7944123Z 			"size": 1954,
2024-11-01T16:33:28.7944700Z 			"digest": "sha256:237a8e022b8f60ad36555b6284b4143c44c318ddd70a9d28cd6d292755f659ed"
2024-11-01T16:33:28.7945341Z 		},
2024-11-01T16:33:28.7945590Z 		{
2024-11-01T16:33:28.7946043Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7946643Z 			"size": 105,
2024-11-01T16:33:28.7947219Z 			"digest": "sha256:4d96209047f1f2d24d3df20eabbd2d733838c37b3544d2e2328e3c0f30bdf0a3"
2024-11-01T16:33:28.7947863Z 		},
2024-11-01T16:33:28.7948105Z 		{
2024-11-01T16:33:28.7948553Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7949143Z 			"size": 565,
2024-11-01T16:33:28.7949703Z 			"digest": "sha256:4420e33558d942976560de16418150d87efc6be945e63d860aeda66b15a02df3"
2024-11-01T16:33:28.7950328Z 		},
2024-11-01T16:33:28.7950572Z 		{
2024-11-01T16:33:28.7951027Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7951616Z 			"size": 32,
2024-11-01T16:33:28.7952191Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.7952855Z 		},
2024-11-01T16:33:28.7953086Z 		{
2024-11-01T16:33:28.7953690Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7954428Z 			"size": 104,
2024-11-01T16:33:28.7955021Z 			"digest": "sha256:c37cac30f6eba63e452bfd10d3e9c08c7209acb36466ac9b10798cbee414df86"
2024-11-01T16:33:28.7955742Z 		},
2024-11-01T16:33:28.7956030Z 		{
2024-11-01T16:33:28.7956482Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7957085Z 			"size": 504,
2024-11-01T16:33:28.7957649Z 			"digest": "sha256:c948118bdc76122529c04a2f98ddc1494f290a8c11903c1e3b684b973b1e5be6"
2024-11-01T16:33:28.7958325Z 		},
2024-11-01T16:33:28.7958575Z 		{
2024-11-01T16:33:28.7959030Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7959625Z 			"size": 121478527,
2024-11-01T16:33:28.7960213Z 			"digest": "sha256:74aa3fc4723a6ab03be78fc61e4dc613297fd85f663135a39c041ebda2fdfcbf"
2024-11-01T16:33:28.7960885Z 		},
2024-11-01T16:33:28.7961133Z 		{
2024-11-01T16:33:28.7961587Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7962186Z 			"size": 109,
2024-11-01T16:33:28.7962765Z 			"digest": "sha256:3a6f65356b895cc8b2ab5e9acc8efd8ec7b94e07f7edbd4f96eecaa902e864dd"
2024-11-01T16:33:28.7963454Z 		},
2024-11-01T16:33:28.7963701Z 		{
2024-11-01T16:33:28.7964158Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7964747Z 			"size": 490,
2024-11-01T16:33:28.7965306Z 			"digest": "sha256:6e9d54a5f6176215a67fb101e24e472d02f68c106ddcec4b1a6baf6c51ca72e1"
2024-11-01T16:33:28.7965966Z 		},
2024-11-01T16:33:28.7966207Z 		{
2024-11-01T16:33:28.7966662Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7967254Z 			"size": 383,
2024-11-01T16:33:28.7967792Z 			"digest": "sha256:041693a89b0584605ba0b2510db7774893670372dd420ce025dbaf15e9d4783b"
2024-11-01T16:33:28.7968428Z 		},
2024-11-01T16:33:28.7968671Z 		{
2024-11-01T16:33:28.7969124Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7969716Z 			"size": 103,
2024-11-01T16:33:28.7970270Z 			"digest": "sha256:868221d23f2901e05f854e27b347c2e09792a00dfc53205c00e2216c84b33aed"
2024-11-01T16:33:28.7970900Z 		},
2024-11-01T16:33:28.7971144Z 		{
2024-11-01T16:33:28.7971595Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7972190Z 			"size": 1474,
2024-11-01T16:33:28.7972770Z 			"digest": "sha256:6be03ece1c540e36048bfe200b12ab2305a10d6da9cd0161f9b2c28043b3dfca"
2024-11-01T16:33:28.7973433Z 		},
2024-11-01T16:33:28.7973709Z 		{
2024-11-01T16:33:28.7974163Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7974754Z 			"size": 426795986,
2024-11-01T16:33:28.7975539Z 			"digest": "sha256:763709bffc6df0ee4de2c34c490dbcec0250fd79a482a2aa680e34b19b39ab83"
2024-11-01T16:33:28.7976209Z 		},
2024-11-01T16:33:28.7976459Z 		{
2024-11-01T16:33:28.7976911Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7977498Z 			"size": 163,
2024-11-01T16:33:28.7978059Z 			"digest": "sha256:1034221b0067414e0982a426a7f901f335ce659c1bba8742dab67ab2a75e187c"
2024-11-01T16:33:28.7978694Z 		},
2024-11-01T16:33:28.7978935Z 		{
2024-11-01T16:33:28.7979384Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7979971Z 			"size": 2211,
2024-11-01T16:33:28.7980530Z 			"digest": "sha256:7e09006426536eac9d4cc056a3e772a62060d10909ec00ef10f0590280b58678"
2024-11-01T16:33:28.7981173Z 		},
2024-11-01T16:33:28.7981403Z 		{
2024-11-01T16:33:28.7981852Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7982440Z 			"size": 152,
2024-11-01T16:33:28.7983044Z 			"digest": "sha256:bedd70a265dfbd943c3ab2168fbf630b3b2fb98fdcca808fd0a9c4c47adfacfa"
2024-11-01T16:33:28.7983769Z 		},
2024-11-01T16:33:28.7983999Z 		{
2024-11-01T16:33:28.7984452Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7985037Z 			"size": 647,
2024-11-01T16:33:28.7985602Z 			"digest": "sha256:d871b68b8c5ef7c24851d6c6c46230296bec440e34014435d8d9febf4c9c2643"
2024-11-01T16:33:28.7986353Z 		},
2024-11-01T16:33:28.7986583Z 		{
2024-11-01T16:33:28.7987031Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7987619Z 			"size": 32,
2024-11-01T16:33:28.7988195Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.7988858Z 		},
2024-11-01T16:33:28.7989111Z 		{
2024-11-01T16:33:28.7989563Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7990145Z 			"size": 166,
2024-11-01T16:33:28.7990711Z 			"digest": "sha256:041c182362652a29bb2409a859cdfef6169cd0bc2beeb21d31d39665d5b76dc4"
2024-11-01T16:33:28.7991371Z 		},
2024-11-01T16:33:28.7991616Z 		{
2024-11-01T16:33:28.7992052Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7992682Z 			"size": 109,
2024-11-01T16:33:28.7993251Z 			"digest": "sha256:060225a95076cb4a41cbba4998bc0155bc7550c1d91ee14353e33d24732a338f"
2024-11-01T16:33:28.7994057Z 		},
2024-11-01T16:33:28.7994300Z 		{
2024-11-01T16:33:28.7994738Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7995326Z 			"size": 102,
2024-11-01T16:33:28.7995896Z 			"digest": "sha256:056d1f4e2b943281d7223c7ab3c309ed955f6d8abc24eaa402c9398c004608c8"
2024-11-01T16:33:28.7996557Z 		},
2024-11-01T16:33:28.7996799Z 		{
2024-11-01T16:33:28.7997237Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.7997822Z 			"size": 422,
2024-11-01T16:33:28.7998398Z 			"digest": "sha256:4b5067a4de91512b8a77d0d66c35222c9f6dfb1b1c803b0a97cadf0076c183fa"
2024-11-01T16:33:28.7999059Z 		},
2024-11-01T16:33:28.7999305Z 		{
2024-11-01T16:33:28.7999742Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8000332Z 			"size": 32,
2024-11-01T16:33:28.8000911Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8001574Z 		},
2024-11-01T16:33:28.8001821Z 		{
2024-11-01T16:33:28.8002261Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8002852Z 			"size": 111,
2024-11-01T16:33:28.8003434Z 			"digest": "sha256:fb6f677247c1e7a0f8f5c1031f0818ab3eb8eedccc77ddfc70708fd38e6a69ad"
2024-11-01T16:33:28.8004109Z 		},
2024-11-01T16:33:28.8004357Z 		{
2024-11-01T16:33:28.8004808Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8005380Z 			"size": 473,
2024-11-01T16:33:28.8005940Z 			"digest": "sha256:709e972708a9c43a71332da84b3099bd3d31347e186fd062a6e8c7aa11c066ee"
2024-11-01T16:33:28.8006918Z 		},
2024-11-01T16:33:28.8007193Z 		{
2024-11-01T16:33:28.8007918Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8008502Z 			"size": 32,
2024-11-01T16:33:28.8009081Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8009784Z 		},
2024-11-01T16:33:28.8010030Z 		{
2024-11-01T16:33:28.8010489Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8011066Z 			"size": 112,
2024-11-01T16:33:28.8011652Z 			"digest": "sha256:ff7efa4d0d03b241858cb688c09f5b6de50bf4922f8ea5d505dbea0ed782e472"
2024-11-01T16:33:28.8012325Z 		},
2024-11-01T16:33:28.8012565Z 		{
2024-11-01T16:33:28.8013016Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8013590Z 			"size": 564,
2024-11-01T16:33:28.8014161Z 			"digest": "sha256:4f7a6785dd367ca4dae8fc8897aaa1507533378293cc6149d33062b12b619b9b"
2024-11-01T16:33:28.8014816Z 		},
2024-11-01T16:33:28.8015058Z 		{
2024-11-01T16:33:28.8015515Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8016105Z 			"size": 43148219,
2024-11-01T16:33:28.8016702Z 			"digest": "sha256:dc01a5d84fa184b84fd69aef4c861f3a613cbdff3d14394ebbb3a100a38a47e2"
2024-11-01T16:33:28.8017378Z 		},
2024-11-01T16:33:28.8017622Z 		{
2024-11-01T16:33:28.8018076Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8018762Z 			"size": 106,
2024-11-01T16:33:28.8019322Z 			"digest": "sha256:6911517f714baf8f91a0a64405efa17d77bbb4f2a2602ad5899edd5db6d8d520"
2024-11-01T16:33:28.8019973Z 		},
2024-11-01T16:33:28.8020216Z 		{
2024-11-01T16:33:28.8020667Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8021253Z 			"size": 295,
2024-11-01T16:33:28.8021824Z 			"digest": "sha256:c4b256a3e0e02bbeeab0ea33767925d4d9dcdc67be4043f64b08a1c94119f26c"
2024-11-01T16:33:28.8022501Z 		},
2024-11-01T16:33:28.8022743Z 		{
2024-11-01T16:33:28.8023194Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8023781Z 			"size": 32,
2024-11-01T16:33:28.8024344Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8025013Z 		},
2024-11-01T16:33:28.8025253Z 		{
2024-11-01T16:33:28.8025704Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8026293Z 			"size": 106,
2024-11-01T16:33:28.8026864Z 			"digest": "sha256:59c0aea7054a3d978ea3b461b9b3730395010efe92d2a970c784400631db6693"
2024-11-01T16:33:28.8027504Z 		},
2024-11-01T16:33:28.8027743Z 		{
2024-11-01T16:33:28.8028193Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8028777Z 			"size": 425,
2024-11-01T16:33:28.8029340Z 			"digest": "sha256:3432201c49088e0dcbc99e9d1e177b20e3a6968e892e20048b1220ba2a150b0b"
2024-11-01T16:33:28.8029978Z 		},
2024-11-01T16:33:28.8030221Z 		{
2024-11-01T16:33:28.8030676Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8031266Z 			"size": 20262161,
2024-11-01T16:33:28.8031849Z 			"digest": "sha256:78581735d693c4dc8fd044293c328616d14519a9ed6fb0285d9ae11448180619"
2024-11-01T16:33:28.8032481Z 		},
2024-11-01T16:33:28.8032722Z 		{
2024-11-01T16:33:28.8033174Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8033760Z 			"size": 108,
2024-11-01T16:33:28.8034482Z 			"digest": "sha256:f2abdd58f890ff7f163ab89f90b9360b86a93666b5dd0258cb6be351572a61e0"
2024-11-01T16:33:28.8035137Z 		},
2024-11-01T16:33:28.8035382Z 		{
2024-11-01T16:33:28.8035835Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8036431Z 			"size": 438,
2024-11-01T16:33:28.8037018Z 			"digest": "sha256:290b43f33e974a2b28b5b5ea8edded2f478a2a6e13e972f16bbc3daf230f0d2a"
2024-11-01T16:33:28.8037687Z 		},
2024-11-01T16:33:28.8037919Z 		{
2024-11-01T16:33:28.8038368Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8038954Z 			"size": 702,
2024-11-01T16:33:28.8039629Z 			"digest": "sha256:bd5175f3c54832248bf6f92544afe9e2c6f989282c24ea086a7e6c0dc4049671"
2024-11-01T16:33:28.8040281Z 		},
2024-11-01T16:33:28.8040511Z 		{
2024-11-01T16:33:28.8040961Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8041550Z 			"size": 142,
2024-11-01T16:33:28.8042126Z 			"digest": "sha256:47baf2ba278685f7fcfd34a488005aa1627cce88de60412514ffd5e2133f3cba"
2024-11-01T16:33:28.8042792Z 		},
2024-11-01T16:33:28.8043021Z 		{
2024-11-01T16:33:28.8043472Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8044059Z 			"size": 135,
2024-11-01T16:33:28.8044618Z 			"digest": "sha256:de6286175e72589d8470a2b2e50d953cf1f350d3e5bc1d367a1a6f4642374505"
2024-11-01T16:33:28.8045264Z 		},
2024-11-01T16:33:28.8045490Z 		{
2024-11-01T16:33:28.8045937Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8046520Z 			"size": 32,
2024-11-01T16:33:28.8047095Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8047759Z 		},
2024-11-01T16:33:28.8048003Z 		{
2024-11-01T16:33:28.8048441Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8049025Z 			"size": 196,
2024-11-01T16:33:28.8049585Z 			"digest": "sha256:96b2aab8e70e30573094f6324190628f6562a0431243fcebb7825c11572f0505"
2024-11-01T16:33:28.8050307Z 		},
2024-11-01T16:33:28.8050552Z 		{
2024-11-01T16:33:28.8050990Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8066868Z 			"size": 1402,
2024-11-01T16:33:28.8067691Z 			"digest": "sha256:0aa056faa16002c9abb63a58b5dba6e9072297048ae38525a7ad7cafe7829aa7"
2024-11-01T16:33:28.8068385Z 		},
2024-11-01T16:33:28.8068637Z 		{
2024-11-01T16:33:28.8069088Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8069691Z 			"size": 702,
2024-11-01T16:33:28.8070268Z 			"digest": "sha256:bd5175f3c54832248bf6f92544afe9e2c6f989282c24ea086a7e6c0dc4049671"
2024-11-01T16:33:28.8070933Z 		},
2024-11-01T16:33:28.8071198Z 		{
2024-11-01T16:33:28.8071658Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8072238Z 			"size": 136,
2024-11-01T16:33:28.8072822Z 			"digest": "sha256:90b4cbecc01ee995c28811f99381e13c134a2cdf7c6eaed890aec27559c8c194"
2024-11-01T16:33:28.8073512Z 		},
2024-11-01T16:33:28.8073754Z 		{
2024-11-01T16:33:28.8074380Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8074964Z 			"size": 32,
2024-11-01T16:33:28.8075556Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8076233Z 		},
2024-11-01T16:33:28.8076484Z 		{
2024-11-01T16:33:28.8076940Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8077516Z 			"size": 155,
2024-11-01T16:33:28.8078082Z 			"digest": "sha256:971049fe7206371ffbf4829118f629fa4bf480c671b73ee2083e01ecc8ae7395"
2024-11-01T16:33:28.8078726Z 		},
2024-11-01T16:33:28.8078975Z 		{
2024-11-01T16:33:28.8079437Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8080013Z 			"size": 1402,
2024-11-01T16:33:28.8080604Z 			"digest": "sha256:0aa056faa16002c9abb63a58b5dba6e9072297048ae38525a7ad7cafe7829aa7"
2024-11-01T16:33:28.8081275Z 		},
2024-11-01T16:33:28.8081527Z 		{
2024-11-01T16:33:28.8081998Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8082588Z 			"size": 702,
2024-11-01T16:33:28.8083145Z 			"digest": "sha256:bd5175f3c54832248bf6f92544afe9e2c6f989282c24ea086a7e6c0dc4049671"
2024-11-01T16:33:28.8083804Z 		},
2024-11-01T16:33:28.8084050Z 		{
2024-11-01T16:33:28.8084499Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8085094Z 			"size": 140,
2024-11-01T16:33:28.8085679Z 			"digest": "sha256:dcb3cbd15248b9fce16dc66c5a5c16c0ad348dcc0f895a04d89f43f5985453ce"
2024-11-01T16:33:28.8086355Z 		},
2024-11-01T16:33:28.8086596Z 		{
2024-11-01T16:33:28.8087378Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8087976Z 			"size": 32,
2024-11-01T16:33:28.8088552Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8089233Z 		},
2024-11-01T16:33:28.8089480Z 		{
2024-11-01T16:33:28.8089939Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8090535Z 			"size": 160,
2024-11-01T16:33:28.8091093Z 			"digest": "sha256:b359f25c8f3fb1293997235b1de1a5ce3df220daf2726313df265647c53f6bc5"
2024-11-01T16:33:28.8091759Z 		},
2024-11-01T16:33:28.8092004Z 		{
2024-11-01T16:33:28.8092456Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8093041Z 			"size": 849,
2024-11-01T16:33:28.8093614Z 			"digest": "sha256:e93f3a4e9cee1a3f01051a94ad11c3124874a7047f11f760e6181fd13b836d2b"
2024-11-01T16:33:28.8094260Z 		},
2024-11-01T16:33:28.8094504Z 		{
2024-11-01T16:33:28.8094960Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8095555Z 			"size": 702,
2024-11-01T16:33:28.8096124Z 			"digest": "sha256:bd5175f3c54832248bf6f92544afe9e2c6f989282c24ea086a7e6c0dc4049671"
2024-11-01T16:33:28.8096768Z 		},
2024-11-01T16:33:28.8097010Z 		{
2024-11-01T16:33:28.8097462Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8098159Z 			"size": 141,
2024-11-01T16:33:28.8098749Z 			"digest": "sha256:dedcc6ca0f5876569476329d3fd665b1ad9697e1de71f6ce2643c20cf2a307d3"
2024-11-01T16:33:28.8099408Z 		},
2024-11-01T16:33:28.8099655Z 		{
2024-11-01T16:33:28.8100107Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8100698Z 			"size": 32,
2024-11-01T16:33:28.8101274Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8101922Z 		},
2024-11-01T16:33:28.8102168Z 		{
2024-11-01T16:33:28.8102619Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8103206Z 			"size": 160,
2024-11-01T16:33:28.8103782Z 			"digest": "sha256:9d53c2778a8e99500bbbe01795aa3c8cd130f9afde3964458a4142694fa190ab"
2024-11-01T16:33:28.8104436Z 		},
2024-11-01T16:33:28.8104714Z 		{
2024-11-01T16:33:28.8105168Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8105757Z 			"size": 907,
2024-11-01T16:33:28.8106331Z 			"digest": "sha256:6573da121594129c30e5b1d84c470321d3d274bfd03cdf7df28b42bec4639bc3"
2024-11-01T16:33:28.8107286Z 		},
2024-11-01T16:33:28.8107525Z 		{
2024-11-01T16:33:28.8107990Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8108584Z 			"size": 702,
2024-11-01T16:33:28.8109156Z 			"digest": "sha256:bd5175f3c54832248bf6f92544afe9e2c6f989282c24ea086a7e6c0dc4049671"
2024-11-01T16:33:28.8109815Z 		},
2024-11-01T16:33:28.8110043Z 		{
2024-11-01T16:33:28.8110498Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8111084Z 			"size": 135,
2024-11-01T16:33:28.8111659Z 			"digest": "sha256:3c664e41a6c431b0dada062d556441825a5b31d8d3c8617ef508f52d253bb1d7"
2024-11-01T16:33:28.8112314Z 		},
2024-11-01T16:33:28.8112543Z 		{
2024-11-01T16:33:28.8112997Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8113585Z 			"size": 32,
2024-11-01T16:33:28.8114287Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8114971Z 		},
2024-11-01T16:33:28.8115219Z 		{
2024-11-01T16:33:28.8115664Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8116261Z 			"size": 158,
2024-11-01T16:33:28.8116816Z 			"digest": "sha256:97b765e7f18814111c54f92ec783b73457484b3fa05e49879af706b061240eb5"
2024-11-01T16:33:28.8117454Z 		},
2024-11-01T16:33:28.8117699Z 		{
2024-11-01T16:33:28.8118139Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8118728Z 			"size": 1520,
2024-11-01T16:33:28.8119595Z 			"digest": "sha256:11539d5ffd1eb4f1304acae722ffaf3387ce58108fc7902d3e97de16ad21fce3"
2024-11-01T16:33:28.8120275Z 		},
2024-11-01T16:33:28.8120520Z 		{
2024-11-01T16:33:28.8120962Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8121553Z 			"size": 32,
2024-11-01T16:33:28.8122136Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8122809Z 		},
2024-11-01T16:33:28.8123055Z 		{
2024-11-01T16:33:28.8123492Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8124085Z 			"size": 135,
2024-11-01T16:33:28.8124663Z 			"digest": "sha256:69a2b353ecfe4ee971909e1597ffcca336de0d00006ef64f0fc9861cb56c9678"
2024-11-01T16:33:28.8125331Z 		},
2024-11-01T16:33:28.8125576Z 		{
2024-11-01T16:33:28.8126029Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8126604Z 			"size": 379,
2024-11-01T16:33:28.8127167Z 			"digest": "sha256:3e046c373bdd33358a884a262f4d7368748d0565bf9a44abcc82f973c6c1a0ea"
2024-11-01T16:33:28.8127823Z 		},
2024-11-01T16:33:28.8128074Z 		{
2024-11-01T16:33:28.8128529Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8129105Z 			"size": 32,
2024-11-01T16:33:28.8129683Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8130455Z 		},
2024-11-01T16:33:28.8130705Z 		{
2024-11-01T16:33:28.8131159Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8131735Z 			"size": 104,
2024-11-01T16:33:28.8132323Z 			"digest": "sha256:6c3de4adbcbab77951bbf32cc1a03118500ee9cf829aaf3e675bf458d0af43a2"
2024-11-01T16:33:28.8132998Z 		},
2024-11-01T16:33:28.8133243Z 		{
2024-11-01T16:33:28.8133695Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8134268Z 			"size": 2047,
2024-11-01T16:33:28.8134866Z 			"digest": "sha256:4bf8c82a45cda076d9e9500b16d31a22d0fb34a4aca41e795d6d89aea094f570"
2024-11-01T16:33:28.8135533Z 		},
2024-11-01T16:33:28.8135780Z 		{
2024-11-01T16:33:28.8136236Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8136826Z 			"size": 233879500,
2024-11-01T16:33:28.8137419Z 			"digest": "sha256:5dc7d1f58a07ea52b0df72261ec83c5732dfd6e3a33dedd55d2b66366ad18194"
2024-11-01T16:33:28.8138098Z 		},
2024-11-01T16:33:28.8138343Z 		{
2024-11-01T16:33:28.8138795Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8139383Z 			"size": 106,
2024-11-01T16:33:28.8139942Z 			"digest": "sha256:df02151305efc1ca8279b387260ee6c9171c04c4c3f0a35ebcb6b397f1512ab1"
2024-11-01T16:33:28.8140603Z 		},
2024-11-01T16:33:28.8140844Z 		{
2024-11-01T16:33:28.8141294Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8141881Z 			"size": 164,
2024-11-01T16:33:28.8142472Z 			"digest": "sha256:d70ba44bc62a4bfb716de12ff6cffea2026a3cfc1e9064ce0f6ebcd81a4412ed"
2024-11-01T16:33:28.8143157Z 		},
2024-11-01T16:33:28.8143401Z 		{
2024-11-01T16:33:28.8143854Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8144440Z 			"size": 7944,
2024-11-01T16:33:28.8145007Z 			"digest": "sha256:29f7349de058c05c4397f7cecca88cafc82ac6b66f41c421e010e0e0887832cb"
2024-11-01T16:33:28.8145673Z 		},
2024-11-01T16:33:28.8145917Z 		{
2024-11-01T16:33:28.8146365Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8146961Z 			"size": 8069,
2024-11-01T16:33:28.8147535Z 			"digest": "sha256:11f2f216ce74a7e53e042845cc712fb4aa9a0034039d66eef0d58916377b8e6e"
2024-11-01T16:33:28.8148175Z 		},
2024-11-01T16:33:28.8148418Z 		{
2024-11-01T16:33:28.8148868Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8149456Z 			"size": 304,
2024-11-01T16:33:28.8150044Z 			"digest": "sha256:cf2facbc7537bdf322d67d00a94126a666a22d20f24d1956edf93decf75a8d09"
2024-11-01T16:33:28.8150702Z 		},
2024-11-01T16:33:28.8150948Z 		{
2024-11-01T16:33:28.8151399Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8152103Z 			"size": 32,
2024-11-01T16:33:28.8152689Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8153342Z 		},
2024-11-01T16:33:28.8153591Z 		{
2024-11-01T16:33:28.8154191Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8154803Z 			"size": 108,
2024-11-01T16:33:28.8155400Z 			"digest": "sha256:c3946a485fdfcae7bcbfbb37ef5ff9fc3ac7c000473010ab3531d09e95c87e4e"
2024-11-01T16:33:28.8156066Z 		},
2024-11-01T16:33:28.8156310Z 		{
2024-11-01T16:33:28.8156761Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8157351Z 			"size": 54145652,
2024-11-01T16:33:28.8157948Z 			"digest": "sha256:05914e664c50c0574bfb7fed2e1e4a07a676c1be68b7d0cbeede54ffd697f3cd"
2024-11-01T16:33:28.8158613Z 		},
2024-11-01T16:33:28.8158846Z 		{
2024-11-01T16:33:28.8159299Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2024-11-01T16:33:28.8159883Z 			"size": 32,
2024-11-01T16:33:28.8160459Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2024-11-01T16:33:28.8161131Z 		}
2024-11-01T16:33:28.8161360Z 	]
2024-11-01T16:33:28.8161602Z }
2024-11-01T16:33:28.8296791Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/}
2024-11-01T16:33:28.8297465Z tag=${ECR_DOCKER_IMAGE##*/}
2024-11-01T16:33:28.8298044Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
2024-11-01T16:33:28.8304630Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:28.8305151Z env:
2024-11-01T16:33:28.8305434Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:28.8306476Z   ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:28.8307880Z ##[endgroup]
2024-11-01T16:33:28.8336218Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.12-clang10-bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:28.8409919Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2024-11-01T16:33:28.8410527Z with:
2024-11-01T16:33:28.8411488Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:28.8412721Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:28.8413279Z env:
2024-11-01T16:33:28.8413563Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:28.8413923Z ##[endgroup]
2024-11-01T16:33:28.8435476Z ##[group]Run set -x
2024-11-01T16:33:28.8435856Z set -x
2024-11-01T16:33:28.8436163Z set +e
2024-11-01T16:33:28.8436462Z 
2024-11-01T16:33:28.8436744Z login() {
2024-11-01T16:33:28.8437446Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2024-11-01T16:33:28.8438216Z }
2024-11-01T16:33:28.8438476Z 
2024-11-01T16:33:28.8438810Z retry () {
2024-11-01T16:33:28.8439205Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2024-11-01T16:33:28.8439656Z }
2024-11-01T16:33:28.8439930Z 
2024-11-01T16:33:28.8440249Z retry login "${DOCKER_REGISTRY}"
2024-11-01T16:33:28.8440690Z 
2024-11-01T16:33:28.8440965Z set -e
2024-11-01T16:33:28.8441460Z # ignore output since only exit code is used for conditional
2024-11-01T16:33:28.8442206Z # only pull docker image if it's not available locally
2024-11-01T16:33:28.8443080Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2024-11-01T16:33:28.8443826Z   retry docker pull "${DOCKER_IMAGE}"
2024-11-01T16:33:28.8444291Z fi
2024-11-01T16:33:28.8450710Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:33:28.8451261Z env:
2024-11-01T16:33:28.8451533Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:33:28.8452575Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:28.8453795Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:28.8454356Z ##[endgroup]
2024-11-01T16:33:28.8480681Z + set +e
2024-11-01T16:33:28.8481403Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:28.8482113Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:28.8485059Z + aws ecr get-login-password --region us-east-1
2024-11-01T16:33:28.8486033Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2024-11-01T16:33:29.4873373Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2024-11-01T16:33:29.4874482Z Configure a credential helper to remove this warning. See
2024-11-01T16:33:29.4875508Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2024-11-01T16:33:29.4876083Z 
2024-11-01T16:33:29.4876216Z Login Succeeded
2024-11-01T16:33:29.4887784Z + set -e
2024-11-01T16:33:29.4889744Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:29.5032904Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:29.5035309Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:33:29.7304011Z bdd298b12da59246147f016e0693ffd722419941: Pulling from pytorch/pytorch-linux-focal-py3.12-clang10
2024-11-01T16:33:29.7305435Z 86e5016c2693: Pulling fs layer
2024-11-01T16:33:29.7306134Z f48a1ee812be: Pulling fs layer
2024-11-01T16:33:29.7307061Z 01f11ae3947b: Pulling fs layer
2024-11-01T16:33:29.7307854Z 30e3c9199e55: Pulling fs layer
2024-11-01T16:33:29.7308476Z 52eeee609856: Pulling fs layer
2024-11-01T16:33:29.7309511Z 0ea45f9f1d46: Pulling fs layer
2024-11-01T16:33:29.7310035Z 9b8709086551: Pulling fs layer
2024-11-01T16:33:29.7310473Z d78418420831: Pulling fs layer
2024-11-01T16:33:29.7310962Z 8ed9113f5485: Pulling fs layer
2024-11-01T16:33:29.7311344Z 14ebba734113: Pulling fs layer
2024-11-01T16:33:29.7311813Z 3cf490ac95a7: Pulling fs layer
2024-11-01T16:33:29.7312297Z bd5175f3c548: Pulling fs layer
2024-11-01T16:33:29.7312695Z ea78d06f9363: Pulling fs layer
2024-11-01T16:33:29.7313161Z 4f4fb700ef54: Pulling fs layer
2024-11-01T16:33:29.7313591Z 15991ce77c64: Pulling fs layer
2024-11-01T16:33:29.7314195Z 52eeee609856: Waiting
2024-11-01T16:33:29.7314531Z 0ea45f9f1d46: Waiting
2024-11-01T16:33:29.7314874Z f3764307c26b: Pulling fs layer
2024-11-01T16:33:29.7315245Z 9b8709086551: Waiting
2024-11-01T16:33:29.7315643Z 39a462aa048f: Pulling fs layer
2024-11-01T16:33:29.7316001Z 8ed9113f5485: Waiting
2024-11-01T16:33:29.7316445Z dbdd556fb5b1: Pulling fs layer
2024-11-01T16:33:29.7316948Z 14ebba734113: Waiting
2024-11-01T16:33:29.7317898Z 237a8e022b8f: Pulling fs layer
2024-11-01T16:33:29.7318591Z d78418420831: Waiting
2024-11-01T16:33:29.7319078Z 4d96209047f1: Pulling fs layer
2024-11-01T16:33:29.7319456Z 3cf490ac95a7: Waiting
2024-11-01T16:33:29.7319782Z 15991ce77c64: Waiting
2024-11-01T16:33:29.7320129Z 4420e33558d9: Pulling fs layer
2024-11-01T16:33:29.7320543Z bd5175f3c548: Waiting
2024-11-01T16:33:29.7320852Z 4f4fb700ef54: Waiting
2024-11-01T16:33:29.7321183Z c37cac30f6eb: Pulling fs layer
2024-11-01T16:33:29.7321603Z ea78d06f9363: Waiting
2024-11-01T16:33:29.7321994Z c948118bdc76: Pulling fs layer
2024-11-01T16:33:29.7322385Z 74aa3fc4723a: Pulling fs layer
2024-11-01T16:33:29.7322742Z f3764307c26b: Waiting
2024-11-01T16:33:29.7323075Z 3a6f65356b89: Pulling fs layer
2024-11-01T16:33:29.7323481Z 39a462aa048f: Waiting
2024-11-01T16:33:29.7323816Z 6e9d54a5f617: Pulling fs layer
2024-11-01T16:33:29.7324197Z 041693a89b05: Pulling fs layer
2024-11-01T16:33:29.7324555Z dbdd556fb5b1: Waiting
2024-11-01T16:33:29.7324897Z 868221d23f29: Pulling fs layer
2024-11-01T16:33:29.7325282Z 6be03ece1c54: Pulling fs layer
2024-11-01T16:33:29.7325664Z 763709bffc6d: Pulling fs layer
2024-11-01T16:33:29.7326028Z 30e3c9199e55: Waiting
2024-11-01T16:33:29.7326343Z 1034221b0067: Pulling fs layer
2024-11-01T16:33:29.7326714Z 237a8e022b8f: Waiting
2024-11-01T16:33:29.7327046Z 7e0900642653: Pulling fs layer
2024-11-01T16:33:29.7327578Z bedd70a265df: Pulling fs layer
2024-11-01T16:33:29.7327949Z 4d96209047f1: Waiting
2024-11-01T16:33:29.7328256Z 74aa3fc4723a: Waiting
2024-11-01T16:33:29.7328895Z d871b68b8c5e: Pulling fs layer
2024-11-01T16:33:29.7329344Z 041c18236265: Pulling fs layer
2024-11-01T16:33:29.7329774Z 060225a95076: Pulling fs layer
2024-11-01T16:33:29.7330240Z 056d1f4e2b94: Pulling fs layer
2024-11-01T16:33:29.7330595Z 041693a89b05: Waiting
2024-11-01T16:33:29.7330970Z 4b5067a4de91: Pulling fs layer
2024-11-01T16:33:29.7331339Z 3a6f65356b89: Waiting
2024-11-01T16:33:29.7331655Z 868221d23f29: Waiting
2024-11-01T16:33:29.7331979Z 4420e33558d9: Waiting
2024-11-01T16:33:29.7332282Z 6e9d54a5f617: Waiting
2024-11-01T16:33:29.7332603Z 6be03ece1c54: Waiting
2024-11-01T16:33:29.7332920Z c948118bdc76: Waiting
2024-11-01T16:33:29.7333240Z c37cac30f6eb: Waiting
2024-11-01T16:33:29.7333560Z 763709bffc6d: Waiting
2024-11-01T16:33:29.7333881Z fb6f677247c1: Pulling fs layer
2024-11-01T16:33:29.7334501Z 709e972708a9: Pulling fs layer
2024-11-01T16:33:29.7334876Z bedd70a265df: Waiting
2024-11-01T16:33:29.7335217Z ff7efa4d0d03: Pulling fs layer
2024-11-01T16:33:29.7335587Z 1034221b0067: Waiting
2024-11-01T16:33:29.7335907Z d871b68b8c5e: Waiting
2024-11-01T16:33:29.7336226Z 4f7a6785dd36: Pulling fs layer
2024-11-01T16:33:29.7336594Z 7e0900642653: Waiting
2024-11-01T16:33:29.7336926Z dc01a5d84fa1: Pulling fs layer
2024-11-01T16:33:29.7337369Z 6911517f714b: Pulling fs layer
2024-11-01T16:33:29.7337897Z c4b256a3e0e0: Pulling fs layer
2024-11-01T16:33:29.7338272Z 4b5067a4de91: Waiting
2024-11-01T16:33:29.7338576Z 041c18236265: Waiting
2024-11-01T16:33:29.7339042Z 59c0aea7054a: Pulling fs layer
2024-11-01T16:33:29.7339429Z 3432201c4908: Pulling fs layer
2024-11-01T16:33:29.7339811Z 78581735d693: Pulling fs layer
2024-11-01T16:33:29.7340175Z 060225a95076: Waiting
2024-11-01T16:33:29.7340609Z f2abdd58f890: Pulling fs layer
2024-11-01T16:33:29.7341141Z 056d1f4e2b94: Waiting
2024-11-01T16:33:29.7341474Z fb6f677247c1: Waiting
2024-11-01T16:33:29.7341820Z 290b43f33e97: Pulling fs layer
2024-11-01T16:33:29.7342188Z 709e972708a9: Waiting
2024-11-01T16:33:29.7342496Z ff7efa4d0d03: Waiting
2024-11-01T16:33:29.7342871Z 6911517f714b: Waiting
2024-11-01T16:33:29.7343436Z 47baf2ba2786: Pulling fs layer
2024-11-01T16:33:29.7343831Z de6286175e72: Pulling fs layer
2024-11-01T16:33:29.7344302Z 3432201c4908: Waiting
2024-11-01T16:33:29.7344602Z 78581735d693: Waiting
2024-11-01T16:33:29.7345015Z 96b2aab8e70e: Pulling fs layer
2024-11-01T16:33:29.7345405Z 0aa056faa160: Pulling fs layer
2024-11-01T16:33:29.7345790Z 90b4cbecc01e: Pulling fs layer
2024-11-01T16:33:29.7346161Z 59c0aea7054a: Waiting
2024-11-01T16:33:29.7346475Z c4b256a3e0e0: Waiting
2024-11-01T16:33:29.7346796Z f2abdd58f890: Waiting
2024-11-01T16:33:29.7347127Z 971049fe7206: Pulling fs layer
2024-11-01T16:33:29.7347493Z 4f7a6785dd36: Waiting
2024-11-01T16:33:29.7347830Z dcb3cbd15248: Pulling fs layer
2024-11-01T16:33:29.7348191Z 47baf2ba2786: Waiting
2024-11-01T16:33:29.7348518Z 96b2aab8e70e: Waiting
2024-11-01T16:33:29.7348852Z b359f25c8f3f: Pulling fs layer
2024-11-01T16:33:29.7349226Z 90b4cbecc01e: Waiting
2024-11-01T16:33:29.7349542Z 971049fe7206: Waiting
2024-11-01T16:33:29.7349860Z e93f3a4e9cee: Pulling fs layer
2024-11-01T16:33:29.7350230Z 0aa056faa160: Waiting
2024-11-01T16:33:29.7350591Z dcb3cbd15248: Waiting
2024-11-01T16:33:29.7351143Z dedcc6ca0f58: Pulling fs layer
2024-11-01T16:33:29.7351561Z e93f3a4e9cee: Waiting
2024-11-01T16:33:29.7352024Z b359f25c8f3f: Waiting
2024-11-01T16:33:29.7352423Z 9d53c2778a8e: Pulling fs layer
2024-11-01T16:33:29.7352812Z 6573da121594: Pulling fs layer
2024-11-01T16:33:29.7353207Z 3c664e41a6c4: Pulling fs layer
2024-11-01T16:33:29.7353639Z 9d53c2778a8e: Waiting
2024-11-01T16:33:29.7354060Z 6573da121594: Waiting
2024-11-01T16:33:29.7354363Z 290b43f33e97: Waiting
2024-11-01T16:33:29.7354753Z 97b765e7f188: Pulling fs layer
2024-11-01T16:33:29.7355144Z 11539d5ffd1e: Pulling fs layer
2024-11-01T16:33:29.7355532Z 69a2b353ecfe: Pulling fs layer
2024-11-01T16:33:29.7355909Z 11539d5ffd1e: Waiting
2024-11-01T16:33:29.7356233Z 3e046c373bdd: Pulling fs layer
2024-11-01T16:33:29.7356659Z 97b765e7f188: Waiting
2024-11-01T16:33:29.7356980Z 69a2b353ecfe: Waiting
2024-11-01T16:33:29.7357321Z 6c3de4adbcba: Pulling fs layer
2024-11-01T16:33:29.7357709Z 4bf8c82a45cd: Pulling fs layer
2024-11-01T16:33:29.7358285Z 6c3de4adbcba: Waiting
2024-11-01T16:33:29.7358960Z 5dc7d1f58a07: Pulling fs layer
2024-11-01T16:33:29.7359628Z df02151305ef: Pulling fs layer
2024-11-01T16:33:29.7360250Z 4bf8c82a45cd: Waiting
2024-11-01T16:33:29.7360824Z d70ba44bc62a: Pulling fs layer
2024-11-01T16:33:29.7361220Z 29f7349de058: Pulling fs layer
2024-11-01T16:33:29.7361591Z df02151305ef: Waiting
2024-11-01T16:33:29.7361911Z d70ba44bc62a: Waiting
2024-11-01T16:33:29.7362235Z 5dc7d1f58a07: Waiting
2024-11-01T16:33:29.7362573Z 11f2f216ce74: Pulling fs layer
2024-11-01T16:33:29.7362928Z 29f7349de058: Waiting
2024-11-01T16:33:29.7363266Z cf2facbc7537: Pulling fs layer
2024-11-01T16:33:29.7363819Z c3946a485fdf: Pulling fs layer
2024-11-01T16:33:29.7364192Z 11f2f216ce74: Waiting
2024-11-01T16:33:29.7364554Z cf2facbc7537: Waiting
2024-11-01T16:33:29.7364941Z 05914e664c50: Pulling fs layer
2024-11-01T16:33:29.7365307Z 05914e664c50: Waiting
2024-11-01T16:33:29.7966214Z f48a1ee812be: Download complete
2024-11-01T16:33:29.8621836Z 30e3c9199e55: Verifying Checksum
2024-11-01T16:33:29.8622570Z 30e3c9199e55: Download complete
2024-11-01T16:33:30.0629113Z 86e5016c2693: Verifying Checksum
2024-11-01T16:33:30.0629667Z 86e5016c2693: Download complete
2024-11-01T16:33:30.1301579Z 0ea45f9f1d46: Verifying Checksum
2024-11-01T16:33:30.1302644Z 0ea45f9f1d46: Download complete
2024-11-01T16:33:30.2105457Z 9b8709086551: Verifying Checksum
2024-11-01T16:33:30.2106325Z 9b8709086551: Download complete
2024-11-01T16:33:30.2867278Z d78418420831: Verifying Checksum
2024-11-01T16:33:30.2867801Z d78418420831: Download complete
2024-11-01T16:33:30.3602313Z 8ed9113f5485: Download complete
2024-11-01T16:33:30.4270023Z 14ebba734113: Download complete
2024-11-01T16:33:30.5035796Z 3cf490ac95a7: Verifying Checksum
2024-11-01T16:33:30.5036645Z 3cf490ac95a7: Download complete
2024-11-01T16:33:30.5731782Z bd5175f3c548: Verifying Checksum
2024-11-01T16:33:30.5733312Z bd5175f3c548: Download complete
2024-11-01T16:33:30.7003287Z 52eeee609856: Verifying Checksum
2024-11-01T16:33:30.7004028Z 52eeee609856: Download complete
2024-11-01T16:33:30.7097372Z 4f4fb700ef54: Download complete
2024-11-01T16:33:30.7805383Z 15991ce77c64: Verifying Checksum
2024-11-01T16:33:30.8664340Z 15991ce77c64: Download complete
2024-11-01T16:33:30.8665176Z f3764307c26b: Download complete
2024-11-01T16:33:30.9465457Z 39a462aa048f: Verifying Checksum
2024-11-01T16:33:30.9466253Z 39a462aa048f: Download complete
2024-11-01T16:33:31.0329539Z 86e5016c2693: Pull complete
2024-11-01T16:33:31.0486442Z dbdd556fb5b1: Verifying Checksum
2024-11-01T16:33:31.0486974Z dbdd556fb5b1: Download complete
2024-11-01T16:33:31.0605368Z f48a1ee812be: Pull complete
2024-11-01T16:33:31.1238395Z 237a8e022b8f: Download complete
2024-11-01T16:33:31.2061269Z 4d96209047f1: Verifying Checksum
2024-11-01T16:33:31.2061867Z 4d96209047f1: Download complete
2024-11-01T16:33:31.2627705Z 4420e33558d9: Verifying Checksum
2024-11-01T16:33:31.2628437Z 4420e33558d9: Download complete
2024-11-01T16:33:31.3186050Z c37cac30f6eb: Verifying Checksum
2024-11-01T16:33:31.3186625Z c37cac30f6eb: Download complete
2024-11-01T16:33:31.3952861Z c948118bdc76: Verifying Checksum
2024-11-01T16:33:31.3953474Z c948118bdc76: Download complete
2024-11-01T16:33:32.6688002Z 74aa3fc4723a: Verifying Checksum
2024-11-01T16:33:32.6688894Z 74aa3fc4723a: Download complete
2024-11-01T16:33:32.7586157Z 3a6f65356b89: Verifying Checksum
2024-11-01T16:33:32.7587958Z 3a6f65356b89: Download complete
2024-11-01T16:33:32.8135118Z 6e9d54a5f617: Verifying Checksum
2024-11-01T16:33:32.8135809Z 6e9d54a5f617: Download complete
2024-11-01T16:33:32.8956227Z 041693a89b05: Verifying Checksum
2024-11-01T16:33:32.9189423Z 041693a89b05: Download complete
2024-11-01T16:33:32.9190296Z 01f11ae3947b: Verifying Checksum
2024-11-01T16:33:32.9190887Z 01f11ae3947b: Download complete
2024-11-01T16:33:32.9692042Z 868221d23f29: Download complete
2024-11-01T16:33:32.9784443Z 6be03ece1c54: Verifying Checksum
2024-11-01T16:33:32.9785243Z 6be03ece1c54: Download complete
2024-11-01T16:33:33.0445639Z 1034221b0067: Verifying Checksum
2024-11-01T16:33:33.0446613Z 1034221b0067: Download complete
2024-11-01T16:33:33.1095816Z 7e0900642653: Download complete
2024-11-01T16:33:33.1911632Z bedd70a265df: Download complete
2024-11-01T16:33:33.2577090Z d871b68b8c5e: Verifying Checksum
2024-11-01T16:33:33.2577901Z d871b68b8c5e: Download complete
2024-11-01T16:33:33.3424994Z 041c18236265: Verifying Checksum
2024-11-01T16:33:33.3425757Z 041c18236265: Download complete
2024-11-01T16:33:33.4097786Z 060225a95076: Download complete
2024-11-01T16:33:33.4870332Z 056d1f4e2b94: Download complete
2024-11-01T16:33:33.5978034Z 4b5067a4de91: Verifying Checksum
2024-11-01T16:33:33.5979274Z 4b5067a4de91: Download complete
2024-11-01T16:33:33.6811226Z fb6f677247c1: Verifying Checksum
2024-11-01T16:33:33.6811793Z fb6f677247c1: Download complete
2024-11-01T16:33:33.7527098Z 709e972708a9: Download complete
2024-11-01T16:33:33.8594509Z ff7efa4d0d03: Verifying Checksum
2024-11-01T16:33:33.8595581Z ff7efa4d0d03: Download complete
2024-11-01T16:33:34.0407892Z 4f7a6785dd36: Verifying Checksum
2024-11-01T16:33:34.0408644Z 4f7a6785dd36: Download complete
2024-11-01T16:33:34.5482635Z dc01a5d84fa1: Verifying Checksum
2024-11-01T16:33:34.5483523Z dc01a5d84fa1: Download complete
2024-11-01T16:33:34.6454539Z 6911517f714b: Verifying Checksum
2024-11-01T16:33:34.6455343Z 6911517f714b: Download complete
2024-11-01T16:33:34.7126580Z c4b256a3e0e0: Verifying Checksum
2024-11-01T16:33:34.7127362Z c4b256a3e0e0: Download complete
2024-11-01T16:33:34.7888777Z 59c0aea7054a: Download complete
2024-11-01T16:33:34.8734359Z 3432201c4908: Download complete
2024-11-01T16:33:35.1565448Z 78581735d693: Verifying Checksum
2024-11-01T16:33:35.2468747Z 78581735d693: Download complete
2024-11-01T16:33:35.2469416Z f2abdd58f890: Download complete
2024-11-01T16:33:35.3120865Z 290b43f33e97: Verifying Checksum
2024-11-01T16:33:35.3121715Z 290b43f33e97: Download complete
2024-11-01T16:33:35.3963672Z 47baf2ba2786: Verifying Checksum
2024-11-01T16:33:35.3964306Z 47baf2ba2786: Download complete
2024-11-01T16:33:35.5787136Z de6286175e72: Verifying Checksum
2024-11-01T16:33:35.5787806Z de6286175e72: Download complete
2024-11-01T16:33:35.6619092Z 96b2aab8e70e: Download complete
2024-11-01T16:33:35.7307881Z 0aa056faa160: Verifying Checksum
2024-11-01T16:33:35.7308625Z 0aa056faa160: Download complete
2024-11-01T16:33:35.8083127Z 90b4cbecc01e: Verifying Checksum
2024-11-01T16:33:35.8084000Z 90b4cbecc01e: Download complete
2024-11-01T16:33:35.8905753Z 971049fe7206: Verifying Checksum
2024-11-01T16:33:35.8906376Z 971049fe7206: Download complete
2024-11-01T16:33:36.0143794Z dcb3cbd15248: Download complete
2024-11-01T16:33:36.0794828Z b359f25c8f3f: Verifying Checksum
2024-11-01T16:33:36.0795694Z b359f25c8f3f: Download complete
2024-11-01T16:33:36.1595272Z e93f3a4e9cee: Verifying Checksum
2024-11-01T16:33:36.1596113Z e93f3a4e9cee: Download complete
2024-11-01T16:33:36.2285200Z dedcc6ca0f58: Verifying Checksum
2024-11-01T16:33:36.2285846Z dedcc6ca0f58: Download complete
2024-11-01T16:33:36.2981956Z 9d53c2778a8e: Verifying Checksum
2024-11-01T16:33:36.2982835Z 9d53c2778a8e: Download complete
2024-11-01T16:33:36.3639591Z 6573da121594: Verifying Checksum
2024-11-01T16:33:36.3640465Z 6573da121594: Download complete
2024-11-01T16:33:36.4581700Z 3c664e41a6c4: Verifying Checksum
2024-11-01T16:33:36.4583015Z 3c664e41a6c4: Download complete
2024-11-01T16:33:36.5403806Z 97b765e7f188: Verifying Checksum
2024-11-01T16:33:36.5404563Z 97b765e7f188: Download complete
2024-11-01T16:33:36.6369196Z 11539d5ffd1e: Download complete
2024-11-01T16:33:36.7123592Z 69a2b353ecfe: Verifying Checksum
2024-11-01T16:33:36.7124342Z 69a2b353ecfe: Download complete
2024-11-01T16:33:36.7816718Z 3e046c373bdd: Verifying Checksum
2024-11-01T16:33:36.7817629Z 3e046c373bdd: Download complete
2024-11-01T16:33:36.8662650Z 6c3de4adbcba: Verifying Checksum
2024-11-01T16:33:36.8663275Z 6c3de4adbcba: Download complete
2024-11-01T16:33:36.9392948Z 4bf8c82a45cd: Verifying Checksum
2024-11-01T16:33:36.9393753Z 4bf8c82a45cd: Download complete
2024-11-01T16:33:37.3090125Z 763709bffc6d: Verifying Checksum
2024-11-01T16:33:37.3090705Z 763709bffc6d: Download complete
2024-11-01T16:33:37.3950871Z df02151305ef: Verifying Checksum
2024-11-01T16:33:37.3951444Z df02151305ef: Download complete
2024-11-01T16:33:37.4669563Z d70ba44bc62a: Verifying Checksum
2024-11-01T16:33:37.4670479Z d70ba44bc62a: Download complete
2024-11-01T16:33:37.5466313Z 29f7349de058: Verifying Checksum
2024-11-01T16:33:37.5467044Z 29f7349de058: Download complete
2024-11-01T16:33:37.6271436Z 11f2f216ce74: Verifying Checksum
2024-11-01T16:33:37.6272162Z 11f2f216ce74: Download complete
2024-11-01T16:33:37.7046269Z cf2facbc7537: Verifying Checksum
2024-11-01T16:33:37.7047073Z cf2facbc7537: Download complete
2024-11-01T16:33:37.7995929Z c3946a485fdf: Download complete
2024-11-01T16:33:38.3954983Z 05914e664c50: Download complete
2024-11-01T16:33:39.3493221Z 5dc7d1f58a07: Verifying Checksum
2024-11-01T16:33:39.3494103Z 5dc7d1f58a07: Download complete
2024-11-01T16:33:42.0621650Z 01f11ae3947b: Pull complete
2024-11-01T16:33:42.2019846Z 30e3c9199e55: Pull complete
2024-11-01T16:33:43.9323601Z 52eeee609856: Pull complete
2024-11-01T16:33:44.0385879Z 0ea45f9f1d46: Pull complete
2024-11-01T16:33:44.1629935Z 9b8709086551: Pull complete
2024-11-01T16:33:44.2848163Z d78418420831: Pull complete
2024-11-01T16:33:44.3932897Z 8ed9113f5485: Pull complete
2024-11-01T16:33:44.4997697Z 14ebba734113: Pull complete
2024-11-01T16:33:44.6113746Z 3cf490ac95a7: Pull complete
2024-11-01T16:33:44.7229761Z bd5175f3c548: Pull complete
2024-11-01T16:33:57.0412727Z ea78d06f9363: Verifying Checksum
2024-11-01T16:33:57.0413237Z ea78d06f9363: Download complete
2024-11-01T16:34:42.1416977Z ea78d06f9363: Pull complete
2024-11-01T16:34:42.1579688Z 4f4fb700ef54: Pull complete
2024-11-01T16:34:42.1694694Z 15991ce77c64: Pull complete
2024-11-01T16:34:42.1834636Z f3764307c26b: Pull complete
2024-11-01T16:34:42.1979389Z 39a462aa048f: Pull complete
2024-11-01T16:34:42.2822247Z dbdd556fb5b1: Pull complete
2024-11-01T16:34:42.3615352Z 237a8e022b8f: Pull complete
2024-11-01T16:34:42.4989769Z 4d96209047f1: Pull complete
2024-11-01T16:34:42.5129652Z 4420e33558d9: Pull complete
2024-11-01T16:34:42.5394097Z c37cac30f6eb: Pull complete
2024-11-01T16:34:42.5586561Z c948118bdc76: Pull complete
2024-11-01T16:34:45.5736336Z 74aa3fc4723a: Pull complete
2024-11-01T16:34:45.6815330Z 3a6f65356b89: Pull complete
2024-11-01T16:34:45.8058917Z 6e9d54a5f617: Pull complete
2024-11-01T16:34:46.0064827Z 041693a89b05: Pull complete
2024-11-01T16:34:46.0947923Z 868221d23f29: Pull complete
2024-11-01T16:34:46.1620120Z 6be03ece1c54: Pull complete
2024-11-01T16:34:57.4837795Z 763709bffc6d: Pull complete
2024-11-01T16:34:57.5629197Z 1034221b0067: Pull complete
2024-11-01T16:34:57.6057810Z 7e0900642653: Pull complete
2024-11-01T16:34:57.6244884Z bedd70a265df: Pull complete
2024-11-01T16:34:57.6382474Z d871b68b8c5e: Pull complete
2024-11-01T16:34:57.6693566Z 041c18236265: Pull complete
2024-11-01T16:34:57.6855986Z 060225a95076: Pull complete
2024-11-01T16:34:57.7010016Z 056d1f4e2b94: Pull complete
2024-11-01T16:34:57.7145202Z 4b5067a4de91: Pull complete
2024-11-01T16:34:57.7435730Z fb6f677247c1: Pull complete
2024-11-01T16:34:57.7574727Z 709e972708a9: Pull complete
2024-11-01T16:34:57.7804291Z ff7efa4d0d03: Pull complete
2024-11-01T16:34:57.7923482Z 4f7a6785dd36: Pull complete
2024-11-01T16:34:59.6388512Z dc01a5d84fa1: Pull complete
2024-11-01T16:34:59.6521408Z 6911517f714b: Pull complete
2024-11-01T16:34:59.6670070Z c4b256a3e0e0: Pull complete
2024-11-01T16:34:59.6912274Z 59c0aea7054a: Pull complete
2024-11-01T16:34:59.7047397Z 3432201c4908: Pull complete
2024-11-01T16:34:59.9608663Z 78581735d693: Pull complete
2024-11-01T16:34:59.9811153Z f2abdd58f890: Pull complete
2024-11-01T16:34:59.9927672Z 290b43f33e97: Pull complete
2024-11-01T16:35:00.0146484Z 47baf2ba2786: Pull complete
2024-11-01T16:35:00.0309277Z de6286175e72: Pull complete
2024-11-01T16:35:00.0568419Z 96b2aab8e70e: Pull complete
2024-11-01T16:35:00.0695897Z 0aa056faa160: Pull complete
2024-11-01T16:35:00.0921324Z 90b4cbecc01e: Pull complete
2024-11-01T16:35:00.1174058Z 971049fe7206: Pull complete
2024-11-01T16:35:00.1843881Z dcb3cbd15248: Pull complete
2024-11-01T16:35:00.2196553Z b359f25c8f3f: Pull complete
2024-11-01T16:35:00.2315218Z e93f3a4e9cee: Pull complete
2024-11-01T16:35:00.2532169Z dedcc6ca0f58: Pull complete
2024-11-01T16:35:00.2788005Z 9d53c2778a8e: Pull complete
2024-11-01T16:35:00.2930947Z 6573da121594: Pull complete
2024-11-01T16:35:00.3170575Z 3c664e41a6c4: Pull complete
2024-11-01T16:35:00.3398306Z 97b765e7f188: Pull complete
2024-11-01T16:35:00.3514019Z 11539d5ffd1e: Pull complete
2024-11-01T16:35:00.3755222Z 69a2b353ecfe: Pull complete
2024-11-01T16:35:00.3872178Z 3e046c373bdd: Pull complete
2024-11-01T16:35:00.4101266Z 6c3de4adbcba: Pull complete
2024-11-01T16:35:00.4212196Z 4bf8c82a45cd: Pull complete
2024-11-01T16:35:10.1532694Z 5dc7d1f58a07: Pull complete
2024-11-01T16:35:10.3772305Z df02151305ef: Pull complete
2024-11-01T16:35:10.4370845Z d70ba44bc62a: Pull complete
2024-11-01T16:35:10.4679517Z 29f7349de058: Pull complete
2024-11-01T16:35:10.4837136Z 11f2f216ce74: Pull complete
2024-11-01T16:35:10.4983174Z cf2facbc7537: Pull complete
2024-11-01T16:35:10.5295188Z c3946a485fdf: Pull complete
2024-11-01T16:35:12.1495635Z 05914e664c50: Pull complete
2024-11-01T16:35:12.1694977Z Digest: sha256:84b16e894952e5aa6d467c4bb4368d576360bf0463a1b87add4ca805eae1be35
2024-11-01T16:35:12.1713024Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:35:12.1735815Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:35:12.1788447Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2024-11-01T16:35:12.1789901Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2024-11-01T16:35:12.1798315Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:12.1798833Z env:
2024-11-01T16:35:12.1799117Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:12.1799477Z ##[endgroup]
2024-11-01T16:35:12.1912522Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84
2024-11-01T16:35:12.1913364Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84
2024-11-01T16:35:12.1914242Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2024-11-01T16:35:12.1914944Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2024-11-01T16:35:12.1921384Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:12.1921931Z env:
2024-11-01T16:35:12.1922213Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:12.1922556Z ##[endgroup]
2024-11-01T16:35:12.7364412Z Defaulting to user installation because normal site-packages is not writeable
2024-11-01T16:35:13.1654971Z Collecting psutil==5.9.1
2024-11-01T16:35:13.2050971Z   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)
2024-11-01T16:35:13.2592631Z Collecting nvidia-ml-py==11.525.84
2024-11-01T16:35:13.2649068Z   Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB)
2024-11-01T16:35:13.3544178Z Installing collected packages: psutil, nvidia-ml-py
2024-11-01T16:35:13.5496952Z Successfully installed nvidia-ml-py-11.525.84 psutil-5.9.1
2024-11-01T16:35:13.7967942Z Prepare all required actions
2024-11-01T16:35:13.7968557Z Getting action download info
2024-11-01T16:35:13.9296682Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6)
2024-11-01T16:35:14.1282039Z Download action repository 'actions/download-artifact@v3' (SHA:9bc31d5ccc31df68ecc42ccf4149144866c47d8a)
2024-11-01T16:35:14.2473705Z ##[group]Run ./.github/actions/download-build-artifacts
2024-11-01T16:35:14.2474354Z with:
2024-11-01T16:35:14.2474643Z   name: linux-focal-py3.12-clang10
2024-11-01T16:35:14.2475066Z   s3-bucket: gha-artifacts
2024-11-01T16:35:14.2475409Z env:
2024-11-01T16:35:14.2475686Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:14.2476036Z ##[endgroup]
2024-11-01T16:35:14.2544541Z ##[group]Run seemethere/download-artifact-s3@v4
2024-11-01T16:35:14.2545032Z with:
2024-11-01T16:35:14.2545340Z   name: linux-focal-py3.12-clang10
2024-11-01T16:35:14.2545744Z   s3-bucket: gha-artifacts
2024-11-01T16:35:14.2546178Z   region: us-east-1
2024-11-01T16:35:14.2546484Z env:
2024-11-01T16:35:14.2546746Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:14.2547102Z ##[endgroup]
2024-11-01T16:35:14.7677937Z (node:39679) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2024-11-01T16:35:14.7679095Z 
2024-11-01T16:35:14.7679470Z Please migrate your code to use AWS SDK for JavaScript (v3).
2024-11-01T16:35:14.7680622Z For more information, check the migration guide at https://a.co/7PzMCcy
2024-11-01T16:35:14.7682057Z (Use `node --trace-warnings ...` to show where the warning was created)
2024-11-01T16:35:14.8467599Z Found 1 objects with prefix pytorch/pytorch/11632514903/linux-focal-py3.12-clang10/
2024-11-01T16:35:14.8468741Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2024-11-01T16:35:18.7221723Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2024-11-01T16:35:18.7229458Z Artifact download has finished successfully
2024-11-01T16:35:18.7410614Z ##[group]Run unzip -o artifacts.zip
2024-11-01T16:35:18.7411083Z unzip -o artifacts.zip
2024-11-01T16:35:18.7417529Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:18.7418076Z env:
2024-11-01T16:35:18.7418363Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:18.7418717Z ##[endgroup]
2024-11-01T16:35:18.7802974Z Archive:  artifacts.zip
2024-11-01T16:35:18.7803818Z    creating: dist/
2024-11-01T16:35:19.7876162Z   inflating: dist/torch-2.6.0a0+gitd1aa4ef-cp312-cp312-linux_x86_64.whl  
2024-11-01T16:35:19.7876888Z    creating: build/custom_test_artifacts/
2024-11-01T16:35:19.7877614Z    creating: build/custom_test_artifacts/custom-op-build/
2024-11-01T16:35:19.7879034Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/
2024-11-01T16:35:19.7880324Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeOutput.log  
2024-11-01T16:35:19.7881730Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/
2024-11-01T16:35:19.7883158Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeSystem.cmake  
2024-11-01T16:35:19.7884566Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/
2024-11-01T16:35:19.7886107Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/tmp/
2024-11-01T16:35:19.7887849Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c  
2024-11-01T16:35:19.7889702Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdC/a.out  
2024-11-01T16:35:19.7891445Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/
2024-11-01T16:35:19.7893200Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/
2024-11-01T16:35:19.7894794Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp  
2024-11-01T16:35:19.7896111Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out  
2024-11-01T16:35:19.7897386Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin  
2024-11-01T16:35:19.7898673Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake  
2024-11-01T16:35:19.7899979Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin  
2024-11-01T16:35:19.7901287Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake  
2024-11-01T16:35:19.7902363Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeTmp/
2024-11-01T16:35:19.7903365Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/cmake.check_cache  
2024-11-01T16:35:19.7904393Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/
2024-11-01T16:35:19.7916676Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/depend.make  
2024-11-01T16:35:19.7918847Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/link.txt  
2024-11-01T16:35:19.7920549Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/cmake_clean.cmake  
2024-11-01T16:35:19.7921764Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/build.make  
2024-11-01T16:35:19.7922970Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/DependInfo.cmake  
2024-11-01T16:35:19.7924181Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/flags.make  
2024-11-01T16:35:19.7925372Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/progress.make  
2024-11-01T16:35:19.7971953Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/CXX.includecache  
2024-11-01T16:35:19.7990264Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/depend.internal  
2024-11-01T16:35:19.8089252Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/custom_ops.dir/op.cpp.o  
2024-11-01T16:35:19.8090405Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/
2024-11-01T16:35:19.8115990Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/depend.make  
2024-11-01T16:35:19.8118296Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/link.txt  
2024-11-01T16:35:19.8119789Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/cmake_clean.cmake  
2024-11-01T16:35:19.8121084Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/build.make  
2024-11-01T16:35:19.8122378Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/DependInfo.cmake  
2024-11-01T16:35:19.8123666Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/flags.make  
2024-11-01T16:35:19.8124934Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/progress.make  
2024-11-01T16:35:19.8171513Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/CXX.includecache  
2024-11-01T16:35:19.8189920Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/depend.internal  
2024-11-01T16:35:19.8240198Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/test_custom_ops.dir/test_custom_ops.cpp.o  
2024-11-01T16:35:19.8242723Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeDirectoryInformation.cmake  
2024-11-01T16:35:19.8244629Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/TargetDirectories.txt  
2024-11-01T16:35:19.8246707Z  extracting: build/custom_test_artifacts/custom-op-build/CMakeFiles/progress.marks  
2024-11-01T16:35:19.8247747Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile2  
2024-11-01T16:35:19.8248772Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/Makefile.cmake  
2024-11-01T16:35:19.8249754Z   inflating: build/custom_test_artifacts/custom-op-build/CMakeCache.txt  
2024-11-01T16:35:19.8250622Z   inflating: build/custom_test_artifacts/custom-op-build/Makefile  
2024-11-01T16:35:19.8251523Z   inflating: build/custom_test_artifacts/custom-op-build/cmake_install.cmake  
2024-11-01T16:35:19.8338676Z   inflating: build/custom_test_artifacts/custom-op-build/libcustom_ops.so  
2024-11-01T16:35:19.8381085Z   inflating: build/custom_test_artifacts/custom-op-build/test_custom_ops  
2024-11-01T16:35:19.8382008Z    creating: build/custom_test_artifacts/jit-hook-build/
2024-11-01T16:35:19.8382973Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/
2024-11-01T16:35:19.8384768Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeOutput.log  
2024-11-01T16:35:19.8385772Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/
2024-11-01T16:35:19.8387291Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeSystem.cmake  
2024-11-01T16:35:19.8389274Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/
2024-11-01T16:35:19.8390844Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/tmp/
2024-11-01T16:35:19.8392461Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c  
2024-11-01T16:35:19.8394497Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdC/a.out  
2024-11-01T16:35:19.8396019Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/
2024-11-01T16:35:19.8397990Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/
2024-11-01T16:35:19.8399309Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp  
2024-11-01T16:35:19.8400659Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out  
2024-11-01T16:35:19.8401950Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin  
2024-11-01T16:35:19.8403210Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake  
2024-11-01T16:35:19.8404518Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin  
2024-11-01T16:35:19.8405817Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake  
2024-11-01T16:35:19.8407186Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeTmp/
2024-11-01T16:35:19.8408194Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/cmake.check_cache  
2024-11-01T16:35:19.8409249Z    creating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/
2024-11-01T16:35:19.8424500Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/depend.make  
2024-11-01T16:35:19.8426765Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/link.txt  
2024-11-01T16:35:19.8428293Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/cmake_clean.cmake  
2024-11-01T16:35:19.8429532Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/build.make  
2024-11-01T16:35:19.8430771Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/DependInfo.cmake  
2024-11-01T16:35:19.8432006Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/flags.make  
2024-11-01T16:35:19.8433231Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/progress.make  
2024-11-01T16:35:19.8480658Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/CXX.includecache  
2024-11-01T16:35:19.8498817Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/depend.internal  
2024-11-01T16:35:19.8532693Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/test_jit_hooks.dir/test_jit_hooks.cpp.o  
2024-11-01T16:35:19.8535095Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/CMakeDirectoryInformation.cmake  
2024-11-01T16:35:19.8536929Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/TargetDirectories.txt  
2024-11-01T16:35:19.8538692Z  extracting: build/custom_test_artifacts/jit-hook-build/CMakeFiles/progress.marks  
2024-11-01T16:35:19.8539706Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile2  
2024-11-01T16:35:19.8540708Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeFiles/Makefile.cmake  
2024-11-01T16:35:19.8541672Z   inflating: build/custom_test_artifacts/jit-hook-build/CMakeCache.txt  
2024-11-01T16:35:19.8542533Z   inflating: build/custom_test_artifacts/jit-hook-build/Makefile  
2024-11-01T16:35:19.8543403Z   inflating: build/custom_test_artifacts/jit-hook-build/cmake_install.cmake  
2024-11-01T16:35:19.8566391Z   inflating: build/custom_test_artifacts/jit-hook-build/test_jit_hooks  
2024-11-01T16:35:19.8567381Z    creating: build/custom_test_artifacts/custom-backend-build/
2024-11-01T16:35:19.8568365Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/
2024-11-01T16:35:19.8570252Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeOutput.log  
2024-11-01T16:35:19.8571325Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/
2024-11-01T16:35:19.8572918Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeSystem.cmake  
2024-11-01T16:35:19.8574662Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/
2024-11-01T16:35:19.8576262Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/tmp/
2024-11-01T16:35:19.8577995Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/CMakeCCompilerId.c  
2024-11-01T16:35:19.8580043Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdC/a.out  
2024-11-01T16:35:19.8581928Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/
2024-11-01T16:35:19.8583816Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/tmp/
2024-11-01T16:35:19.8585205Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/CMakeCXXCompilerId.cpp  
2024-11-01T16:35:19.8586585Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CompilerIdCXX/a.out  
2024-11-01T16:35:19.8587960Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_C.bin  
2024-11-01T16:35:19.8589329Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeCCompiler.cmake  
2024-11-01T16:35:19.8590729Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeDetermineCompilerABI_CXX.bin  
2024-11-01T16:35:19.8592120Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/3.18.5/CMakeCXXCompiler.cmake  
2024-11-01T16:35:19.8593259Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeTmp/
2024-11-01T16:35:19.8594435Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/cmake.check_cache  
2024-11-01T16:35:19.8595598Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/
2024-11-01T16:35:19.8611967Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/depend.make  
2024-11-01T16:35:19.8614800Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/link.txt  
2024-11-01T16:35:19.8616238Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/cmake_clean.cmake  
2024-11-01T16:35:19.8617655Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/build.make  
2024-11-01T16:35:19.8619076Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/DependInfo.cmake  
2024-11-01T16:35:19.8620489Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/flags.make  
2024-11-01T16:35:19.8621890Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/progress.make  
2024-11-01T16:35:19.8667932Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/CXX.includecache  
2024-11-01T16:35:19.8686499Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/depend.internal  
2024-11-01T16:35:19.8711248Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/test_custom_backend.dir/test_custom_backend.cpp.o  
2024-11-01T16:35:19.8712881Z    creating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/
2024-11-01T16:35:19.8716339Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/depend.make  
2024-11-01T16:35:19.8718776Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/link.txt  
2024-11-01T16:35:19.8720407Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/cmake_clean.cmake  
2024-11-01T16:35:19.8721746Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/build.make  
2024-11-01T16:35:19.8723091Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/DependInfo.cmake  
2024-11-01T16:35:19.8724447Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/flags.make  
2024-11-01T16:35:19.8726191Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/progress.make  
2024-11-01T16:35:19.8727579Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/CXX.includecache  
2024-11-01T16:35:19.8729547Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/depend.internal  
2024-11-01T16:35:19.8814307Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/custom_backend.dir/custom_backend.cpp.o  
2024-11-01T16:35:19.8815814Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/CMakeDirectoryInformation.cmake  
2024-11-01T16:35:19.8817160Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/TargetDirectories.txt  
2024-11-01T16:35:19.8818371Z  extracting: build/custom_test_artifacts/custom-backend-build/CMakeFiles/progress.marks  
2024-11-01T16:35:19.8819559Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile2  
2024-11-01T16:35:19.8820630Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeFiles/Makefile.cmake  
2024-11-01T16:35:19.8821674Z   inflating: build/custom_test_artifacts/custom-backend-build/CMakeCache.txt  
2024-11-01T16:35:19.8822654Z   inflating: build/custom_test_artifacts/custom-backend-build/Makefile  
2024-11-01T16:35:19.8823643Z   inflating: build/custom_test_artifacts/custom-backend-build/cmake_install.cmake  
2024-11-01T16:35:19.8893083Z   inflating: build/custom_test_artifacts/custom-backend-build/libcustom_backend.so  
2024-11-01T16:35:19.8913520Z   inflating: build/custom_test_artifacts/custom-backend-build/test_custom_backend  
2024-11-01T16:35:19.8914720Z    creating: build/lib/
2024-11-01T16:35:19.8915131Z   inflating: build/lib/libclog.a     
2024-11-01T16:35:19.8923701Z   inflating: build/lib/libpthreadpool.a  
2024-11-01T16:35:19.8983092Z   inflating: build/lib/libgtest.a    
2024-11-01T16:35:19.8989866Z   inflating: build/lib/libcpuinfo_internals.a  
2024-11-01T16:35:19.8997746Z   inflating: build/lib/libcpuinfo.a  
2024-11-01T16:35:19.9062049Z   inflating: build/lib/libbenchmark.a  
2024-11-01T16:35:19.9138108Z   inflating: build/lib/libprotobuf-lite.a  
2024-11-01T16:35:19.9145507Z   inflating: build/lib/libittnotify.a  
2024-11-01T16:35:19.9171403Z   inflating: build/lib/libtensorpipe_uv.a  
2024-11-01T16:35:19.9256291Z   inflating: build/lib/libgloo.a     
2024-11-01T16:35:19.9320026Z   inflating: build/lib/libasmjit.a   
2024-11-01T16:35:19.9715604Z   inflating: build/lib/libprotobuf.a  
2024-11-01T16:35:19.9735062Z   inflating: build/lib/libfmt.a      
2024-11-01T16:35:19.9736298Z   inflating: build/lib/libtorch_global_deps.so  
2024-11-01T16:35:19.9825212Z   inflating: build/lib/libc10.so     
2024-11-01T16:35:19.9827612Z   inflating: build/lib/libnnpack_reference_layers.a  
2024-11-01T16:35:19.9846002Z   inflating: build/lib/libpytorch_qnnpack.a  
2024-11-01T16:35:19.9858238Z   inflating: build/lib/libgmock.a    
2024-11-01T16:35:19.9859076Z   inflating: build/lib/libgtest_main.a  
2024-11-01T16:35:19.9859967Z   inflating: build/lib/libbenchmark_main.a  
2024-11-01T16:35:20.0298402Z   inflating: build/lib/libprotoc.a   
2024-11-01T16:35:20.0779194Z   inflating: build/lib/libtensorpipe.a  
2024-11-01T16:35:20.1782633Z   inflating: build/lib/libfbgemm.a   
2024-11-01T16:35:20.1783453Z   inflating: build/lib/libgmock_main.a  
2024-11-01T16:35:20.2053783Z   inflating: build/lib/libkineto.a   
2024-11-01T16:35:21.5123614Z   inflating: build/lib/libdnnl.a     
2024-11-01T16:35:21.5167130Z   inflating: build/lib/libonnx_proto.a  
2024-11-01T16:35:21.5184131Z   inflating: build/lib/libnnpack.a   
2024-11-01T16:35:21.5388672Z   inflating: build/lib/libXNNPACK.a  
2024-11-01T16:35:21.6108544Z   inflating: build/lib/libonnx.a     
2024-11-01T16:35:24.0370487Z   inflating: build/lib/libtorch_cpu.so  
2024-11-01T16:35:24.0371719Z   inflating: build/lib/libtorch.so   
2024-11-01T16:35:24.0376295Z   inflating: build/lib/libunbox_lib.a  
2024-11-01T16:35:24.0380869Z   inflating: build/lib/libshm.so     
2024-11-01T16:35:24.0403655Z   inflating: build/lib/libjitbackend_test.so  
2024-11-01T16:35:24.0479411Z   inflating: build/lib/libtorchbind_test.so  
2024-11-01T16:35:24.0505530Z   inflating: build/lib/libbackend_with_compiler.so  
2024-11-01T16:35:24.0533514Z   inflating: build/lib/libaoti_custom_ops.so  
2024-11-01T16:35:24.2557030Z   inflating: build/lib/libtorch_python.so  
2024-11-01T16:35:24.2594723Z   inflating: build/lib/libnnapi_backend.so  
2024-11-01T16:35:24.2595208Z    creating: build/bin/
2024-11-01T16:35:24.2595582Z    creating: build/bin/CMakeFiles/
2024-11-01T16:35:24.2596414Z   inflating: build/bin/CMakeFiles/CMakeDirectoryInformation.cmake  
2024-11-01T16:35:24.2597137Z  extracting: build/bin/CMakeFiles/progress.marks  
2024-11-01T16:35:24.2598140Z   inflating: build/bin/Makefile      
2024-11-01T16:35:24.2599441Z   inflating: build/bin/cmake_install.cmake  
2024-11-01T16:35:24.2600474Z   inflating: build/bin/CTestTestfile.cmake  
2024-11-01T16:35:24.2649257Z   inflating: build/bin/c10_TypeList_test  
2024-11-01T16:35:24.2697162Z   inflating: build/bin/c10_Synchronized_test  
2024-11-01T16:35:24.2746664Z   inflating: build/bin/c10_TypeIndex_test  
2024-11-01T16:35:24.2798131Z   inflating: build/bin/c10_Metaprogramming_test  
2024-11-01T16:35:24.2845628Z   inflating: build/bin/c10_ConstexprCrc_test  
2024-11-01T16:35:24.2895021Z   inflating: build/bin/c10_ssize_test  
2024-11-01T16:35:24.2943009Z   inflating: build/bin/c10_DeadlockDetection_test  
2024-11-01T16:35:24.2996499Z   inflating: build/bin/c10_LeftRight_test  
2024-11-01T16:35:24.3044723Z   inflating: build/bin/c10_Half_test  
2024-11-01T16:35:24.3097078Z   inflating: build/bin/c10_ThreadLocal_test  
2024-11-01T16:35:24.3147686Z   inflating: build/bin/c10_NetworkFlow_test  
2024-11-01T16:35:24.3221152Z   inflating: build/bin/c10_optional_test  
2024-11-01T16:35:24.3280383Z   inflating: build/bin/c10_ordered_preserving_dict_test  
2024-11-01T16:35:24.3335773Z   inflating: build/bin/c10_DispatchKeySet_test  
2024-11-01T16:35:24.3382898Z   inflating: build/bin/c10_StreamGuard_test  
2024-11-01T16:35:24.3431779Z   inflating: build/bin/c10_CompileTimeFunctionPointer_test  
2024-11-01T16:35:24.3479830Z   inflating: build/bin/c10_tempfile_test  
2024-11-01T16:35:24.3530122Z   inflating: build/bin/c10_Device_test  
2024-11-01T16:35:24.3577027Z   inflating: build/bin/c10_TypeTraits_test  
2024-11-01T16:35:24.3626427Z   inflating: build/bin/c10_DeviceGuard_test  
2024-11-01T16:35:24.3677680Z   inflating: build/bin/c10_typeid_test  
2024-11-01T16:35:24.3728529Z   inflating: build/bin/c10_Scalar_test  
2024-11-01T16:35:24.3793572Z   inflating: build/bin/c10_cow_test  
2024-11-01T16:35:24.3842251Z   inflating: build/bin/c10_SymInt_test  
2024-11-01T16:35:24.3892364Z   inflating: build/bin/c10_Bitset_test  
2024-11-01T16:35:24.3947169Z   inflating: build/bin/c10_SizesAndStrides_test  
2024-11-01T16:35:24.4000700Z   inflating: build/bin/c10_InlineStreamGuard_test  
2024-11-01T16:35:24.4049930Z   inflating: build/bin/c10_accumulate_test  
2024-11-01T16:35:24.4102139Z   inflating: build/bin/c10_InlineDeviceGuard_test  
2024-11-01T16:35:24.4151372Z   inflating: build/bin/c10_bit_cast_test  
2024-11-01T16:35:24.4207148Z   inflating: build/bin/c10_string_view_test  
2024-11-01T16:35:24.4259544Z   inflating: build/bin/c10_bfloat16_test  
2024-11-01T16:35:24.4309400Z   inflating: build/bin/c10_irange_test  
2024-11-01T16:35:24.4360542Z   inflating: build/bin/c10_exception_test  
2024-11-01T16:35:24.4410833Z   inflating: build/bin/c10_flags_test  
2024-11-01T16:35:24.4464222Z   inflating: build/bin/c10_complex_test  
2024-11-01T16:35:24.4512898Z   inflating: build/bin/c10_generic_math_test  
2024-11-01T16:35:24.4651551Z   inflating: build/bin/c10_intrusive_ptr_test  
2024-11-01T16:35:24.4707040Z   inflating: build/bin/c10_complex_math_test  
2024-11-01T16:35:24.4764546Z   inflating: build/bin/c10_registry_test  
2024-11-01T16:35:24.4818587Z   inflating: build/bin/c10_logging_test  
2024-11-01T16:35:24.4958124Z   inflating: build/bin/c10_small_vector_test  
2024-11-01T16:35:24.5008973Z   inflating: build/bin/c10_lazy_test  
2024-11-01T16:35:24.5058509Z   inflating: build/bin/c10_string_util_test  
2024-11-01T16:35:24.5100314Z   inflating: build/bin/c10_intrusive_ptr_benchmark  
2024-11-01T16:35:24.5484127Z   inflating: build/bin/protoc-3.13.0.0  
2024-11-01T16:35:24.5865231Z   inflating: build/bin/protoc        
2024-11-01T16:35:24.6271410Z   inflating: build/bin/vec_test_all_types_AVX2  
2024-11-01T16:35:24.6662818Z   inflating: build/bin/vec_test_all_types_AVX512  
2024-11-01T16:35:24.7052975Z   inflating: build/bin/vec_test_all_types_DEFAULT  
2024-11-01T16:35:24.7103671Z   inflating: build/bin/HashStoreTest  
2024-11-01T16:35:24.7156732Z   inflating: build/bin/TCPStoreTest  
2024-11-01T16:35:24.7207838Z   inflating: build/bin/FileStoreTest  
2024-11-01T16:35:24.7273164Z   inflating: build/bin/ProcessGroupGlooTest  
2024-11-01T16:35:24.7323334Z   inflating: build/bin/BackoffTest   
2024-11-01T16:35:24.7326745Z   inflating: build/bin/example_allreduce  
2024-11-01T16:35:24.7380403Z   inflating: build/bin/test_dist_autograd  
2024-11-01T16:35:24.7382949Z   inflating: build/bin/parallel_benchmark  
2024-11-01T16:35:24.7393177Z   inflating: build/bin/aot_model_compiler_test  
2024-11-01T16:35:24.7459301Z   inflating: build/bin/test_mobile_nnc  
2024-11-01T16:35:24.7506364Z   inflating: build/bin/op_allowlist_test  
2024-11-01T16:35:24.7572652Z   inflating: build/bin/test_cpp_rpc  
2024-11-01T16:35:24.7626106Z   inflating: build/bin/backend_fallback_test  
2024-11-01T16:35:24.7722741Z   inflating: build/bin/make_boxed_from_unboxed_functor_test  
2024-11-01T16:35:24.8049371Z   inflating: build/bin/test_lazy     
2024-11-01T16:35:24.8108346Z   inflating: build/bin/kernel_stackbased_test  
2024-11-01T16:35:24.8203529Z   inflating: build/bin/kernel_function_test  
2024-11-01T16:35:24.8332906Z   inflating: build/bin/kernel_function_legacy_test  
2024-11-01T16:35:24.8389710Z   inflating: build/bin/IListRef_test  
2024-11-01T16:35:24.8452711Z   inflating: build/bin/KernelFunction_test  
2024-11-01T16:35:24.8502940Z   inflating: build/bin/xla_tensor_test  
2024-11-01T16:35:24.8574127Z   inflating: build/bin/legacy_vmap_test  
2024-11-01T16:35:24.8625229Z   inflating: build/bin/type_ptr_test  
2024-11-01T16:35:24.8684507Z   inflating: build/bin/type_test     
2024-11-01T16:35:24.8764061Z   inflating: build/bin/tensor_iterator_test  
2024-11-01T16:35:24.8814876Z   inflating: build/bin/stride_properties_test  
2024-11-01T16:35:24.8864332Z   inflating: build/bin/StorageUtils_test  
2024-11-01T16:35:24.8920947Z   inflating: build/bin/apply_utils_test  
2024-11-01T16:35:24.8971055Z   inflating: build/bin/weakref_test  
2024-11-01T16:35:24.9025630Z   inflating: build/bin/NamedTensor_test  
2024-11-01T16:35:24.9081531Z   inflating: build/bin/scalar_test   
2024-11-01T16:35:24.9151403Z   inflating: build/bin/Dict_test     
2024-11-01T16:35:24.9203849Z   inflating: build/bin/broadcast_test  
2024-11-01T16:35:24.9265256Z   inflating: build/bin/basic         
2024-11-01T16:35:24.9320734Z   inflating: build/bin/cpu_generator_test  
2024-11-01T16:35:24.9372827Z   inflating: build/bin/test_parallel  
2024-11-01T16:35:24.9436556Z   inflating: build/bin/MaybeOwned_test  
2024-11-01T16:35:24.9539134Z   inflating: build/bin/kernel_lambda_test  
2024-11-01T16:35:24.9590901Z   inflating: build/bin/cpu_profiling_allocator_test  
2024-11-01T16:35:25.0918327Z   inflating: build/bin/test_api      
2024-11-01T16:35:25.0968290Z   inflating: build/bin/half_test     
2024-11-01T16:35:25.1016892Z   inflating: build/bin/cpu_allocator_test  
2024-11-01T16:35:25.1146470Z   inflating: build/bin/kernel_lambda_legacy_test  
2024-11-01T16:35:25.1195849Z   inflating: build/bin/Dimname_test  
2024-11-01T16:35:25.1249598Z   inflating: build/bin/static_runtime_bench  
2024-11-01T16:35:25.1250902Z   inflating: build/bin/verify_api_visibility  
2024-11-01T16:35:25.1300913Z   inflating: build/bin/memory_overlapping_test  
2024-11-01T16:35:25.1357621Z   inflating: build/bin/atest         
2024-11-01T16:35:25.1447954Z   inflating: build/bin/cpu_rng_test  
2024-11-01T16:35:25.1745919Z   inflating: build/bin/static_runtime_test  
2024-11-01T16:35:25.1794191Z   inflating: build/bin/dispatch_key_set_test  
2024-11-01T16:35:25.1853107Z   inflating: build/bin/inline_container_test  
2024-11-01T16:35:25.1855553Z   inflating: build/bin/thread_init_test  
2024-11-01T16:35:25.1954246Z   inflating: build/bin/List_test     
2024-11-01T16:35:25.2002131Z   inflating: build/bin/operators_test  
2024-11-01T16:35:25.2050003Z   inflating: build/bin/operator_name_test  
2024-11-01T16:35:25.2101068Z   inflating: build/bin/wrapdim_test  
2024-11-01T16:35:25.2158500Z   inflating: build/bin/extension_backend_test  
2024-11-01T16:35:25.2206725Z   inflating: build/bin/dlconvertor_test  
2024-11-01T16:35:25.2256702Z   inflating: build/bin/undefined_tensor_test  
2024-11-01T16:35:25.2351169Z   inflating: build/bin/ivalue_test   
2024-11-01T16:35:25.2398672Z   inflating: build/bin/lazy_tensor_test  
2024-11-01T16:35:25.2446628Z   inflating: build/bin/CppSignature_test  
2024-11-01T16:35:25.2497125Z   inflating: build/bin/mobile_memory_cleanup  
2024-11-01T16:35:25.2821079Z   inflating: build/bin/op_registration_test  
2024-11-01T16:35:25.2875183Z   inflating: build/bin/scalar_tensor_test  
2024-11-01T16:35:25.2925621Z   inflating: build/bin/math_kernel_test  
2024-11-01T16:35:25.2975518Z   inflating: build/bin/memory_format_test  
2024-11-01T16:35:25.3029839Z   inflating: build/bin/native_test   
2024-11-01T16:35:25.3077175Z   inflating: build/bin/reduce_ops_test  
2024-11-01T16:35:25.3125854Z   inflating: build/bin/packedtensoraccessor_test  
2024-11-01T16:35:25.3193988Z   inflating: build/bin/pow_test      
2024-11-01T16:35:25.3247732Z   inflating: build/bin/quantized_test  
2024-11-01T16:35:25.3295352Z   inflating: build/bin/reportMemoryUsage_test  
2024-11-01T16:35:25.3345469Z   inflating: build/bin/test_edge_op_registration  
2024-11-01T16:35:25.3349861Z   inflating: build/bin/torch_shm_manager  
2024-11-01T16:35:25.3366101Z   inflating: build/bin/tutorial_tensorexpr  
2024-11-01T16:35:25.4411234Z   inflating: build/bin/test_tensorexpr  
2024-11-01T16:35:25.4996320Z   inflating: build/bin/test_jit      
2024-11-01T16:35:25.4996808Z    creating: .additional_ci_files/
2024-11-01T16:35:25.5076235Z   inflating: .additional_ci_files/test-times.json  
2024-11-01T16:35:25.5390631Z   inflating: .additional_ci_files/test-class-times.json  
2024-11-01T16:35:25.5421577Z ##[group]Run rm artifacts.zip
2024-11-01T16:35:25.5421982Z rm artifacts.zip
2024-11-01T16:35:25.5428771Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:25.5429299Z env:
2024-11-01T16:35:25.5429573Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:25.5429922Z ##[endgroup]
2024-11-01T16:35:25.5727904Z ##[group]Run df -H
2024-11-01T16:35:25.5728228Z df -H
2024-11-01T16:35:25.5734405Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:25.5734938Z env:
2024-11-01T16:35:25.5735221Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:25.5735588Z ##[endgroup]
2024-11-01T16:35:25.5793348Z Filesystem        Size  Used Avail Use% Mounted on
2024-11-01T16:35:25.5794598Z devtmpfs          4.2M     0  4.2M   0% /dev
2024-11-01T16:35:25.5795113Z tmpfs             8.2G     0  8.2G   0% /dev/shm
2024-11-01T16:35:25.5795774Z tmpfs             3.3G  488k  3.3G   1% /run
2024-11-01T16:35:25.5796409Z /dev/nvme0n1p1    161G   22G  140G  14% /
2024-11-01T16:35:25.5796881Z tmpfs             8.2G  4.1k  8.2G   1% /tmp
2024-11-01T16:35:25.5797391Z /dev/nvme0n1p128   11M  1.4M  9.2M  13% /boot/efi
2024-11-01T16:35:25.5847669Z Prepare all required actions
2024-11-01T16:35:25.5848126Z Getting action download info
2024-11-01T16:35:25.7106021Z ##[group]Run ./.github/actions/download-td-artifacts
2024-11-01T16:35:25.7106526Z with:
2024-11-01T16:35:25.7107107Z env:
2024-11-01T16:35:25.7107385Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:25.7107749Z ##[endgroup]
2024-11-01T16:35:25.7342450Z ##[group]Run seemethere/download-artifact-s3@v4
2024-11-01T16:35:25.7342938Z with:
2024-11-01T16:35:25.7343211Z   name: td_results
2024-11-01T16:35:25.7343539Z   s3-bucket: gha-artifacts
2024-11-01T16:35:25.7343905Z   region: us-east-1
2024-11-01T16:35:25.7344210Z env:
2024-11-01T16:35:25.7344484Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:25.7344835Z ##[endgroup]
2024-11-01T16:35:26.2484661Z (node:39698) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2024-11-01T16:35:26.2486050Z 
2024-11-01T16:35:26.2486619Z Please migrate your code to use AWS SDK for JavaScript (v3).
2024-11-01T16:35:26.2487898Z For more information, check the migration guide at https://a.co/7PzMCcy
2024-11-01T16:35:26.2489580Z (Use `node --trace-warnings ...` to show where the warning was created)
2024-11-01T16:35:26.3150632Z Found 1 objects with prefix pytorch/pytorch/11632514903/td_results/
2024-11-01T16:35:26.3152688Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json
2024-11-01T16:35:26.3728962Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json
2024-11-01T16:35:26.3736936Z Artifact download has finished successfully
2024-11-01T16:35:26.3906323Z ##[group]Run mkdir -p .additional_ci_files
2024-11-01T16:35:26.3907441Z mkdir -p .additional_ci_files
2024-11-01T16:35:26.3908405Z mv td_results.json .additional_ci_files/td_results.json
2024-11-01T16:35:26.3919648Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:26.3920516Z env:
2024-11-01T16:35:26.3921003Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:26.3921589Z ##[endgroup]
2024-11-01T16:35:26.4037201Z ##[group]Run .github/scripts/parse_ref.py
2024-11-01T16:35:26.4037734Z .github/scripts/parse_ref.py
2024-11-01T16:35:26.4043873Z shell: /usr/bin/bash -e {0}
2024-11-01T16:35:26.4044241Z env:
2024-11-01T16:35:26.4044512Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:26.4044867Z ##[endgroup]
2024-11-01T16:35:26.4366064Z Prepare all required actions
2024-11-01T16:35:26.4431753Z ##[group]Run ./.github/actions/get-workflow-job-id
2024-11-01T16:35:26.4432244Z with:
2024-11-01T16:35:26.4432909Z   github-token: ***
2024-11-01T16:35:26.4433217Z env:
2024-11-01T16:35:26.4433494Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:26.4434001Z ##[endgroup]
2024-11-01T16:35:26.4454709Z ##[group]Run set -eux
2024-11-01T16:35:26.4455064Z set -eux
2024-11-01T16:35:26.4455695Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2024-11-01T16:35:26.4462238Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:26.4462765Z env:
2024-11-01T16:35:26.4463046Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:26.4463620Z   GITHUB_TOKEN: ***
2024-11-01T16:35:26.4463929Z ##[endgroup]
2024-11-01T16:35:26.4490833Z + python3 .github/scripts/get_workflow_job_id.py 11632514903 i-00163257c71a08003
2024-11-01T16:35:28.4037251Z setting job-id=32396395154
2024-11-01T16:35:28.4038254Z setting job-name=linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:35:28.4355676Z Prepare all required actions
2024-11-01T16:35:28.4356403Z Getting action download info
2024-11-01T16:35:28.5576920Z ##[group]Run ./.github/actions/filter-test-configs
2024-11-01T16:35:28.5577415Z with:
2024-11-01T16:35:28.5577908Z   github-token: ***
2024-11-01T16:35:28.5581120Z   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"}]}
2024-11-01T16:35:28.5584583Z   job-name: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:35:28.5585272Z env:
2024-11-01T16:35:28.5585535Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:28.5585894Z ##[endgroup]
2024-11-01T16:35:28.5651512Z ##[group]Run nick-fields/retry@v3.0.0
2024-11-01T16:35:28.5651926Z with:
2024-11-01T16:35:28.5652196Z   shell: bash
2024-11-01T16:35:28.5652496Z   timeout_minutes: 10
2024-11-01T16:35:28.5652823Z   max_attempts: 5
2024-11-01T16:35:28.5653142Z   retry_wait_seconds: 30
2024-11-01T16:35:28.5654363Z   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

2024-11-01T16:35:28.5655636Z   polling_interval_seconds: 1
2024-11-01T16:35:28.5656019Z   warning_on_retry: true
2024-11-01T16:35:28.5656376Z   continue_on_error: false
2024-11-01T16:35:28.5656713Z env:
2024-11-01T16:35:28.5656985Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:28.5657571Z   GITHUB_TOKEN: ***
2024-11-01T16:35:28.5657873Z ##[endgroup]
2024-11-01T16:35:31.0320543Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1
2024-11-01T16:35:31.3085883Z Defaulting to user installation because normal site-packages is not writeable
2024-11-01T16:35:31.6795323Z Collecting requests==2.27.1
2024-11-01T16:35:31.7200866Z   Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
2024-11-01T16:35:31.9688697Z Collecting pyyaml==6.0.1
2024-11-01T16:35:31.9739174Z   Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB)
2024-11-01T16:35:32.3831410Z Collecting charset-normalizer~=2.0.0
2024-11-01T16:35:32.3924143Z   Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
2024-11-01T16:35:32.4944162Z Collecting certifi>=2017.4.17
2024-11-01T16:35:32.4996049Z   Downloading certifi-2024.8.30-py3-none-any.whl (167 kB)
2024-11-01T16:35:32.5313413Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10)
2024-11-01T16:35:32.5323262Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10)
2024-11-01T16:35:32.6145481Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml
2024-11-01T16:35:32.9246592Z Successfully installed certifi-2024.8.30 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1
2024-11-01T16:35:34.0002007Z Command completed after 1 attempt(s).
2024-11-01T16:35:34.0086853Z ##[group]Run set -x
2024-11-01T16:35:34.0087202Z set -x
2024-11-01T16:35:34.0087502Z 
2024-11-01T16:35:34.0088079Z # Use relative path here as this could be checked out anywhere, not necessarily
2024-11-01T16:35:34.0088804Z # in runner workspace
2024-11-01T16:35:34.0089341Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py"
2024-11-01T16:35:34.0096055Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:34.0096581Z env:
2024-11-01T16:35:34.0097067Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:34.0097432Z ##[endgroup]
2024-11-01T16:35:34.0126317Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py
2024-11-01T16:35:34.0392689Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}"
2024-11-01T16:35:34.0393274Z echo "Workflow: ${GITHUB_WORKFLOW}"
2024-11-01T16:35:34.0393764Z echo "Job name: ${JOB_NAME}"
2024-11-01T16:35:34.0394368Z 
2024-11-01T16:35:34.0394931Z # Use relative path here as this could be checked out anywhere, not necessarily
2024-11-01T16:35:34.0395660Z # in runner workspace
2024-11-01T16:35:34.0396276Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \
2024-11-01T16:35:34.0396967Z   --workflow "${GITHUB_WORKFLOW}" \
2024-11-01T16:35:34.0397444Z   --job-name "${JOB_NAME}" \
2024-11-01T16:35:34.0400682Z   --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"}]}" \
2024-11-01T16:35:34.0403942Z   --selected-test-configs "" \
2024-11-01T16:35:34.0404416Z   --pr-number "${PR_NUMBER}" \
2024-11-01T16:35:34.0404850Z   --tag "${TAG}" \
2024-11-01T16:35:34.0405250Z   --event-name "${EVENT_NAME}" \
2024-11-01T16:35:34.0405701Z   --schedule "${SCHEDULE}" \
2024-11-01T16:35:34.0406148Z   --branch "${HEAD_BRANCH}"
2024-11-01T16:35:34.0412965Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:34.0413511Z env:
2024-11-01T16:35:34.0413799Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:34.0414394Z   GITHUB_TOKEN: ***
2024-11-01T16:35:34.0414980Z   JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:35:34.0415676Z   PR_NUMBER: 138766
2024-11-01T16:35:34.0415991Z   TAG: 
2024-11-01T16:35:34.0416269Z   EVENT_NAME: pull_request
2024-11-01T16:35:34.0416620Z   SCHEDULE: 
2024-11-01T16:35:34.0416907Z   HEAD_BRANCH: 
2024-11-01T16:35:34.0417186Z ##[endgroup]
2024-11-01T16:35:34.0443330Z Workflow: pull
2024-11-01T16:35:34.0444167Z Job name: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:35:34.2785231Z INFO:root:Found no test-config label on the PR, so all test configs are included
2024-11-01T16:35:34.4398906Z ##[group]Run echo "Filtered matrix:"
2024-11-01T16:35:34.4399540Z echo "Filtered matrix:"
2024-11-01T16:35:34.4402693Z 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"}]}"
2024-11-01T16:35:34.4405847Z 
2024-11-01T16:35:34.4406122Z echo
2024-11-01T16:35:34.4406498Z echo "Is the current job unstable? False"
2024-11-01T16:35:34.4407207Z 
2024-11-01T16:35:34.4407487Z echo
2024-11-01T16:35:34.4408113Z echo "Is keep-going label set? False"
2024-11-01T16:35:34.4408587Z 
2024-11-01T16:35:34.4408969Z echo
2024-11-01T16:35:34.4409293Z echo "Renabled issues? "
2024-11-01T16:35:34.4415995Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:34.4416526Z env:
2024-11-01T16:35:34.4416807Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:34.4417165Z ##[endgroup]
2024-11-01T16:35:34.4444523Z Filtered matrix:
2024-11-01T16:35:34.4449208Z {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}]}
2024-11-01T16:35:34.4452158Z 
2024-11-01T16:35:34.4452331Z Is the current job unstable? False
2024-11-01T16:35:34.4452679Z 
2024-11-01T16:35:34.4453041Z Is keep-going label set? False
2024-11-01T16:35:34.4453323Z 
2024-11-01T16:35:34.4453459Z Renabled issues? 
2024-11-01T16:35:34.4503957Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2024-11-01T16:35:34.4504722Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2024-11-01T16:35:34.4511667Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T16:35:34.4512196Z env:
2024-11-01T16:35:34.4512481Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:34.4512831Z   JOB_TIMEOUT: 600
2024-11-01T16:35:34.4513142Z ##[endgroup]
2024-11-01T16:35:34.4603369Z ##[group]Run # Fetch aws credential from IMDs
2024-11-01T16:35:34.4603997Z # Fetch aws credential from IMDs
2024-11-01T16:35:34.4604629Z eval "$(python3 .github/scripts/get_aws_session_tokens.py)"
2024-11-01T16:35:34.4605211Z set -x
2024-11-01T16:35:34.4605497Z 
2024-11-01T16:35:34.4605854Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2024-11-01T16:35:34.4606444Z   TEST_COMMAND=.ci/pytorch/multigpu-test.sh
2024-11-01T16:35:34.4607402Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2024-11-01T16:35:34.4607946Z   TEST_COMMAND=.ci/onnx/test.sh
2024-11-01T16:35:34.4608378Z else
2024-11-01T16:35:34.4608713Z   TEST_COMMAND=.ci/pytorch/test.sh
2024-11-01T16:35:34.4609151Z fi
2024-11-01T16:35:34.4609422Z 
2024-11-01T16:35:34.4609907Z # detached container should get cleaned up by teardown_ec2_linux
2024-11-01T16:35:34.4610806Z # TODO: Stop building test binaries as part of the build phase
2024-11-01T16:35:34.4611532Z # Used for GPU_FLAG since that doesn't play nice
2024-11-01T16:35:34.4612110Z # shellcheck disable=SC2086,SC2090
2024-11-01T16:35:34.4612581Z container_name=$(docker run \
2024-11-01T16:35:34.4613014Z   ${GPU_FLAG:-} \
2024-11-01T16:35:34.4613453Z   ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
2024-11-01T16:35:34.4613950Z   -e BUILD_ENVIRONMENT \
2024-11-01T16:35:34.4614367Z   -e PR_NUMBER \
2024-11-01T16:35:34.4614724Z   -e GITHUB_ACTIONS \
2024-11-01T16:35:34.4615128Z   -e GITHUB_REPOSITORY \
2024-11-01T16:35:34.4615549Z   -e GITHUB_WORKFLOW \
2024-11-01T16:35:34.4615946Z   -e GITHUB_JOB \
2024-11-01T16:35:34.4616315Z   -e GITHUB_RUN_ID \
2024-11-01T16:35:34.4616705Z   -e GITHUB_RUN_NUMBER \
2024-11-01T16:35:34.4617109Z   -e GITHUB_RUN_ATTEMPT \
2024-11-01T16:35:34.4617524Z   -e JOB_ID \
2024-11-01T16:35:34.4617869Z   -e JOB_NAME \
2024-11-01T16:35:34.4618223Z   -e BASE_SHA \
2024-11-01T16:35:34.4618572Z   -e BRANCH \
2024-11-01T16:35:34.4618900Z   -e SHA1 \
2024-11-01T16:35:34.4619246Z   -e AWS_DEFAULT_REGION \
2024-11-01T16:35:34.4619666Z   -e AWS_ACCESS_KEY_ID \
2024-11-01T16:35:34.4620322Z   -e AWS_SECRET_ACCESS_KEY \
2024-11-01T16:35:34.4620760Z   -e AWS_SESSION_TOKEN \
2024-11-01T16:35:34.4621158Z   -e IN_WHEEL_TEST \
2024-11-01T16:35:34.4621549Z   -e SHARD_NUMBER \
2024-11-01T16:35:34.4621934Z   -e TEST_CONFIG \
2024-11-01T16:35:34.4622312Z   -e NUM_TEST_SHARDS \
2024-11-01T16:35:34.4622715Z   -e REENABLED_ISSUES \
2024-11-01T16:35:34.4623137Z   -e CONTINUE_THROUGH_ERROR \
2024-11-01T16:35:34.4623569Z   -e VERBOSE_TEST_LOGS \
2024-11-01T16:35:34.4623984Z   -e TEST_SHOWLOCALS \
2024-11-01T16:35:34.4624391Z   -e NO_TEST_TIMEOUT \
2024-11-01T16:35:34.4624779Z   -e NO_TD \
2024-11-01T16:35:34.4625127Z   -e TD_DISTRIBUTED \
2024-11-01T16:35:34.4625507Z   -e PR_LABELS \
2024-11-01T16:35:34.4625925Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2024-11-01T16:35:34.4626399Z   -e SCCACHE_BUCKET \
2024-11-01T16:35:34.4626798Z   -e SCCACHE_REGION \
2024-11-01T16:35:34.4627202Z   -e SCCACHE_S3_KEY_PREFIX \
2024-11-01T16:35:34.4627620Z   -e XLA_CUDA \
2024-11-01T16:35:34.4628468Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2024-11-01T16:35:34.4629005Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2024-11-01T16:35:34.4629557Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2024-11-01T16:35:34.4630098Z   -e SKIP_SCCACHE_INITIALIZATION=1 \
2024-11-01T16:35:34.4630586Z   -e HUGGING_FACE_HUB_TOKEN \
2024-11-01T16:35:34.4631053Z   -e SCRIBE_GRAPHQL_ACCESS_TOKEN \
2024-11-01T16:35:34.4631690Z   -e DASHBOARD_TAG \
2024-11-01T16:35:34.4632090Z   -e IS_A100_RUNNER \
2024-11-01T16:35:34.4632504Z   -e ARTIFACTS_FILE_SUFFIX \
2024-11-01T16:35:34.4633047Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2024-11-01T16:35:34.4633630Z   --security-opt seccomp=unconfined \
2024-11-01T16:35:34.4634233Z   --cap-add=SYS_PTRACE \
2024-11-01T16:35:34.4634646Z   --ipc=host \
2024-11-01T16:35:34.4635015Z   --shm-size="${SHM_SIZE}" \
2024-11-01T16:35:34.4635424Z   --tty \
2024-11-01T16:35:34.4635744Z   --detach \
2024-11-01T16:35:34.4636108Z   --name="${container_name}" \
2024-11-01T16:35:34.4636530Z   --user jenkins \
2024-11-01T16:35:34.4637042Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2024-11-01T16:35:34.4637634Z   -w /var/lib/jenkins/workspace \
2024-11-01T16:35:34.4638084Z   "${DOCKER_IMAGE}"
2024-11-01T16:35:34.4638447Z )
2024-11-01T16:35:34.4638853Z # Propagate download.pytorch.org IP to container
2024-11-01T16:35:34.4639848Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts"
2024-11-01T16:35:34.4640900Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2024-11-01T16:35:34.4641894Z docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2024-11-01T16:35:34.4648803Z shell: /usr/bin/bash -e {0}
2024-11-01T16:35:34.4649168Z env:
2024-11-01T16:35:34.4649452Z   GIT_DEFAULT_BRANCH: main
2024-11-01T16:35:34.4649890Z   BUILD_ENVIRONMENT: linux-focal-py3.12-clang10
2024-11-01T16:35:34.4650369Z   PR_NUMBER: 138766
2024-11-01T16:35:34.4650722Z   GITHUB_REPOSITORY: pytorch/pytorch
2024-11-01T16:35:34.4651139Z   GITHUB_WORKFLOW: pull
2024-11-01T16:35:34.4651484Z   GITHUB_JOB: test
2024-11-01T16:35:34.4651804Z   GITHUB_RUN_ID: 11632514903
2024-11-01T16:35:34.4652184Z   GITHUB_RUN_NUMBER: 261361
2024-11-01T16:35:34.4652550Z   GITHUB_RUN_ATTEMPT: 1
2024-11-01T16:35:34.4652873Z   JOB_ID: 32396395154
2024-11-01T16:35:34.4653463Z   JOB_NAME: linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:35:34.4654189Z   BRANCH: pull/138766
2024-11-01T16:35:34.4654589Z   SHA1: d1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea
2024-11-01T16:35:34.4655290Z   BASE_SHA: 2055d3c8dc8b93c52eda6b9c07d716448d9e28c9
2024-11-01T16:35:34.4655791Z   TEST_CONFIG: dynamo_wrapped
2024-11-01T16:35:34.4656152Z   SHARD_NUMBER: 1
2024-11-01T16:35:34.4656463Z   NUM_TEST_SHARDS: 3
2024-11-01T16:35:34.4656793Z   REENABLED_ISSUES: 
2024-11-01T16:35:34.4657133Z   CONTINUE_THROUGH_ERROR: False
2024-11-01T16:35:34.4657530Z   VERBOSE_TEST_LOGS: False
2024-11-01T16:35:34.4657880Z   TEST_SHOWLOCALS: False
2024-11-01T16:35:34.4658232Z   NO_TEST_TIMEOUT: False
2024-11-01T16:35:34.4658567Z   NO_TD: False
2024-11-01T16:35:34.4658869Z   TD_DISTRIBUTED: False
2024-11-01T16:35:34.4659307Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2024-11-01T16:35:34.4659799Z   SCCACHE_REGION: us-east-1
2024-11-01T16:35:34.4660176Z   SCCACHE_S3_KEY_PREFIX: pull
2024-11-01T16:35:34.4660545Z   SHM_SIZE: 1g
2024-11-01T16:35:34.4661500Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:35:34.4662560Z   XLA_CUDA: 
2024-11-01T16:35:34.4663066Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2024-11-01T16:35:34.4663707Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2024-11-01T16:35:34.4664156Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2024-11-01T16:35:34.4664581Z   DASHBOARD_TAG: 
2024-11-01T16:35:34.4665162Z   HUGGING_FACE_HUB_TOKEN: ***
2024-11-01T16:35:34.4665737Z   SCRIBE_GRAPHQL_ACCESS_TOKEN: ***
2024-11-01T16:35:34.4666138Z   IS_A100_RUNNER: 0
2024-11-01T16:35:34.4666662Z   ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T16:35:34.4667257Z ##[endgroup]
2024-11-01T16:35:34.4894859Z Traceback (most recent call last):
2024-11-01T16:35:34.4896167Z   File "/home/ec2-user/actions-runner/_work/pytorch/pytorch/.github/scripts/get_aws_session_tokens.py", line 2, in <module>
2024-11-01T16:35:34.4897147Z     import boto3  # type: ignore[import]
2024-11-01T16:35:34.4897700Z ModuleNotFoundError: No module named 'boto3'
2024-11-01T16:35:34.4929440Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]]
2024-11-01T16:35:34.4930027Z + [[ linux-focal-py3.12-clang10 == *onnx* ]]
2024-11-01T16:35:34.4930514Z + TEST_COMMAND=.ci/pytorch/test.sh
2024-11-01T16:35:34.4938733Z +++ nproc --ignore=2
2024-11-01T16:35:34.4972704Z ++ 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 AWS_ACCESS_KEY_ID -e AWS_SECRET_ACCESS_KEY -e AWS_SESSION_TOKEN -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_11632514903 --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-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T16:35:37.9515865Z + container_name=be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T16:35:37.9518236Z + grep download.pytorch.org /etc/hosts
2024-11-01T16:35:37.9520178Z + docker exec -i be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d sudo bash -c '/bin/cat >> /etc/hosts'
2024-11-01T16:35:38.1586790Z + echo DOCKER_CONTAINER_ID=be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T16:35:38.1591643Z ++ echo dist/torch-2.6.0a0+gitd1aa4ef-cp312-cp312-linux_x86_64.whl
2024-11-01T16:35:38.1594025Z + docker exec -t be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d sh -c 'pip install dist/torch-2.6.0a0+gitd1aa4ef-cp312-cp312-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh'
2024-11-01T16:35:38.7067213Z Processing ./dist/torch-2.6.0a0+gitd1aa4ef-cp312-cp312-linux_x86_64.whl (from torch==2.6.0a0+gitd1aa4ef)
2024-11-01T16:35:39.1895106Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (3.16.1)
2024-11-01T16:35:39.1898818Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (4.12.2)
2024-11-01T16:35:39.1902262Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (2.8.8)
2024-11-01T16:35:39.1905952Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (3.1.4)
2024-11-01T16:35:39.1908998Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (2024.10.0)
2024-11-01T16:35:39.1924116Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (75.1.0)
2024-11-01T16:35:39.1929507Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (1.13.1)
2024-11-01T16:35:39.1946419Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (1.3.0)
2024-11-01T16:35:39.1965400Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (3.3.0)
2024-11-01T16:35:39.1981895Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from opt-einsum>=3.3->torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (1.26.2)
2024-11-01T16:35:39.2095381Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch==2.6.0a0+gitd1aa4ef->torch==2.6.0a0+gitd1aa4ef) (3.0.2)
2024-11-01T16:35:39.4058636Z Installing collected packages: torch
2024-11-01T16:35:50.3980496Z Successfully installed torch-2.6.0a0+gitd1aa4ef
2024-11-01T16:35:50.5274117Z + export TERM=vt100
2024-11-01T16:35:50.5274575Z + TERM=vt100
2024-11-01T16:35:50.5276482Z ++ dirname .ci/pytorch/test.sh
2024-11-01T16:35:50.5293575Z + source .ci/pytorch/common.sh
2024-11-01T16:35:50.5301910Z +++ dirname .ci/pytorch/common.sh
2024-11-01T16:35:50.5309418Z ++ source .ci/pytorch/common_utils.sh
2024-11-01T16:35:50.5315059Z +++ declare -f -t trap_add
2024-11-01T16:35:50.5321445Z ++ set -ex
2024-11-01T16:35:50.5322003Z ++ [[ linux-focal-py3.12-clang10 == *rocm* ]]
2024-11-01T16:35:50.5322724Z ++ BUILD_TEST_LIBTORCH=0
2024-11-01T16:35:50.5323298Z + [[ linux-focal-py3.12-clang10 != *rocm* ]]
2024-11-01T16:35:50.5326573Z ++ stat -c %u /var/lib/jenkins/workspace
2024-11-01T16:35:50.5376367Z + WORKSPACE_ORIGINAL_OWNER_ID=1000
2024-11-01T16:35:50.5377102Z + trap_add cleanup_workspace EXIT
2024-11-01T16:35:50.5377544Z + trap_add_cmd=cleanup_workspace
2024-11-01T16:35:50.5377928Z + shift
2024-11-01T16:35:50.5378244Z + for trap_add_name in "$@"
2024-11-01T16:35:50.5385762Z +++ trap -p EXIT
2024-11-01T16:35:50.5388309Z ++ eval 'extract_trap_cmd '
2024-11-01T16:35:50.5388783Z +++ extract_trap_cmd
2024-11-01T16:35:50.5389151Z +++ printf '%s\n' ''
2024-11-01T16:35:50.5389613Z ++ printf '%s\n' cleanup_workspace
2024-11-01T16:35:50.5390614Z + trap -- '
2024-11-01T16:35:50.5391609Z cleanup_workspace' EXIT
2024-11-01T16:35:50.5392120Z + sudo chown -R jenkins /var/lib/jenkins/workspace
2024-11-01T16:35:51.0373578Z + git config --global --add safe.directory /var/lib/jenkins/workspace
2024-11-01T16:35:51.0548509Z + echo 'Environment variables:'
2024-11-01T16:35:51.0549020Z Environment variables:
2024-11-01T16:35:51.0549434Z + env
2024-11-01T16:35:51.0567880Z INSTALLED_DB=yes
2024-11-01T16:35:51.0569194Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2024-11-01T16:35:51.0569829Z CONTINUE_THROUGH_ERROR=False
2024-11-01T16:35:51.0570342Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10
2024-11-01T16:35:51.0570836Z HOSTNAME=be73fc5f4173
2024-11-01T16:35:51.0571779Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0572719Z GITHUB_ACTION=__self
2024-11-01T16:35:51.0573231Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2024-11-01T16:35:51.0573702Z GITHUB_RUN_NUMBER=261361
2024-11-01T16:35:51.0574162Z TEST_CONFIG=dynamo_wrapped
2024-11-01T16:35:51.0574557Z GITHUB_REPOSITORY_OWNER_ID=21003710
2024-11-01T16:35:51.0575076Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2024-11-01T16:35:51.0575497Z IS_A100_RUNNER=0
2024-11-01T16:35:51.0576176Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2024-11-01T16:35:51.0576592Z GITHUB_TRIGGERING_ACTOR=c00w
2024-11-01T16:35:51.0576974Z GITHUB_REF_TYPE=branch
2024-11-01T16:35:51.0577331Z TORCH_CUDA_ARCH_LIST=Maxwell
2024-11-01T16:35:51.0577800Z BASE_SHA=2055d3c8dc8b93c52eda6b9c07d716448d9e28c9
2024-11-01T16:35:51.0578259Z XLA_CUDA=
2024-11-01T16:35:51.0578712Z HUGGING_FACE_HUB_TOKEN=***
2024-11-01T16:35:51.0580560Z ***
2024-11-01T16:35:51.0581471Z GITHUB_REPOSITORY_ID=65600975
2024-11-01T16:35:51.0581864Z GITHUB_ACTIONS=true
2024-11-01T16:35:51.0582254Z SHA1=d1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea
2024-11-01T16:35:51.0582779Z GITHUB_SHA=368da7510f5d755034681777a4e5ae6d33c07b38
2024-11-01T16:35:51.0583546Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/138766/merge
2024-11-01T16:35:51.0584247Z UCC_HOME=/usr
2024-11-01T16:35:51.0584554Z VERBOSE_TEST_LOGS=False
2024-11-01T16:35:51.0584914Z GITHUB_REF=refs/pull/138766/merge
2024-11-01T16:35:51.0585292Z SHARD_NUMBER=1
2024-11-01T16:35:51.0585607Z GITHUB_REF_PROTECTED=false
2024-11-01T16:35:51.0585970Z HOME=/var/lib/jenkins
2024-11-01T16:35:51.0586350Z GITHUB_API_URL=https://api.github.com
2024-11-01T16:35:51.0586812Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2024-11-01T16:35:51.0587207Z UCX_COMMIT=
2024-11-01T16:35:51.0587504Z SCCACHE_S3_KEY_PREFIX=pull
2024-11-01T16:35:51.0587862Z NUM_TEST_SHARDS=3
2024-11-01T16:35:51.0588172Z UCX_HOME=/usr
2024-11-01T16:35:51.0589167Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0590417Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:35:51.0591669Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0592929Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2024-11-01T16:35:51.0593654Z GITHUB_EVENT_NAME=pull_request
2024-11-01T16:35:51.0594160Z DASHBOARD_TAG=
2024-11-01T16:35:51.0594473Z GITHUB_RUN_ID=11632514903
2024-11-01T16:35:51.0595498Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0596450Z GITHUB_ACTOR=c00w
2024-11-01T16:35:51.0596766Z PR_NUMBER=138766
2024-11-01T16:35:51.0597082Z DESIRED_CUDA=
2024-11-01T16:35:51.0597386Z GITHUB_RUN_ATTEMPT=1
2024-11-01T16:35:51.0597745Z ANACONDA_PYTHON_VERSION=3.12
2024-11-01T16:35:51.0598208Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2024-11-01T16:35:51.0598705Z TERM=vt100
2024-11-01T16:35:51.0598998Z INSTALLED_VISION=yes
2024-11-01T16:35:51.0599337Z BRANCH=pull/138766
2024-11-01T16:35:51.0599719Z SCCACHE_REGION=us-east-1
2024-11-01T16:35:51.0600074Z OPENSSL_ROOT_DIR=/opt/openssl
2024-11-01T16:35:51.0600614Z CUDA_PATH=/usr/local/cuda
2024-11-01T16:35:51.0601446Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2024-11-01T16:35:51.0602276Z GITHUB_SERVER_URL=https://github.com
2024-11-01T16:35:51.0602695Z UCC_COMMIT=
2024-11-01T16:35:51.0602970Z REENABLED_ISSUES=
2024-11-01T16:35:51.0603272Z DOCS=
2024-11-01T16:35:51.0603544Z INSTALLED_ANDROID=
2024-11-01T16:35:51.0603850Z SHLVL=1
2024-11-01T16:35:51.0604111Z MAX_JOBS=6
2024-11-01T16:35:51.0604379Z GITHUB_ACTOR_ID=486199
2024-11-01T16:35:51.0604841Z GITHUB_WORKFLOW_SHA=368da7510f5d755034681777a4e5ae6d33c07b38
2024-11-01T16:35:51.0605395Z GITHUB_REF_NAME=138766/merge
2024-11-01T16:35:51.0606049Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2024-11-01T16:35:51.0606967Z GITHUB_JOB=test
2024-11-01T16:35:51.0607272Z NO_TEST_TIMEOUT=False
2024-11-01T16:35:51.0607616Z TD_DISTRIBUTED=False
2024-11-01T16:35:51.0607977Z GITHUB_REPOSITORY=pytorch/pytorch
2024-11-01T16:35:51.0608403Z GITHUB_RETENTION_DAYS=90
2024-11-01T16:35:51.0608768Z OPENSSL_DIR=/opt/openssl
2024-11-01T16:35:51.0609114Z GITHUB_ACTION_REPOSITORY=
2024-11-01T16:35:51.0610270Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2024-11-01T16:35:51.0611468Z GITHUB_BASE_REF=gh/c00w/2/base
2024-11-01T16:35:51.0611851Z INSTALLED_ACL=
2024-11-01T16:35:51.0612464Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T16:35:51.0613068Z CI=true
2024-11-01T16:35:51.0613347Z GITHUB_REPOSITORY_OWNER=pytorch
2024-11-01T16:35:51.0613897Z JOB_ID=32396395154
2024-11-01T16:35:51.0614221Z INSTALLED_PROTOBUF=yes
2024-11-01T16:35:51.0614579Z GITHUB_HEAD_REF=gh/c00w/2/head
2024-11-01T16:35:51.0614961Z GITHUB_ACTION_REF=
2024-11-01T16:35:51.0615418Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2024-11-01T16:35:51.0615917Z TEST_SHOWLOCALS=False
2024-11-01T16:35:51.0616257Z GITHUB_WORKFLOW=pull
2024-11-01T16:35:51.0616613Z DEBIAN_FRONTEND=noninteractive
2024-11-01T16:35:51.0617621Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0618497Z NO_TD=False
2024-11-01T16:35:51.0618800Z SKIP_SCCACHE_INITIALIZATION=1
2024-11-01T16:35:51.0619178Z _=/usr/bin/env
2024-11-01T16:35:51.0619713Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2024-11-01T16:35:51.0723162Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch
2024-11-01T16:35:51.0724771Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/bin
2024-11-01T16:35:51.0725807Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib
2024-11-01T16:35:51.0726800Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/test
2024-11-01T16:35:51.0727457Z + BUILD_DIR=build
2024-11-01T16:35:51.0727790Z + BUILD_RENAMED_DIR=build_renamed
2024-11-01T16:35:51.0728202Z + BUILD_BIN_DIR=build/bin
2024-11-01T16:35:51.0728618Z + SHARD_NUMBER=1
2024-11-01T16:35:51.0729100Z + NUM_TEST_SHARDS=3
2024-11-01T16:35:51.0729429Z + export VALGRIND=ON
2024-11-01T16:35:51.0729754Z + VALGRIND=ON
2024-11-01T16:35:51.0730190Z + [[ linux-focal-py3.12-clang10 == *clang9* ]]
2024-11-01T16:35:51.0730652Z + [[ 0 == \1 ]]
2024-11-01T16:35:51.0730937Z + [[ False == \1 ]]
2024-11-01T16:35:51.0731371Z + [[ linux-focal-py3.12-clang10 != *bazel* ]]
2024-11-01T16:35:51.0731874Z ++ realpath build/custom_test_artifacts
2024-11-01T16:35:51.0751955Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2024-11-01T16:35:51.0752821Z + [[ -n '' ]]
2024-11-01T16:35:51.0753196Z + echo 'Environment variables'
2024-11-01T16:35:51.0753585Z Environment variables
2024-11-01T16:35:51.0754015Z + env
2024-11-01T16:35:51.0760029Z INSTALLED_DB=yes
2024-11-01T16:35:51.0761111Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2024-11-01T16:35:51.0762214Z CONTINUE_THROUGH_ERROR=False
2024-11-01T16:35:51.0763151Z BUILD_ENVIRONMENT=linux-focal-py3.12-clang10
2024-11-01T16:35:51.0763822Z HOSTNAME=be73fc5f4173
2024-11-01T16:35:51.0764997Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0765887Z GITHUB_ACTION=__self
2024-11-01T16:35:51.0766248Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2024-11-01T16:35:51.0766667Z GITHUB_RUN_NUMBER=261361
2024-11-01T16:35:51.0767025Z TEST_CONFIG=dynamo_wrapped
2024-11-01T16:35:51.0767397Z GITHUB_REPOSITORY_OWNER_ID=21003710
2024-11-01T16:35:51.0767915Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2024-11-01T16:35:51.0768343Z IS_A100_RUNNER=0
2024-11-01T16:35:51.0769193Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2024-11-01T16:35:51.0769603Z GITHUB_TRIGGERING_ACTOR=c00w
2024-11-01T16:35:51.0769978Z GITHUB_REF_TYPE=branch
2024-11-01T16:35:51.0770319Z TORCH_CUDA_ARCH_LIST=Maxwell
2024-11-01T16:35:51.0770762Z BASE_SHA=2055d3c8dc8b93c52eda6b9c07d716448d9e28c9
2024-11-01T16:35:51.0771241Z XLA_CUDA=
2024-11-01T16:35:51.0771724Z HUGGING_FACE_HUB_TOKEN=***
2024-11-01T16:35:51.0772218Z ***
2024-11-01T16:35:51.0772501Z GITHUB_REPOSITORY_ID=65600975
2024-11-01T16:35:51.0772864Z GITHUB_ACTIONS=true
2024-11-01T16:35:51.0773253Z SHA1=d1aa4ef5a6c0ee2d57fb0086f5557e34537f0fea
2024-11-01T16:35:51.0773791Z GITHUB_SHA=368da7510f5d755034681777a4e5ae6d33c07b38
2024-11-01T16:35:51.0774560Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/138766/merge
2024-11-01T16:35:51.0775243Z UCC_HOME=/usr
2024-11-01T16:35:51.0775533Z VERBOSE_TEST_LOGS=False
2024-11-01T16:35:51.0775971Z GITHUB_REF=refs/pull/138766/merge
2024-11-01T16:35:51.0776545Z SHARD_NUMBER=1
2024-11-01T16:35:51.0776861Z GITHUB_REF_PROTECTED=false
2024-11-01T16:35:51.0777226Z HOME=/var/lib/jenkins
2024-11-01T16:35:51.0777591Z GITHUB_API_URL=https://api.github.com
2024-11-01T16:35:51.0778049Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2024-11-01T16:35:51.0778460Z UCX_COMMIT=
2024-11-01T16:35:51.0778758Z SCCACHE_S3_KEY_PREFIX=pull
2024-11-01T16:35:51.0779200Z NUM_TEST_SHARDS=3
2024-11-01T16:35:51.0779496Z UCX_HOME=/usr
2024-11-01T16:35:51.0780434Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0781677Z JOB_NAME=linux-focal-py3.12-clang10 / test (dynamo_wrapped, 1, 3, linux.2xlarge)
2024-11-01T16:35:51.0783007Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0784254Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2024-11-01T16:35:51.0785054Z GITHUB_EVENT_NAME=pull_request
2024-11-01T16:35:51.0785421Z DASHBOARD_TAG=
2024-11-01T16:35:51.0785728Z GITHUB_RUN_ID=11632514903
2024-11-01T16:35:51.0786752Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0787695Z GITHUB_ACTOR=c00w
2024-11-01T16:35:51.0788015Z PR_NUMBER=138766
2024-11-01T16:35:51.0788320Z DESIRED_CUDA=
2024-11-01T16:35:51.0788607Z GITHUB_RUN_ATTEMPT=1
2024-11-01T16:35:51.0789001Z VALGRIND=ON
2024-11-01T16:35:51.0789299Z ANACONDA_PYTHON_VERSION=3.12
2024-11-01T16:35:51.0789761Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2024-11-01T16:35:51.0790240Z TERM=vt100
2024-11-01T16:35:51.0790510Z INSTALLED_VISION=yes
2024-11-01T16:35:51.0790846Z BRANCH=pull/138766
2024-11-01T16:35:51.0791216Z SCCACHE_REGION=us-east-1
2024-11-01T16:35:51.0791583Z OPENSSL_ROOT_DIR=/opt/openssl
2024-11-01T16:35:51.0791968Z CUDA_PATH=/usr/local/cuda
2024-11-01T16:35:51.0792787Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2024-11-01T16:35:51.0793677Z GITHUB_SERVER_URL=https://github.com
2024-11-01T16:35:51.0794232Z UCC_COMMIT=
2024-11-01T16:35:51.0794522Z REENABLED_ISSUES=
2024-11-01T16:35:51.0794823Z DOCS=
2024-11-01T16:35:51.0795077Z INSTALLED_ANDROID=
2024-11-01T16:35:51.0795378Z SHLVL=1
2024-11-01T16:35:51.0795751Z MAX_JOBS=6
2024-11-01T16:35:51.0796035Z GITHUB_ACTOR_ID=486199
2024-11-01T16:35:51.0796501Z GITHUB_WORKFLOW_SHA=368da7510f5d755034681777a4e5ae6d33c07b38
2024-11-01T16:35:51.0797030Z GITHUB_REF_NAME=138766/merge
2024-11-01T16:35:51.0797730Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2024-11-01T16:35:51.0798345Z GITHUB_JOB=test
2024-11-01T16:35:51.0798660Z NO_TEST_TIMEOUT=False
2024-11-01T16:35:51.0798998Z TD_DISTRIBUTED=False
2024-11-01T16:35:51.0799356Z GITHUB_REPOSITORY=pytorch/pytorch
2024-11-01T16:35:51.0799758Z GITHUB_RETENTION_DAYS=90
2024-11-01T16:35:51.0800115Z OPENSSL_DIR=/opt/openssl
2024-11-01T16:35:51.0800481Z GITHUB_ACTION_REPOSITORY=
2024-11-01T16:35:51.0801619Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2024-11-01T16:35:51.0802807Z GITHUB_BASE_REF=gh/c00w/2/base
2024-11-01T16:35:51.0803174Z INSTALLED_ACL=
2024-11-01T16:35:51.0803889Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T16:35:51.0804492Z CI=true
2024-11-01T16:35:51.0804786Z GITHUB_REPOSITORY_OWNER=pytorch
2024-11-01T16:35:51.0805175Z JOB_ID=32396395154
2024-11-01T16:35:51.0805484Z INSTALLED_PROTOBUF=yes
2024-11-01T16:35:51.0805839Z GITHUB_HEAD_REF=gh/c00w/2/head
2024-11-01T16:35:51.0806217Z GITHUB_ACTION_REF=
2024-11-01T16:35:51.0806978Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2024-11-01T16:35:51.0807486Z TEST_SHOWLOCALS=False
2024-11-01T16:35:51.0807809Z GITHUB_WORKFLOW=pull
2024-11-01T16:35:51.0808163Z DEBIAN_FRONTEND=noninteractive
2024-11-01T16:35:51.0809338Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_149176ba-dd3d-4701-9c58-354bf243752a
2024-11-01T16:35:51.0810234Z NO_TD=False
2024-11-01T16:35:51.0810543Z SKIP_SCCACHE_INITIALIZATION=1
2024-11-01T16:35:51.0810922Z _=/usr/bin/env
2024-11-01T16:35:51.0811270Z + echo 'Testing pytorch'
2024-11-01T16:35:51.0811627Z Testing pytorch
2024-11-01T16:35:51.0811970Z + export LANG=C.UTF-8
2024-11-01T16:35:51.0812316Z + LANG=C.UTF-8
2024-11-01T16:35:51.0834487Z + PR_NUMBER=138766
2024-11-01T16:35:51.0835047Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]]
2024-11-01T16:35:51.0835884Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]]
2024-11-01T16:35:51.0836698Z + [[ dynamo_wrapped == \s\l\o\w ]]
2024-11-01T16:35:51.0837615Z + [[ linux-focal-py3.12-clang10 == *slow-gradcheck* ]]
2024-11-01T16:35:51.0838296Z + [[ linux-focal-py3.12-clang10 == *cuda* ]]
2024-11-01T16:35:51.0838851Z + [[ linux-focal-py3.12-clang10 == *rocm* ]]
2024-11-01T16:35:51.0839413Z + [[ linux-focal-py3.12-clang10 == *xpu* ]]
2024-11-01T16:35:51.0839913Z + [[ dynamo_wrapped == *crossref* ]]
2024-11-01T16:35:51.0840437Z + [[ linux-focal-py3.12-clang10 == *rocm* ]]
2024-11-01T16:35:51.0840997Z + [[ linux-focal-py3.12-clang10 == *xpu* ]]
2024-11-01T16:35:51.0841574Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]]
2024-11-01T16:35:51.0842100Z + pip_install --user ninja==1.10.2
2024-11-01T16:35:51.0842676Z + pip install --progress-bar off --user ninja==1.10.2
2024-11-01T16:35:51.6201485Z Collecting ninja==1.10.2
2024-11-01T16:35:51.6406726Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB)
2024-11-01T16:35:51.6510407Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2024-11-01T16:35:51.8312124Z Installing collected packages: ninja
2024-11-01T16:35:51.8400325Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2024-11-01T16:35:51.8401770Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2024-11-01T16:35:51.8497866Z Successfully installed ninja-1.10.2
2024-11-01T16:35:51.9665205Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2024-11-01T16:35:51.9668298Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2024-11-01T16:35:51.9670060Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]]
2024-11-01T16:35:51.9670636Z + install_tlparse
2024-11-01T16:35:51.9671031Z + pip_install --user tlparse==0.3.25
2024-11-01T16:35:51.9671715Z + pip install --progress-bar off --user tlparse==0.3.25
2024-11-01T16:35:52.3982980Z Collecting tlparse==0.3.25
2024-11-01T16:35:52.4169268Z   Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.7 kB)
2024-11-01T16:35:52.4265914Z Downloading tlparse-0.3.25-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)
2024-11-01T16:35:52.6239584Z Installing collected packages: tlparse
2024-11-01T16:35:52.6613856Z Successfully installed tlparse-0.3.25
2024-11-01T16:35:52.7770370Z ++ python -m site --user-base
2024-11-01T16:35:52.7929641Z + 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_3.12/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2024-11-01T16:35:52.7931973Z + [[ linux-focal-py3.12-clang10 == *asan* ]]
2024-11-01T16:35:52.7932999Z + [[ linux-focal-py3.12-clang10 == *-debug* ]]
2024-11-01T16:35:52.7933977Z + [[ linux-focal-py3.12-clang10 != *-bazel-* ]]
2024-11-01T16:35:52.7935495Z + echo 'We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass'
2024-11-01T16:35:52.7937800Z We are not in debug mode: linux-focal-py3.12-clang10. Expect the assertion to pass
2024-11-01T16:35:52.7938953Z + cd test
2024-11-01T16:35:52.7939541Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)'
2024-11-01T16:35:54.5759394Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2024-11-01T16:35:54.5760012Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2024-11-01T16:35:54.5763495Z + DYNAMO_BENCHMARK_FLAGS=()
2024-11-01T16:35:54.5765729Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]]
2024-11-01T16:35:54.5766743Z + [[ dynamo_wrapped == *dynamo_eager* ]]
2024-11-01T16:35:54.5767647Z + [[ dynamo_wrapped == *aot_eager* ]]
2024-11-01T16:35:54.5768521Z + [[ dynamo_wrapped == *aot_inductor* ]]
2024-11-01T16:35:54.5769402Z + [[ dynamo_wrapped == *inductor* ]]
2024-11-01T16:35:54.5770238Z + [[ dynamo_wrapped == *dynamic* ]]
2024-11-01T16:35:54.5771049Z + [[ dynamo_wrapped == *cpu* ]]
2024-11-01T16:35:54.5772148Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
2024-11-01T16:35:54.5803020Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]]
2024-11-01T16:35:54.5803702Z + [[ linux-focal-py3.12-clang10 == *-bazel-* ]]
2024-11-01T16:35:54.5805942Z + cd test
2024-11-01T16:35:54.5806465Z + python -c 'import torch; print(torch.__config__.show())'
2024-11-01T16:35:55.9857824Z PyTorch built with:
2024-11-01T16:35:55.9858504Z   - GCC 4.2
2024-11-01T16:35:55.9858855Z   - C++ Version: 201703
2024-11-01T16:35:55.9859242Z   - clang 10.0.0
2024-11-01T16:35:55.9860182Z   - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2024-11-01T16:35:55.9861473Z   - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
2024-11-01T16:35:55.9862211Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2024-11-01T16:35:55.9862742Z   - LAPACK is enabled (usually provided by MKL)
2024-11-01T16:35:55.9863252Z   - NNPACK is enabled
2024-11-01T16:35:55.9863657Z   - CPU capability usage: AVX512
2024-11-01T16:35:55.9872269Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, 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, 
2024-11-01T16:35:55.9880379Z 
2024-11-01T16:35:56.3595642Z + cd test
2024-11-01T16:35:56.3596904Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2024-11-01T16:35:57.7633630Z ATen/Parallel:
2024-11-01T16:35:57.7634255Z 	at::get_num_threads() : 4
2024-11-01T16:35:57.7634747Z 	at::get_num_interop_threads() : 4
2024-11-01T16:35:57.7635179Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2024-11-01T16:35:57.7635731Z 	omp_get_max_threads() : 4
2024-11-01T16:35:57.7639077Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2024-11-01T16:35:57.7640121Z 	mkl_get_max_threads() : 4
2024-11-01T16:35:57.7640816Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
2024-11-01T16:35:57.7641511Z std::thread::hardware_concurrency() : 8
2024-11-01T16:35:57.7642272Z Environment variables:
2024-11-01T16:35:57.7642623Z 	OMP_NUM_THREADS : [not set]
2024-11-01T16:35:57.7643007Z 	MKL_NUM_THREADS : [not set]
2024-11-01T16:35:57.7643400Z ATen parallel backend: OpenMP
2024-11-01T16:35:57.7643660Z 
2024-11-01T16:35:58.1401738Z + [[ linux-focal-py3.12-clang10 == *aarch64* ]]
2024-11-01T16:35:58.1402693Z + [[ dynamo_wrapped == *backward* ]]
2024-11-01T16:35:58.1403288Z + [[ dynamo_wrapped == *xla* ]]
2024-11-01T16:35:58.1403978Z + [[ dynamo_wrapped == *executorch* ]]
2024-11-01T16:35:58.1404845Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]]
2024-11-01T16:35:58.1405900Z + [[ linux-focal-py3.12-clang10 == *libtorch* ]]
2024-11-01T16:35:58.1407033Z + [[ dynamo_wrapped == distributed ]]
2024-11-01T16:35:58.1407808Z + [[ dynamo_wrapped == *inductor_distributed* ]]
2024-11-01T16:35:58.1408836Z + [[ dynamo_wrapped == *inductor-halide* ]]
2024-11-01T16:35:58.1409771Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]]
2024-11-01T16:35:58.1410725Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]]
2024-11-01T16:35:58.1411276Z + [[ dynamo_wrapped == *huggingface* ]]
2024-11-01T16:35:58.1411735Z + [[ dynamo_wrapped == *timm* ]]
2024-11-01T16:35:58.1412164Z + [[ dynamo_wrapped == *torchbench* ]]
2024-11-01T16:35:58.1412643Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]]
2024-11-01T16:35:58.1413165Z + [[ dynamo_wrapped == *inductor* ]]
2024-11-01T16:35:58.1413623Z + [[ dynamo_wrapped == *dynamo_wrapped* ]]
2024-11-01T16:35:58.1414083Z + install_torchvision
2024-11-01T16:35:58.1414422Z + local orig_preload
2024-11-01T16:35:58.1414763Z + local commit
2024-11-01T16:35:58.1415065Z ++ get_pinned_commit vision
2024-11-01T16:35:58.1415466Z ++ cat .github/ci_commit_pins/vision.txt
2024-11-01T16:35:58.1438318Z + commit=d23a6e1664d20707c11781299611436e1f0c104f
2024-11-01T16:35:58.1438920Z + orig_preload=
2024-11-01T16:35:58.1439367Z + '[' -n '' ']'
2024-11-01T16:35:58.1440488Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2024-11-01T16:35:58.1442298Z + pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2024-11-01T16:35:58.5318814Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2024-11-01T16:35:58.5325655Z   Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-wqc6zd_h
2024-11-01T16:35:58.5354042Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-wqc6zd_h
2024-11-01T16:36:00.2343445Z   Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f'
2024-11-01T16:36:00.2366612Z   Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f
2024-11-01T16:36:01.6973173Z   Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f
2024-11-01T16:36:02.0118096Z   Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f
2024-11-01T16:36:04.4642813Z   Preparing metadata (setup.py) ... [?25l- \ done
2024-11-01T16:36:04.4679578Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.26.2)
2024-11-01T16:36:04.4683164Z Requirement already satisfied: torch in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.6.0a0+gitd1aa4ef)
2024-11-01T16:36:04.4688242Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torchvision==0.19.0a0+d23a6e1) (10.3.0)
2024-11-01T16:36:04.4767988Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1)
2024-11-01T16:36:04.4772304Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2)
2024-11-01T16:36:04.4775122Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8)
2024-11-01T16:36:04.4779438Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.4)
2024-11-01T16:36:04.4782877Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0)
2024-11-01T16:36:04.4795452Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (75.1.0)
2024-11-01T16:36:04.4801019Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1)
2024-11-01T16:36:04.4816661Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from sympy==1.13.1->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0)
2024-11-01T16:36:04.4936294Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.12/lib/python3.12/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2)
2024-11-01T16:36:04.5042386Z Building wheels for collected packages: torchvision
2024-11-01T16:37:17.3256667Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done
2024-11-01T16:37:17.3295032Z [?25h  Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp312-cp312-linux_x86_64.whl size=1117184 sha256=07fd7feb000a73db3fa04b30c117132eec4fdcfe91038ca8fe104e59b288b900
2024-11-01T16:37:17.3296904Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/b9/aa/81/39d3509ec629531316195ffac7a7b05ff7603f393064d63ec9
2024-11-01T16:37:17.3332274Z Successfully built torchvision
2024-11-01T16:37:17.4804784Z Installing collected packages: torchvision
2024-11-01T16:37:17.9772000Z Successfully installed torchvision-0.19.0a0+d23a6e1
2024-11-01T16:37:18.1130541Z + '[' -n '' ']'
2024-11-01T16:37:18.1130932Z + test_dynamo_wrapped_shard 1
2024-11-01T16:37:18.1133211Z + [[ -z 3 ]]
2024-11-01T16:37:18.1133603Z + python tools/dynamo/verify_dynamo.py
2024-11-01T16:37:19.5955055Z Python version: 3.12.7
2024-11-01T16:37:19.5955683Z `torch` version: 2.6.0a0+gitd1aa4ef
2024-11-01T16:37:19.5956319Z CUDA version: None
2024-11-01T16:37:19.5956763Z ROCM version: None
2024-11-01T16:37:19.5956972Z 
2024-11-01T16:37:21.0310437Z CUDA not available -- skipping CUDA check on eager backend
2024-11-01T16:37:21.0310929Z 
2024-11-01T16:37:22.1799778Z CUDA not available -- skipping CUDA check on aot_eager backend
2024-11-01T16:37:22.1800290Z 
2024-11-01T16:37:32.7081171Z CUDA not available -- skipping CUDA check on inductor backend
2024-11-01T16:37:32.7081661Z 
2024-11-01T16:37:32.7081833Z All required checks passed
2024-11-01T16:37:33.5556991Z + 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
2024-11-01T16:37:33.6744597Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2024-11-01T16:37:33.6745870Z   import pkg_resources
2024-11-01T16:37:38.1345318Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2024-11-01T16:37:38.1786485Z Ignoring disabled issues:  ['']
2024-11-01T16:37:38.1932126Z Found test times from artifacts
2024-11-01T16:37:38.2507004Z Found test times from artifacts
2024-11-01T16:37:38.2527603Z Running all tests
2024-11-01T16:37:38.2641842Z Running parallel tests on 3 processes
2024-11-01T16:37:38.2647326Z Name: tests to run (est. time: 67.9min)
2024-11-01T16:37:38.2649025Z   Serial tests (34):
2024-11-01T16:37:38.2649765Z     test_cpp_extensions_open_device_registration 1/1
2024-11-01T16:37:38.2650841Z     test_cpp_api_parity 1/1
2024-11-01T16:37:38.2651404Z     test_multiprocessing_spawn 1/1
2024-11-01T16:37:38.2651831Z     test_autocast 1/1
2024-11-01T16:37:38.2652196Z     test_cpp_extensions_jit 1/1
2024-11-01T16:37:38.2652628Z     test_multiprocessing 1/1
2024-11-01T16:37:38.2653062Z     test_native_mha 1/1
2024-11-01T16:37:38.2653655Z     test_sort_and_select 1/1
2024-11-01T16:37:38.2654302Z     nn/test_pooling 1/1
2024-11-01T16:37:38.2654963Z     test_tensor_creation_ops 1/1
2024-11-01T16:37:38.2655657Z     test_mobile_optimizer 1/1
2024-11-01T16:37:38.2656039Z     test_nn 1/2
2024-11-01T16:37:38.2656364Z     test_spectral_ops 1/1
2024-11-01T16:37:38.2656768Z     distributions/test_distributions 1/2
2024-11-01T16:37:38.2657260Z     distributions/test_distributions 2/2
2024-11-01T16:37:38.2657705Z     test_ci_sanity_check_fail 1/1
2024-11-01T16:37:38.2658152Z     test_cpp_extensions_aot_ninja 1/1
2024-11-01T16:37:38.2658610Z     test_cpp_extensions_aot_no_ninja 1/1
2024-11-01T16:37:38.2672398Z     test_namedtuple_return_api 1/1
2024-11-01T16:37:38.2673032Z     test_autograd_fallback 1/1
2024-11-01T16:37:38.2673432Z     test_jit_disabled 1/1
2024-11-01T16:37:38.2673924Z     test_cpp_extensions_mtia_backend 1/1
2024-11-01T16:37:38.2674439Z     test_cpp_extensions_stream_and_event 1/1
2024-11-01T16:37:38.2674909Z     test_tensorexpr 1/1
2024-11-01T16:37:38.2675259Z     test_cuda_trace 1/1
2024-11-01T16:37:38.2675610Z     test_cuda_primary_ctx 1/1
2024-11-01T16:37:38.2676006Z     test_python_dispatch 1/1
2024-11-01T16:37:38.2676387Z     test_cuda_nvml_based_avail 1/1
2024-11-01T16:37:38.2676796Z     test_reductions 1/2
2024-11-01T16:37:38.2677143Z     test_reductions 2/2
2024-11-01T16:37:38.2677480Z     test_overrides 1/1
2024-11-01T16:37:38.2677808Z     doctests 1/1
2024-11-01T16:37:38.2678119Z     test_autoload_disable 1/1
2024-11-01T16:37:38.2678519Z     test_autoload_enable 1/1
2024-11-01T16:37:38.2678905Z   Parallel tests (48):
2024-11-01T16:37:38.2679275Z     dynamo/test_dynamic_shapes 1/1
2024-11-01T16:37:38.2679687Z     dynamo/test_config 1/1
2024-11-01T16:37:38.2680041Z     dynamo/test_interop 1/1
2024-11-01T16:37:38.2680521Z     dynamo/test_after_aot 1/1
2024-11-01T16:37:38.2680919Z     dynamo/test_export_mutations 1/1
2024-11-01T16:37:38.2681644Z     dynamo/test_misc 1/1
2024-11-01T16:37:38.2682002Z     dynamo/test_export 1/1
2024-11-01T16:37:38.2682355Z     dynamo/test_modules 1/1
2024-11-01T16:37:38.2682755Z     dynamo/test_verify_correctness 1/1
2024-11-01T16:37:38.2683207Z     dynamo/test_higher_order_ops 1/1
2024-11-01T16:37:38.2683628Z     dynamo/test_exc 1/1
2024-11-01T16:37:38.2684002Z     dynamo/test_fx_passes_pre_grad 1/1
2024-11-01T16:37:38.2684429Z     dynamo/test_utils 1/1
2024-11-01T16:37:38.2684770Z     dynamo/test_sdpa 1/1
2024-11-01T16:37:38.2685134Z     dynamo/test_view 1/1
2024-11-01T16:37:38.2685503Z     dynamo/test_profiler 1/1
2024-11-01T16:37:38.2685891Z     dynamo/test_deviceguard 1/1
2024-11-01T16:37:38.2686300Z     dynamo/test_model_output 1/1
2024-11-01T16:37:38.2686759Z     dynamo/test_cudagraphs_expandable_segments 1/1
2024-11-01T16:37:38.2687271Z     dynamo/test_bytecode_utils 1/1
2024-11-01T16:37:38.2687704Z     test_model_exports_to_core_aten 1/1
2024-11-01T16:37:38.2688146Z     test_namedtensor 1/1
2024-11-01T16:37:38.2688554Z     higher_order_ops/test_invoke_subgraph 1/1
2024-11-01T16:37:38.2689054Z     torch_np/numpy_tests/core/test_numeric 1/1
2024-11-01T16:37:38.2689530Z     test_cuda_sanitizer 1/1
2024-11-01T16:37:38.2689958Z     dynamo/test_backward_higher_order_ops 1/1
2024-11-01T16:37:38.2690419Z     test_fx_passes 1/1
2024-11-01T16:37:38.2690768Z     dynamo/test_trace_rules 1/1
2024-11-01T16:37:38.2691183Z     distributions/test_constraints 1/1
2024-11-01T16:37:38.2691633Z     test_fx_reinplace_pass 1/1
2024-11-01T16:37:38.2692056Z     higher_order_ops/test_with_effects 1/1
2024-11-01T16:37:38.2692734Z     torch_np/numpy_tests/lib/test_type_check 1/1
2024-11-01T16:37:38.2693271Z     torch_np/numpy_tests/lib/test_histograms 1/1
2024-11-01T16:37:38.2693763Z     dynamo/test_recompile_ux 1/1
2024-11-01T16:37:38.2694205Z     torch_np/numpy_tests/core/test_indexing 1/1
2024-11-01T16:37:38.2694745Z     torch_np/numpy_tests/lib/test_function_base 1/1
2024-11-01T16:37:38.2695246Z     test_legacy_vmap 1/1
2024-11-01T16:37:38.2695601Z     dynamo/test_hooks 1/1
2024-11-01T16:37:38.2696030Z     torch_np/numpy_tests/core/test_numerictypes 1/1
2024-11-01T16:37:38.2696572Z     torch_np/numpy_tests/lib/test_arraysetops 1/1
2024-11-01T16:37:38.2697062Z     test_cuda_multigpu 1/1
2024-11-01T16:37:38.2697452Z     profiler/test_profiler_tree 1/1
2024-11-01T16:37:38.2697916Z     torch_np/numpy_tests/fft/test_helper 1/1
2024-11-01T16:37:38.2698398Z     torch_np/test_scalars_0D_arrays 1/1
2024-11-01T16:37:38.2698866Z     profiler/test_memory_profiler 1/1
2024-11-01T16:37:38.2699345Z     torch_np/numpy_tests/core/test_scalar_ctors 1/1
2024-11-01T16:37:38.2699898Z     torch_np/numpy_tests/lib/test_arraypad 1/1
2024-11-01T16:37:38.2700366Z     test_dataloader 1/1
2024-11-01T16:37:38.2700737Z Name: excluded (est. time: 0.0min)
2024-11-01T16:37:38.2701148Z   Serial tests (0):
2024-11-01T16:37:38.2701470Z   Parallel tests (0):
2024-11-01T16:37:38.2771036Z Running test_cpp_extensions_open_device_registration 1/1 ... [2024-11-01 16:37:38.276534]
2024-11-01T16:37:38.2772393Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:37:38.2776294Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_open_device_registration.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 16:37:38.277110]
2024-11-01T16:37:46.9079127Z 
2024-11-01T16:37:46.9081398Z test_cpp_extensions_open_device_registration 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_open_device_registration_1.1_333c68a6522ca19a_.log
2024-11-01T16:37:46.9099235Z Running 22 items in this shard: test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_base_device_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_common_registration, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_aliasing, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_compile_autograd_function_returns_self, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_cpu_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_dispatchstub, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_faketensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_generator_registration_and_hooks, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_named_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_numpy_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_packed_sequence, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_quantized, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_random, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_scalar_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_serialization, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_pin_memory, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_resize, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_storage_type, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensor_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRgistration::test_open_device_tensorlist_type_fallback
2024-11-01T16:37:46.9114964Z 
2024-11-01T16:37:46.9115446Z Running test_cpp_api_parity 1/1 ... [2024-11-01 16:37:46.907984]
2024-11-01T16:37:46.9116036Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:37:46.9117873Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 16:37:46.908412]
2024-11-01T16:40:50.4292911Z 
2024-11-01T16:40:50.4295413Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_e21b79fb5a78cf65_.log
2024-11-01T16:40:50.4545795Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_thnn, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_thnn_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_padding, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_padding_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_1x1x1_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_1x1x1_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride_padding, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride_padding_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose3d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose3d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CosineEmbeddingLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CosineEmbeddingLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CosineEmbeddingLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CosineEmbeddingLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CosineEmbeddingLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CosineEmbeddingLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CrossMapLRN2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_CrossMapLRN2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Embedding, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_discontiguous, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_discontiguous_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_max, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_max_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_max_padding_idx, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_max_padding_idx_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_mean_padding_idx, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_mean_padding_idx_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_sparse, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_sparse_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_sum_padding_idx, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_EmbeddingBag_sum_padding_idx_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Embedding_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Embedding_discontiguous, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Embedding_discontiguous_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Embedding_sparse, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Embedding_sparse_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Flatten, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Flatten_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Flatten_no_batch_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Flatten_no_batch_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold_int_input, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold_int_input_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold_no_batch_dim_input, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold_no_batch_dim_input_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold_no_batch_dim_int_input, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Fold_no_batch_dim_int_input_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_HingeEmbeddingLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_HingeEmbeddingLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_HingeEmbeddingLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_HingeEmbeddingLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_HingeEmbeddingLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_HingeEmbeddingLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_LayerNorm_3d_no_affine_large_feature, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_LayerNorm_3d_no_affine_large_feature_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Linear, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Linear_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Linear_no_batch_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Linear_no_batch_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Linear_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Linear_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MarginRankingLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MarginRankingLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MarginRankingLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MarginRankingLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MarginRankingLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MarginRankingLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelMarginLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelMarginLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelMarginLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelMarginLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelMarginLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelMarginLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_NLLLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_NLLLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_NLLLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_NLLLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_NLLLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_NLLLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_broadcast_lhs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_broadcast_lhs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_broadcast_rhs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_broadcast_rhs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_no_batch_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_no_batch_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_with_non_default_args, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PairwiseDistance_with_non_default_args_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PixelShuffle, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PixelShuffle_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PixelUnshuffle, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_PixelUnshuffle_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_RReLU, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_RReLU_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_RReLU_with_up_down, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_RReLU_with_up_down_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_RReLU_with_up_down_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_RReLU_with_up_down_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ReplicationPad3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ReplicationPad3d_complex, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ReplicationPad3d_complex_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ReplicationPad3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ReplicationPad3d_no_batch_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ReplicationPad3d_no_batch_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SampleModule_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SampleModule_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SampleModule_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SampleModule_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SoftMarginLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SoftMarginLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SoftMarginLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SoftMarginLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SoftMarginLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_SoftMarginLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerDecoderLayer_gelu_activation, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerDecoderLayer_gelu_activation_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerDecoderLayer_relu_activation, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerDecoderLayer_relu_activation_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerEncoderLayer_gelu_activation, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerEncoderLayer_gelu_activation_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerEncoderLayer_relu_activation, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TransformerEncoderLayer_relu_activation_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Transformer_multilayer_coder, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Transformer_multilayer_coder_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TripletMarginLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TripletMarginLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TripletMarginLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TripletMarginLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TripletMarginLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_TripletMarginLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Unflatten_no_batch_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Unflatten_no_batch_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Unfold, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Unfold_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Unfold_int_input, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Unfold_int_input_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_no_reduce_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_no_reduce_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_weights_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_weights_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_weights_no_reduce_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCELoss_weights_no_reduce_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCEWithLogitsLoss_legacy_enum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCEWithLogitsLoss_legacy_enum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCEWithLogitsLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_HingeEmbeddingLoss_margin_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_HingeEmbeddingLoss_margin_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_HingeEmbeddingLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_HingeEmbeddingLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_HuberLoss_delta, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_HuberLoss_delta_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce_log_target, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce_log_target_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce_scalar_log_target, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_with_log_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_with_log_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_with_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_KLDivLoss_with_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_L1Loss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_L1Loss_no_reduce_complex, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_L1Loss_no_reduce_complex_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_L1Loss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_L1Loss_no_reduce_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_L1Loss_no_reduce_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MSELoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MSELoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MSELoss_no_reduce_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MSELoss_no_reduce_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_0d_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_1d_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_index_neg, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_index_neg_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelMarginLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelSoftMarginLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_1d_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_1d_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_margin_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_margin_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_p_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_p_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_weights_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_MultiMarginLoss_weights_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss2d_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss2d_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss2d_no_reduce_ignore_index, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss2d_no_reduce_ignore_index_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss2d_no_reduce_weights, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss2d_no_reduce_weights_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLossNd_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLossNd_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLossNd_no_reduce_ignore_index, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLossNd_no_reduce_ignore_index_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLossNd_no_reduce_weights, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLossNd_no_reduce_weights_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_ignore_index, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_ignore_index_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_weights, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_weights_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_PoissonNLLLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_PoissonNLLLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_beta, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_beta_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_no_reduce_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_no_reduce_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_zero_beta, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SmoothL1Loss_zero_beta_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SoftMarginLoss_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_SoftMarginLoss_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda
2024-11-01T16:40:50.4792917Z 
2024-11-01T16:40:50.4793516Z Running test_multiprocessing_spawn 1/1 ... [2024-11-01 16:40:50.429978]
2024-11-01T16:40:50.4794225Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:40:50.4796064Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:40:50.430422]
2024-11-01T16:43:29.3053940Z 
2024-11-01T16:43:29.3055877Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_4bb4a48772ed07e3_.log
2024-11-01T16:43:29.3072925Z 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
2024-11-01T16:43:29.3088582Z 
2024-11-01T16:43:29.3088985Z Running test_autocast 1/1 ... [2024-11-01 16:43:29.305540]
2024-11-01T16:43:29.3089789Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:43:29.3091813Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:43:29.305926]
2024-11-01T16:43:51.7055508Z 
2024-11-01T16:43:51.7057955Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_8dc95c71f147c8df_.log
2024-11-01T16:43:51.7069953Z Running 19 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_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
2024-11-01T16:43:51.7077775Z 
2024-11-01T16:43:51.7078280Z Running test_cpp_extensions_jit 1/1 ... [2024-11-01 16:43:51.705800]
2024-11-01T16:43:51.7078890Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:43:51.7080844Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 16:43:51.706263]
2024-11-01T16:44:31.2351344Z 
2024-11-01T16:44:31.2352927Z test_cpp_extensions_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_jit_1.1_822b78bbea6995cc_.log
2024-11-01T16:44:31.2368173Z Running 27 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_load_with_non_platform_default_encoding, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning
2024-11-01T16:44:31.2382219Z 
2024-11-01T16:44:31.2382668Z Running test_multiprocessing 1/1 ... [2024-11-01 16:44:31.235435]
2024-11-01T16:44:31.2383265Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:44:31.2385073Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:44:31.235854]
2024-11-01T16:45:14.4225502Z 
2024-11-01T16:45:14.4227181Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_fcccfe043cbf4d0b_.log
2024-11-01T16:45:14.4246224Z 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
2024-11-01T16:45:14.4263790Z 
2024-11-01T16:45:14.4264218Z Running test_native_mha 1/1 ... [2024-11-01 16:45:14.422652]
2024-11-01T16:45:14.4264791Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:45:14.4266544Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:45:14.423005]
2024-11-01T16:45:47.9975493Z 
2024-11-01T16:45:47.9976945Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_1ee7d32922407e43_.log
2024-11-01T16:45:48.0008861Z 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
2024-11-01T16:45:48.0036456Z 
2024-11-01T16:45:48.0036930Z Running test_sort_and_select 1/1 ... [2024-11-01 16:45:47.997694]
2024-11-01T16:45:48.0037516Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:45:48.0039290Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:45:47.998136]
2024-11-01T16:46:45.5556228Z 
2024-11-01T16:46:45.5558205Z 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_79eecb1ce35f7db5_.log
2024-11-01T16:46:45.5613304Z Running 110 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_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_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
2024-11-01T16:46:45.5662319Z 
2024-11-01T16:46:45.5662850Z Running nn/test_pooling 1/1 ... [2024-11-01 16:46:45.556214]
2024-11-01T16:46:45.5663405Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:46:45.5665167Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:46:45.556607]
2024-11-01T16:47:57.4624460Z 
2024-11-01T16:47:57.4642431Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_a2335df675fd1fdb_.log
2024-11-01T16:47:57.4695590Z 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
2024-11-01T16:47:57.4745481Z 
2024-11-01T16:47:57.4745960Z Running test_tensor_creation_ops 1/1 ... [2024-11-01 16:47:57.462792]
2024-11-01T16:47:57.4746573Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:47:57.4748374Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_tensor_creation_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'] ... [2024-11-01 16:47:57.463125]
2024-11-01T16:52:25.7035123Z 
2024-11-01T16:52:25.7037244Z test_tensor_creation_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensor_creation_ops_1.1_bfc47e8b5423cdb1_.log
2024-11-01T16:52:25.7343311Z Running 625 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_offset_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ones_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_factories_64bit_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_warning_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_repeat_interleave_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_roll_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_simple_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_storage_filename_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_strided_mismatched_stride_shape_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_ctor_device_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factories_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_copy_var_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_non_writable_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_sequence_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_out_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_std_error_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_inference_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_random_neg_values_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_device_compatibility_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_empty_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_full_like_inference_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_byte_to_int_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_astensor_consistency_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_default_device_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_device_without_index_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_numpy_scalars_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_mult_devices_cpu_float32
2024-11-01T16:52:25.7640907Z 
2024-11-01T16:52:25.7641478Z Running test_mobile_optimizer 1/1 ... [2024-11-01 16:52:25.704411]
2024-11-01T16:52:25.7642219Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:52:25.7644314Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:52:25.704876]
2024-11-01T16:52:35.1349675Z 
2024-11-01T16:52:35.1351391Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_08ff60c25316dcce_.log
2024-11-01T16:52:35.1356862Z 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
2024-11-01T16:52:35.1360069Z 
2024-11-01T16:52:35.1360421Z Running test_nn 1/2 ... [2024-11-01 16:52:35.135114]
2024-11-01T16:52:35.1360936Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T16:52:35.1362645Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 16:52:35.135532]
2024-11-01T17:00:23.7701827Z 
2024-11-01T17:00:23.7703669Z test_nn 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.2_d224967e91d1399f_.log
2024-11-01T17:00:23.8549635Z Running 1050 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_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
2024-11-01T17:00:23.9261662Z 
2024-11-01T17:00:23.9262253Z Running test_spectral_ops 1/1 ... [2024-11-01 17:00:23.772429]
2024-11-01T17:00:23.9262848Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:00:23.9264631Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:00:23.772810]
2024-11-01T17:01:01.5490666Z 
2024-11-01T17:01:01.5494845Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_48473e46d54742d4_.log
2024-11-01T17:01:01.5617968Z 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
2024-11-01T17:01:01.5737795Z 
2024-11-01T17:01:01.5738431Z Running distributions/test_distributions 1/2 ... [2024-11-01 17:01:01.549661]
2024-11-01T17:01:01.5739108Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:01:01.5741250Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:01:01.550044]
2024-11-01T17:08:15.6590211Z 
2024-11-01T17:08:15.6591932Z distributions/test_distributions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.2_9ce848439eddd43a_.log
2024-11-01T17:08:15.6659716Z 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
2024-11-01T17:08:15.6724221Z 
2024-11-01T17:08:15.6724806Z Running distributions/test_distributions 2/2 ... [2024-11-01 17:08:15.659323]
2024-11-01T17:08:15.6725473Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:08:15.6727515Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:08:15.659742]
2024-11-01T17:16:30.7563005Z 
2024-11-01T17:16:30.7564860Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_47bbaa3120ab56c0_.log
2024-11-01T17:16:30.7615841Z 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
2024-11-01T17:16:30.7663197Z 
2024-11-01T17:16:30.7663742Z Running test_ci_sanity_check_fail 1/1 ... [2024-11-01 17:16:30.756570]
2024-11-01T17:16:30.7664355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:16:30.7666185Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 17:16:30.756988]
2024-11-01T17:16:43.4517556Z Running test_cpp_extensions_aot_ninja 1/1 ... [2024-11-01 17:16:43.451313]
2024-11-01T17:16:46.5958160Z running install
2024-11-01T17:16:46.5969149Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2024-11-01T17:16:46.5971119Z !!
2024-11-01T17:16:46.5971410Z 
2024-11-01T17:16:46.5971775Z         ********************************************************************************
2024-11-01T17:16:46.5972853Z         Please avoid running ``setup.py`` directly.
2024-11-01T17:16:46.5973947Z         Instead, use pypa/build, pypa/installer or other
2024-11-01T17:16:46.5975062Z         standards-based tools.
2024-11-01T17:16:46.5975546Z 
2024-11-01T17:16:46.5976557Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2024-11-01T17:16:46.5978002Z         ********************************************************************************
2024-11-01T17:16:46.5978720Z 
2024-11-01T17:16:46.5978923Z !!
2024-11-01T17:16:46.5979425Z   self.initialize_options()
2024-11-01T17:16:46.6118797Z running build
2024-11-01T17:16:46.6119406Z running build_py
2024-11-01T17:16:46.6203805Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:16:46.6213688Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:16:46.6221540Z running build_ext
2024-11-01T17:16:46.7765802Z building 'torch_test_cpp_extension.cpp' extension
2024-11-01T17:16:46.7766819Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312
2024-11-01T17:16:46.8111917Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja...
2024-11-01T17:16:46.8112947Z Compiling objects...
2024-11-01T17:16:46.8113393Z Using envvar MAX_JOBS (6) as the number of workers...
2024-11-01T17:16:47.7554448Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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
2024-11-01T17:16:47.7659323Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:16:48.1359878Z building 'torch_test_cpp_extension.maia' extension
2024-11-01T17:16:48.1706853Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja...
2024-11-01T17:16:48.1711964Z Compiling objects...
2024-11-01T17:16:48.1712818Z Using envvar MAX_JOBS (6) as the number of workers...
2024-11-01T17:16:48.9687757Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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
2024-11-01T17:16:48.9741651Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:16:49.3105563Z building 'torch_test_cpp_extension.rng' extension
2024-11-01T17:16:49.3465597Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/build.ninja...
2024-11-01T17:16:49.3466655Z Compiling objects...
2024-11-01T17:16:49.3467105Z Using envvar MAX_JOBS (6) as the number of workers...
2024-11-01T17:16:50.3181700Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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
2024-11-01T17:16:50.3235142Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:16:50.6955246Z running install_lib
2024-11-01T17:16:50.7036928Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages
2024-11-01T17:16:50.7040841Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:16:50.7043625Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:16:50.7046138Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:16:50.7081943Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:16:50.7119829Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:16:50.7164453Z byte-compiling ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-312.pyc
2024-11-01T17:16:50.7166924Z running install_egg_info
2024-11-01T17:16:50.7367725Z running egg_info
2024-11-01T17:16:50.7368810Z creating torch_test_cpp_extension.egg-info
2024-11-01T17:16:50.7446700Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2024-11-01T17:16:50.7450189Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2024-11-01T17:16:50.7452119Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2024-11-01T17:16:50.7454166Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2024-11-01T17:16:50.7455510Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:16:50.7535895Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:16:50.7543645Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:16:50.7545548Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info
2024-11-01T17:16:50.7552157Z running install_scripts
2024-11-01T17:16:53.3923784Z running install
2024-11-01T17:16:53.3926082Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2024-11-01T17:16:53.3927191Z !!
2024-11-01T17:16:53.3927367Z 
2024-11-01T17:16:53.3927563Z         ********************************************************************************
2024-11-01T17:16:53.3928158Z         Please avoid running ``setup.py`` directly.
2024-11-01T17:16:53.3928736Z         Instead, use pypa/build, pypa/installer or other
2024-11-01T17:16:53.3929316Z         standards-based tools.
2024-11-01T17:16:53.3929574Z 
2024-11-01T17:16:53.3930099Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2024-11-01T17:16:53.3930897Z         ********************************************************************************
2024-11-01T17:16:53.3931285Z 
2024-11-01T17:16:53.3931380Z !!
2024-11-01T17:16:53.3931669Z   self.initialize_options()
2024-11-01T17:16:53.4074215Z running build
2024-11-01T17:16:53.4074686Z running build_ext
2024-11-01T17:16:53.5354414Z building 'no_python_abi_suffix_test' extension
2024-11-01T17:16:53.5357808Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312
2024-11-01T17:16:53.5722010Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja...
2024-11-01T17:16:53.5723484Z Compiling objects...
2024-11-01T17:16:53.5723930Z Using envvar MAX_JOBS (6) as the number of workers...
2024-11-01T17:16:53.6733843Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -I/opt/conda/envs/py_3.12/include/python3.12 -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-312/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
2024-11-01T17:16:53.6783522Z creating build/lib.linux-x86_64-cpython-312
2024-11-01T17:16:53.6790056Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so
2024-11-01T17:16:53.7515943Z running install_lib
2024-11-01T17:16:53.7604287Z creating install/opt/conda/envs/py_3.12/lib/python3.12/site-packages
2024-11-01T17:16:53.7608826Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages
2024-11-01T17:16:53.7614603Z running install_egg_info
2024-11-01T17:16:53.7798959Z running egg_info
2024-11-01T17:16:53.7799892Z creating no_python_abi_suffix_test.egg-info
2024-11-01T17:16:53.7874638Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2024-11-01T17:16:53.7878372Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2024-11-01T17:16:53.7880287Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2024-11-01T17:16:53.7881761Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2024-11-01T17:16:53.7958799Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2024-11-01T17:16:53.7966636Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2024-11-01T17:16:53.7968503Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info
2024-11-01T17:16:53.7973420Z running install_scripts
2024-11-01T17:16:54.2984180Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:16:54.2987349Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:16:54.298375]
2024-11-01T17:17:01.2984651Z 
2024-11-01T17:17:01.2986642Z 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_3a04cb86d7225945_.log
2024-11-01T17:17:01.2995763Z Running 17 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::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
2024-11-01T17:17:01.3004142Z 
2024-11-01T17:17:01.3004619Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2024-11-01 17:17:01.298657]
2024-11-01T17:17:04.4336849Z running install
2024-11-01T17:17:04.4339577Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2024-11-01T17:17:04.4340716Z !!
2024-11-01T17:17:04.4340897Z 
2024-11-01T17:17:04.4341094Z         ********************************************************************************
2024-11-01T17:17:04.4341676Z         Please avoid running ``setup.py`` directly.
2024-11-01T17:17:04.4342263Z         Instead, use pypa/build, pypa/installer or other
2024-11-01T17:17:04.4342842Z         standards-based tools.
2024-11-01T17:17:04.4343100Z 
2024-11-01T17:17:04.4343641Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2024-11-01T17:17:04.4344743Z         ********************************************************************************
2024-11-01T17:17:04.4345146Z 
2024-11-01T17:17:04.4345244Z !!
2024-11-01T17:17:04.4345525Z   self.initialize_options()
2024-11-01T17:17:04.4483670Z running build
2024-11-01T17:17:04.4484001Z running build_py
2024-11-01T17:17:04.4570760Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:17:04.4572951Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:17:04.4577116Z running build_ext
2024-11-01T17:17:04.5472139Z building 'torch_test_cpp_extension.cpp' extension
2024-11-01T17:17:04.5472923Z creating build/temp.linux-x86_64-cpython-312
2024-11-01T17:17:04.5482105Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:17:05.7218041Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2024-11-01T17:17:05.7219705Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2024-11-01T17:17:05.7221095Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9,
2024-11-01T17:17:05.7221943Z                  from extension.cpp:1:
2024-11-01T17:17:05.7237351Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2024-11-01T17:17:05.7238610Z extension.cpp:45:53:   required from here
2024-11-01T17:17:05.7240658Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2024-11-01T17:17:05.7242706Z  1539 | class class_ : public detail::generic_type {
2024-11-01T17:17:05.7243203Z       |       ^~~~~~
2024-11-01T17:17:05.7245377Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2024-11-01T17:17:05.7247332Z extension.cpp:45:53:   required from here
2024-11-01T17:17:05.7251628Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2024-11-01T17:17:05.7255409Z  1599 |             with_internals([&](internals &internals) {
2024-11-01T17:17:05.7255974Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:17:05.7256891Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2024-11-01T17:17:05.7257723Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:17:05.7258403Z  1601 |                                                       : internals.registered_types_cpp;
2024-11-01T17:17:05.7259046Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:17:05.7259707Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2024-11-01T17:17:05.7260316Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:17:05.7260915Z  1603 |                     = instances[std::type_index(typeid(type))];
2024-11-01T17:17:05.7261502Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:17:05.7261964Z  1604 |             });
2024-11-01T17:17:05.7262303Z       |             ~               
2024-11-01T17:17:05.7268570Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:17:06.2780393Z building 'torch_test_cpp_extension.maia' extension
2024-11-01T17:17:06.2787904Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:17:07.4027686Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:17:07.9175105Z building 'torch_test_cpp_extension.rng' extension
2024-11-01T17:17:07.9182483Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:17:09.2334853Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:17:09.2336470Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:17:09.2337826Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:17:09.2339111Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:17:09.2340585Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:17:09.2341549Z                  from rng_extension.cpp:6:
2024-11-01T17:17:09.2342961Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1119: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:17:09.2344321Z  1119 | # pragma unroll
2024-11-01T17:17:09.2344670Z       | 
2024-11-01T17:17:09.2345582Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1159,
2024-11-01T17:17:09.2347119Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:17:09.2348582Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:17:09.2349894Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:17:09.2351155Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:17:09.2352641Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:17:09.2354481Z                  from rng_extension.cpp:6:
2024-11-01T17:17:09.2355791Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:17:09.2357035Z    59 | #pragma unroll
2024-11-01T17:17:09.2357365Z       | 
2024-11-01T17:17:09.2358469Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:17:09.2359574Z    72 | #pragma unroll
2024-11-01T17:17:09.2359902Z       | 
2024-11-01T17:17:09.2361027Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:17:09.2362135Z    87 | #pragma unroll
2024-11-01T17:17:09.2362463Z       | 
2024-11-01T17:17:09.2363342Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1160,
2024-11-01T17:17:09.2364898Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:17:09.2366358Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:17:09.2367661Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:17:09.2368945Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:17:09.2370570Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:17:09.2371538Z                  from rng_extension.cpp:6:
2024-11-01T17:17:09.2372825Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:17:09.2373966Z   153 | #pragma unroll
2024-11-01T17:17:09.2374295Z       | 
2024-11-01T17:17:09.2379460Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:17:09.7839799Z running install_lib
2024-11-01T17:17:09.7933423Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:17:09.8031377Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:17:09.8125542Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:17:09.8231499Z running install_egg_info
2024-11-01T17:17:09.8438050Z running egg_info
2024-11-01T17:17:09.8513974Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2024-11-01T17:17:09.8517858Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2024-11-01T17:17:09.8534725Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2024-11-01T17:17:09.8547651Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2024-11-01T17:17:09.8640398Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:17:09.8649409Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:17:09.8659322Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it)
2024-11-01T17:17:09.8661478Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info
2024-11-01T17:17:09.8667934Z running install_scripts
2024-11-01T17:17:12.5110665Z running install
2024-11-01T17:17:12.5113049Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2024-11-01T17:17:12.5115049Z !!
2024-11-01T17:17:12.5115315Z 
2024-11-01T17:17:12.5115683Z         ********************************************************************************
2024-11-01T17:17:12.5116757Z         Please avoid running ``setup.py`` directly.
2024-11-01T17:17:12.5117837Z         Instead, use pypa/build, pypa/installer or other
2024-11-01T17:17:12.5118911Z         standards-based tools.
2024-11-01T17:17:12.5119413Z 
2024-11-01T17:17:12.5120398Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2024-11-01T17:17:12.5121879Z         ********************************************************************************
2024-11-01T17:17:12.5122606Z 
2024-11-01T17:17:12.5122790Z !!
2024-11-01T17:17:12.5123303Z   self.initialize_options()
2024-11-01T17:17:12.5263074Z running build
2024-11-01T17:17:12.5263470Z running build_ext
2024-11-01T17:17:12.6521154Z building 'no_python_abi_suffix_test' extension
2024-11-01T17:17:12.6865769Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/build.ninja...
2024-11-01T17:17:12.6866887Z Compiling objects...
2024-11-01T17:17:12.6867362Z Using envvar MAX_JOBS (6) as the number of workers...
2024-11-01T17:17:12.7165788Z ninja: no work to do.
2024-11-01T17:17:12.7225733Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-312/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so
2024-11-01T17:17:12.7979950Z running install_lib
2024-11-01T17:17:12.8066662Z copying build/lib.linux-x86_64-cpython-312/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages
2024-11-01T17:17:12.8072118Z running install_egg_info
2024-11-01T17:17:12.8266247Z running egg_info
2024-11-01T17:17:12.8354895Z writing no_python_abi_suffix_test.egg-info/PKG-INFO
2024-11-01T17:17:12.8359949Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt
2024-11-01T17:17:12.8361933Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt
2024-11-01T17:17:12.8445209Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2024-11-01T17:17:12.8452928Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt'
2024-11-01T17:17:12.8455364Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info' (and everything under it)
2024-11-01T17:17:12.8457406Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/no_python_abi_suffix_test-0.0.0-py3.12.egg-info
2024-11-01T17:17:12.8462574Z running install_scripts
2024-11-01T17:17:13.3489708Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:13.3492819Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:17:13.348923]
2024-11-01T17:17:20.3754635Z 
2024-11-01T17:17:20.3756557Z 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_a68d723780563470_.log
2024-11-01T17:17:20.3765871Z Running 17 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::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
2024-11-01T17:17:20.3774051Z 
2024-11-01T17:17:20.3774484Z Running test_namedtuple_return_api 1/1 ... [2024-11-01 17:17:20.375667]
2024-11-01T17:17:20.3775103Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:20.3776987Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:17:20.376013]
2024-11-01T17:17:26.2997966Z 
2024-11-01T17:17:26.2999674Z 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_e7436ab579af59c1_.log
2024-11-01T17:17:26.3002263Z 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
2024-11-01T17:17:26.3003916Z 
2024-11-01T17:17:26.3004297Z Running test_autograd_fallback 1/1 ... [2024-11-01 17:17:26.299964]
2024-11-01T17:17:26.3004900Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:26.3006911Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_autograd_fallback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 17:17:26.300326]
2024-11-01T17:17:32.4244586Z 
2024-11-01T17:17:32.4246538Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_4959cd20e4031c44_.log
2024-11-01T17:17:32.4263510Z Running 28 items in this shard: test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_warn
2024-11-01T17:17:32.4279060Z 
2024-11-01T17:17:32.4279490Z Running test_jit_disabled 1/1 ... [2024-11-01 17:17:32.424608]
2024-11-01T17:17:32.4280064Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:32.4281829Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:17:32.424961]
2024-11-01T17:17:36.6461141Z 
2024-11-01T17:17:36.6462747Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_d17db7187ee6504d_.log
2024-11-01T17:17:36.6465393Z 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
2024-11-01T17:17:36.6466851Z 
2024-11-01T17:17:36.6467478Z Running test_cpp_extensions_mtia_backend 1/1 ... [2024-11-01 17:17:36.646258]
2024-11-01T17:17:36.6468158Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:36.6470178Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_mtia_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 17:17:36.646596]
2024-11-01T17:17:41.3686172Z 
2024-11-01T17:17:41.3688814Z test_cpp_extensions_mtia_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_mtia_backend_1.1_40609143dc0bf7e4_.log
2024-11-01T17:17:41.3695345Z Running 5 items in this shard: test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_device_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_get_device_module, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_basic, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context_different_device
2024-11-01T17:17:41.3698735Z 
2024-11-01T17:17:41.3699417Z Running test_cpp_extensions_stream_and_event 1/1 ... [2024-11-01 17:17:41.368793]
2024-11-01T17:17:41.3700130Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:41.3702011Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cpp_extensions_stream_and_event.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 17:17:41.369247]
2024-11-01T17:17:45.9411121Z 
2024-11-01T17:17:45.9412950Z test_cpp_extensions_stream_and_event 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_stream_and_event_1.1_49b18495c7022ebf_.log
2024-11-01T17:17:45.9415621Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event
2024-11-01T17:17:45.9416456Z 
2024-11-01T17:17:45.9416839Z Running test_tensorexpr 1/1 ... [2024-11-01 17:17:45.941294]
2024-11-01T17:17:45.9417381Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:45.9419459Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_tensorexpr.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 17:17:45.941670]
2024-11-01T17:17:50.0627878Z 
2024-11-01T17:17:50.0629956Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_b13b6adc7226750b_.log
2024-11-01T17:17:50.0658421Z Running 74 items in this shard: test/test_tensorexpr.py::TestTensorExprFuser::test_add_const_rhs, test/test_tensorexpr.py::TestTensorExprFuser::test_add_sub, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_input_and_module, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_module, test/test_tensorexpr.py::TestTensorExprFuser::test_all_combos, test/test_tensorexpr.py::TestTensorExprFuser::test_alpha, test/test_tensorexpr.py::TestTensorExprFuser::test_binary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_bitwise_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast3, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_2, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_big2, test/test_tensorexpr.py::TestTensorExprFuser::test_cat, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_empty_tensors, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_negative_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_only, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_promote_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_with_constant_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_char, test/test_tensorexpr.py::TestTensorExprFuser::test_chunk, test/test_tensorexpr.py::TestTensorExprFuser::test_clamp, test/test_tensorexpr.py::TestTensorExprFuser::test_constant, test/test_tensorexpr.py::TestTensorExprFuser::test_double, test/test_tensorexpr.py::TestTensorExprFuser::test_double_intrinsics, test/test_tensorexpr.py::TestTensorExprFuser::test_dynamic_shape, test/test_tensorexpr.py::TestTensorExprFuser::test_easy, test/test_tensorexpr.py::TestTensorExprFuser::test_eq, test/test_tensorexpr.py::TestTensorExprFuser::test_exp_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_four_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_ge, test/test_tensorexpr.py::TestTensorExprFuser::test_gt, test/test_tensorexpr.py::TestTensorExprFuser::test_guard_fails, test/test_tensorexpr.py::TestTensorExprFuser::test_half_bn_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_half_gelu, test/test_tensorexpr.py::TestTensorExprFuser::test_int64_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_int_output, test/test_tensorexpr.py::TestTensorExprFuser::test_le, test/test_tensorexpr.py::TestTensorExprFuser::test_loop, test/test_tensorexpr.py::TestTensorExprFuser::test_lt, test/test_tensorexpr.py::TestTensorExprFuser::test_mask, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction2, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1_2, test/test_tensorexpr.py::TestTensorExprFuser::test_multi_rand, test/test_tensorexpr.py::TestTensorExprFuser::test_multioutput, test/test_tensorexpr.py::TestTensorExprFuser::test_multiple_outputs, test/test_tensorexpr.py::TestTensorExprFuser::test_nans, test/test_tensorexpr.py::TestTensorExprFuser::test_ne, test/test_tensorexpr.py::TestTensorExprFuser::test_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_propagated_mem_layout, test/test_tensorexpr.py::TestTensorExprFuser::test_rand_like, test/test_tensorexpr.py::TestTensorExprFuser::test_rank_two, test/test_tensorexpr.py::TestTensorExprFuser::test_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_remainder, test/test_tensorexpr.py::TestTensorExprFuser::test_reps, test/test_tensorexpr.py::TestTensorExprFuser::test_round_2, test/test_tensorexpr.py::TestTensorExprFuser::test_scalar, test/test_tensorexpr.py::TestTensorExprFuser::test_short, test/test_tensorexpr.py::TestTensorExprFuser::test_simple_add, test/test_tensorexpr.py::TestTensorExprFuser::test_sin_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_slice, test/test_tensorexpr.py::TestTensorExprFuser::test_sliced_stride, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cpu, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cuda, test/test_tensorexpr.py::TestTensorExprFuser::test_strided_output_preserved, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg2, test/test_tensorexpr.py::TestTensorExprFuser::test_transpose, test/test_tensorexpr.py::TestTensorExprFuser::test_unary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_unsqueeze, test/test_tensorexpr.py::TestTensorExprFuser::test_where
2024-11-01T17:17:50.0684383Z 
2024-11-01T17:17:50.0684809Z Running test_cuda_trace 1/1 ... [2024-11-01 17:17:50.062974]
2024-11-01T17:17:50.0685371Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:50.0687200Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_trace.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'] ... [2024-11-01 17:17:50.063348]
2024-11-01T17:17:53.9359439Z 
2024-11-01T17:17:53.9361231Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_958069b087edbee5_.log
2024-11-01T17:17:53.9362557Z Running 0 items in this shard:
2024-11-01T17:17:53.9362986Z 
2024-11-01T17:17:53.9363626Z Running test_cuda_primary_ctx 1/1 ... [2024-11-01 17:17:53.936078]
2024-11-01T17:17:53.9364329Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:53.9368318Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:17:53.936476]
2024-11-01T17:17:57.8350897Z 
2024-11-01T17:17:57.8352859Z 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_291d02597d20d75d_.log
2024-11-01T17:17:57.8354473Z Running 0 items in this shard:
2024-11-01T17:17:57.8354764Z 
2024-11-01T17:17:57.8355231Z Running test_python_dispatch 1/1 ... [2024-11-01 17:17:57.835288]
2024-11-01T17:17:57.8356028Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:17:57.8360085Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:17:57.835706]
2024-11-01T17:18:14.0260716Z 
2024-11-01T17:18:14.0262658Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_d6d46100cfacbff4_.log
2024-11-01T17:18:14.0321859Z Running 117 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_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_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
2024-11-01T17:18:14.0378678Z 
2024-11-01T17:18:14.0379213Z Running test_cuda_nvml_based_avail 1/1 ... [2024-11-01 17:18:14.026395]
2024-11-01T17:18:14.0379841Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:18:14.0381801Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:18:14.026727]
2024-11-01T17:18:17.8923875Z 
2024-11-01T17:18:17.8926612Z 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_e6bcba5725a6c58a_.log
2024-11-01T17:18:17.8929045Z Running 0 items in this shard:
2024-11-01T17:18:17.8929325Z 
2024-11-01T17:18:17.8929700Z Running test_reductions 1/2 ... [2024-11-01 17:18:17.892563]
2024-11-01T17:18:17.8930249Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:18:17.8933504Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 17:18:17.893004]
2024-11-01T17:43:21.4346883Z 
2024-11-01T17:43:21.4348615Z test_reductions 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.2_51149d8fa8105d36_.log
2024-11-01T17:43:21.5645211Z Running 2355 items in this shard: test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, 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_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, 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_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_float64, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16, 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_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_bincount_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_int32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_uint8, 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_int64, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amin_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_sum_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_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amin_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_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_count_nonzero_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_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_sum_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_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_norm_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_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_var_unbiased_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_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_std_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_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_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_mean_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_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_amin_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_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_nansum_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_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_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_linalg_vector_norm_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_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_count_nonzero_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_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_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_count_nonzero_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_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_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_amin_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_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_count_nonzero_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_sum_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_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_var_unbiased_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_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_sum_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_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_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_any_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_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_sum_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_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_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_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_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_amin_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_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_argmax_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_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_all_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_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_std_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_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_argmax_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_mean_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_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_any_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_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_amin_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_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_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_all_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_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_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_nanmean_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_var_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_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_var_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_std_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_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_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_amax_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_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_amin_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_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_nansum_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_int32, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_cpu_int64, 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_float32, 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_int8, 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_int8, 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_float64, 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_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_int64, 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_int16, 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_min_cpu_float16, 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_int64, 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_nanmedian_cpu_float16, 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_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_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_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_int16, 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_int16, test/test_reductions.py::TestReductionsCPU::test_dim_reduction_fns_fn_name_sum_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_single__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single__refs_count_nonzero_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_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_count_nonzero_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_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_all_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_argmin_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_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_logsumexp_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_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_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_amin_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_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_var_unbiased_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_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_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_amin_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_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_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_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_var_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_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice__refs_sum_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_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_count_nonzero_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_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_mean_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_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_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_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_uint8, 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_int32, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_count_nonzero_cpu_int16, 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_float32, 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_float32, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_complex64, 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_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_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_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int16, 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_float16, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_linalg_vector_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_bool, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_complex128, 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_int8, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_complex64, 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_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_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_int64, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_uint8, 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_int8, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_bfloat16, 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_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_logcumsumexp_complex_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_float32, 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_int64, test/test_reductions.py::TestReductionsCPU::test_max_elementwise_cpu, 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_out_is_alias_of_return_cpu_float16, 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_int32, 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_elementwise_cpu, test/test_reductions.py::TestReductionsCPU::test_min_mixed_devices_cpu, 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_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_int32, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_int64, 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_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_nan_policy_omit_nanmean_cpu_float16, 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_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_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_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_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_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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_var_cpu_bfloat16, 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_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_linalg_vector_norm_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_logsumexp_cpu_bfloat16, 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_mean_cpu_bfloat16, 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_float32, 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_std_cpu_bfloat16, 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_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_masked_sum_cpu_complex64, 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_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_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_prod_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_complex128, 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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_nansum_complex_cpu_complex128, 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_float64, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_int16, 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_out_dtype_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_nansum_vs_numpy_cpu_float16, 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_float16, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float64, 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_float32, 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_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_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_complex128, 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_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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_count_nonzero_cpu_int8, 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_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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_int16, 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_sum_cpu_bfloat16, 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_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_var_cpu_bfloat16, 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_all_cpu_bfloat16, 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_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_uint8, 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_int16, 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_any_cpu_bfloat16, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmax_cpu_bfloat16, 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_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_float64, 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_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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_count_nonzero_cpu_int8, 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_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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int8, 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_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_float16, 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_int64, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float64, 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_logsumexp_cpu_bfloat16, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int64, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_bfloat16, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_float64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_float64, 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_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float16, 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_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nanmean_cpu_float32, 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_float64, 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_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float16, 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_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_unbiased_cpu_float16, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float16, 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_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_complex64, 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_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_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_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_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_float64, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_complex64, 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_uint8, 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_complex64, 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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_prod_cpu_complex128, 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_complex128, 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_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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_var_cpu_complex64, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amax_cpu_bool, 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_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_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float16, 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_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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_any_cpu_int32, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_bfloat16, 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_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_complex64, 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_linalg_vector_norm_cpu_complex128, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int64, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amin_cpu_uint8, 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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_float16, 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_complex64, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_bfloat16, 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_int32, 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_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_float16, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int8, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_sum_cpu_int64, 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_float32, 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_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nanmean_cpu_float16, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int32, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_std_cpu_complex64, 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_sum_cpu_complex128, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_uint8, 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_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_unbiased_cpu_complex128, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_float16, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_bool, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_uint8, 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_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_linalg_vector_norm_cpu_complex64, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float64, 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_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_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_sum_cpu_complex128, 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_var_cpu_bfloat16, 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_float64, 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_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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_uint8, 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_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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float64, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_bfloat16, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_bfloat16, 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_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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float64, 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_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_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_argmax_cpu_bfloat16, 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_int32, 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_float32, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_float16, 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_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_complex64, 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_int32, 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_float32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_prod_cpu_int32, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_complex64, 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_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float16, 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_int8, 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_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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_nansum_cpu_float32, 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_complex64, 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_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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_complex128, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_int16, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_var_unbiased_cpu_complex128, 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_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_int32, 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_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_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_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_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_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_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_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_int32, 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_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_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float64, 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_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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_uint8, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_complex128, 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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_int8, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amin_cpu_bool, 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_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int16, 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_argmin_cpu_bfloat16, 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_int64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_count_nonzero_cpu_int32, 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_complex64, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amax_cpu_uint8, 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_int32, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float16, 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_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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_uint8, 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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_float32, 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_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_int32, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float16, 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_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_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_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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float32, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_bfloat16, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_complex64, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_unbiased_cpu_float64, 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_float64, 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_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int32, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amax_cpu_int8, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int64, 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_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_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_int8, 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_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float64, 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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_int8, 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_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_float32, 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_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_complex128, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int8, 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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_bool, 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_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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float16, 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_int8, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_count_nonzero_cpu_bfloat16, 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_int32, 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_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_float32, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float16, 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_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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_int16, 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_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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_uint8, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_prod_cpu_float64, 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_std_cpu_float16, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_int64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_mean_cpu_float64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_int16, 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_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_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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_std_unbiased_cpu_complex128, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_var_unbiased_cpu_float64, 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_quantile_backward_cpu, test/test_reductions.py::TestReductionsCPU::test_quantile_cpu_float64, 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_float16, 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_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_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_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_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_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_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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_amin_cpu_int8, 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_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_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_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_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_mean_cpu_float32, 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_float16, 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_int64, 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_float32, 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_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values__refs_sum_cpu_int8, 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_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_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_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_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_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmax_cpu_float32, 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_float64, 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_count_nonzero_cpu_bool, 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_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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amax_cpu_int64, 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_float64, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_argmax_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_int32, 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_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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_mean_cpu_int64, 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_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_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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_std_cpu_int8, 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_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_var_cpu_complex128, 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_int64, 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_float32, 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_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_prod_cpu_bool, 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_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_var_cpu_float16, 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_any_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_prod_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_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_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_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_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_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_masked_std_cpu_complex64, 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_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_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_float32, 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_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_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_masked_amin_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_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_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_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_prod_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_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_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_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_std_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_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_amin_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_argmin_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_prod_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_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_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_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_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_int64, 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_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_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_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_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_float64, 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_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_float32, 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_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_int16, 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_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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_complex128, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amax_cpu_int32, 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_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_complex64, 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_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_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_int16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int64, 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_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_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_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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_amax_cpu_int64, 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_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_float64, 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_argmin_cpu_float16, 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_int16, 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_float16, 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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_prod_cpu_int64, 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_float16, 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_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_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_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_complex64, 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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_var_cpu_uint8, 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_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_float64, 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_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_uint8, 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_uint8, 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_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_int8, 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_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amax_cpu_int8, 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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_amin_cpu_int8, 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_float32, 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_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_float64, 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_float32, 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_int64, 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_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_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_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_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_bool, 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_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amax_cpu_int16, 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_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmax_cpu_int32, 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_float64, 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_float16, 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_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_amin_cpu_int16, 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_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_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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_mean_cpu_int8, 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_int64, 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_float32, 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_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_float64, 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_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_int32, 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_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float64, 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_float16, 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_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float16, 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_uint8, 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_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_complex64, 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_uint8, 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_reference_masked_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_int8, 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_int64, 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_int16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int8, 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_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_bool, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int32, 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_float64, 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_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_float64, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float16, 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_int64, 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_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float64, 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_result_dtype__refs_all_cpu_complex128, 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_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_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_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int8, 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_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amin_cpu_int64, 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_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_any_cpu_uint8, 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_float64, 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_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_mean_cpu_bfloat16, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_prod_cpu_int32, 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_sum_cpu_float16, 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_int8, 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_all_cpu_bfloat16, 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_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_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float64, 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_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int16, 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_any_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float64, 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_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_int16, 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_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmin_cpu_int32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_count_nonzero_cpu_uint8, 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_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int32, 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_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_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int16, 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_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float32, 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_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_float16, 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_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_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_int16, 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_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bfloat16, 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_std_cpu_complex64, 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_int8, 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_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float32, 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_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex64, 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_uint8, 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_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float64, 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_uint8, 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_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8, 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_complex64, 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_complex64, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, 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_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_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_float64, 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_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, 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_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_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_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, 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_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64
2024-11-01T17:43:21.6783354Z 
2024-11-01T17:43:21.6783912Z Running test_reductions 2/2 ... [2024-11-01 17:43:21.438142]
2024-11-01T17:43:21.6784512Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:43:21.6786311Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_reductions.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2024-11-01 17:43:21.438487]
2024-11-01T17:46:34.6723391Z 
2024-11-01T17:46:34.6724874Z test_reductions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_2.2_318c8c82f6ef48af_.log
2024-11-01T17:46:34.7819592Z Running 2252 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_float16, 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_uint8, 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_int64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32, 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_int8, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_bucketization_cpu, 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_int64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_cumprod_integer_upcast_cpu, 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_int8, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amax_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_std_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_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_amax_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_all_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_linalg_vector_norm_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_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_masked_var_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_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_argmax_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_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_nansum_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_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_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_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_amax_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_prod_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_any_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_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_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_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_linalg_vector_norm_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_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_count_nonzero_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_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_mean_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_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_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_sum_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_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_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_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_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_linalg_vector_norm_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_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_var_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_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_count_nonzero_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_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_var_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_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_std_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_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_std_unbiased_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_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_var_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_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_amin_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_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_argmin_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_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_sum_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_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_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_any_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_count_nonzero_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_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_amax_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_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_sum_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_logsumexp_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_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_std_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_mean_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_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_offbounds_masked_prod_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_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_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_int8, 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_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_int64, 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_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_max_cpu_bfloat16, 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_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_int8, 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_int8, 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_int32, 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_float32, 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_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_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_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_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_int8, 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_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_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_float64, 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_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_less_than_64_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_linalg_vector_norm_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_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_keepdim_masked_mean_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_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_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_single_masked_norm_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_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_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_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_all_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_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_masked_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_empty_slice_mean_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_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_mean_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_var_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_any_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_argmin_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_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_std_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_masked_var_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_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_empty_tensor_nonempty_slice_var_cpu, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_all_cpu_float32, 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_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_int16, 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_count_nonzero_cpu_bool, 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_float64, 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_float16, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_identity__refs_prod_cpu_float16, 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_int8, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity__refs_sum_cpu_complex128, 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_int8, test/test_reductions.py::TestReductionsCPU::test_identity_all_cpu_complex128, 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_any_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_bool, 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_int32, test/test_reductions.py::TestReductionsCPU::test_identity_any_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_identity_count_nonzero_cpu_complex64, 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_int64, 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_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_prod_cpu_bfloat16, 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_int32, test/test_reductions.py::TestReductionsCPU::test_identity_masked_prod_cpu_int64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_identity_masked_sum_cpu_int32, 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_int32, test/test_reductions.py::TestReductionsCPU::test_identity_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_identity_prod_cpu_bfloat16, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_identity_sum_cpu_int32, 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_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_logsumexp_cpu_float64, 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_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_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_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_real_values_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_median_real_values_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_min_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_min_max_nan_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_minmax_illegal_dtype_cpu, test/test_reductions.py::TestReductionsCPU::test_mode_cpu_int16, 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_int32, 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_float32, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_omit_nansum_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_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_mean_cpu_bfloat16, 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_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_complex64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate__refs_std_cpu_float16, 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_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_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_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_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_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_complex128, 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_complex128, 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_float64, 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_complex128, 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_complex128, 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_float64, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_nan_policy_propagate_mean_cpu_float64, 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_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_unbiased_cpu_bfloat16, 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_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_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_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nansum_complex_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_nansum_out_dtype_cpu_int16, 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_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_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_float32, 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_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_amax_cpu_float32, 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_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_float64, 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_int64, 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_complex64, 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_int32, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_linalg_vector_norm_cpu_float16, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_prod_cpu_complex64, 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_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_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all__refs_var_cpu_complex128, 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_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_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_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_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_amin_cpu_uint8, 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_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_uint8, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_argmin_cpu_int8, 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_int64, 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_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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_amax_cpu_int64, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_argmin_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_logsumexp_cpu_int16, 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_float16, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_prod_cpu_complex128, 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_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_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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_std_cpu_int16, 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_complex128, 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_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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_masked_var_cpu_float32, 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_float16, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_nansum_cpu_int16, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_prod_cpu_int32, 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_float32, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_all_var_cpu_complex128, 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_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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_amin_cpu_int16, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded__refs_any_cpu_int8, 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_complex128, 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_int64, 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_mean_cpu_float32, 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_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_std_cpu_bfloat16, 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_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_float64, 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_complex128, 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_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_all_cpu_complex128, 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_int32, 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_float16, 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_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_amin_cpu_float32, 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_any_cpu_complex128, 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_int16, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_argmin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_count_nonzero_cpu_float16, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_amax_cpu_int8, 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_int16, 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_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmax_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_argmin_cpu_bfloat16, 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_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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_logsumexp_cpu_int32, 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_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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_prod_cpu_complex64, 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_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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_masked_std_cpu_uint8, 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_int8, 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_float64, 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_nanmean_cpu_bfloat16, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_nansum_cpu_int64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_prod_cpu_float32, 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_complex128, 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_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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_expanded_var_cpu_bfloat16, 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_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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amax_cpu_bool, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_amin_cpu_float16, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_any_cpu_float64, 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_count_nonzero_cpu_bfloat16, 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_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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_linalg_vector_norm_cpu_float32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_prod_cpu_bfloat16, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_std_cpu_float32, 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_complex64, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_all_cpu_int8, 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_int64, 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_uint8, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmax_cpu_int32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_argmin_cpu_int32, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_count_nonzero_cpu_uint8, 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_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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_argmax_cpu_int64, 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_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_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_complex64, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_mean_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_norm_cpu_float64, 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_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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_std_cpu_bfloat16, 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_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_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_sum_cpu_float32, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_masked_var_cpu_bfloat16, 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_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_mean_cpu_float32, 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_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_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_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_prod_cpu_int16, 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_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_std_unbiased_cpu_complex64, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_innermost_sum_cpu_float64, 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_var_cpu_complex64, 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_complex64, 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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_amax_cpu_float64, 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_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_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_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_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_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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_count_nonzero_cpu_int8, 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_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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_prod_cpu_bool, 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_int32, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_std_cpu_float32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost__refs_var_cpu_float32, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_all_cpu_float64, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_amax_cpu_bfloat16, 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_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_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_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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_any_cpu_float32, 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_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_argmin_cpu_float16, 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_int8, 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_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_int64, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_linalg_vector_norm_cpu_float32, 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_amin_cpu_bfloat16, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmax_cpu_int64, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_argmin_cpu_float64, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_logsumexp_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_bfloat16, 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_int8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_mean_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_prod_cpu_bool, 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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_masked_var_cpu_int32, 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_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nanmean_cpu_float16, 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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_nansum_cpu_int32, 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_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_prod_cpu_int32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_std_cpu_float32, 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_sum_cpu_bfloat16, 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_int16, 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_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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_outermost_var_unbiased_cpu_float16, 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_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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_all_cpu_int64, 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_int64, 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_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_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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_any_cpu_int32, 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_bool, 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_int64, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_mean_cpu_float32, 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_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_prod_cpu_float64, 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_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_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_sum_cpu_float64, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed__refs_var_cpu_float32, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_all_cpu_uint8, 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_int32, 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_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_amin_cpu_float32, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmax_cpu_uint8, 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_int64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_argmin_cpu_uint8, 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_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_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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_linalg_vector_norm_cpu_float16, 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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_amin_cpu_int16, 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_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_argmax_cpu_float32, 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_logsumexp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_complex64, 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_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_complex128, 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_int16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_mean_cpu_int8, 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_prod_cpu_float16, 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_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_float32, 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_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_complex64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_sum_cpu_float16, 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_int8, 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_float64, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_masked_var_cpu_int16, 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_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_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_nansum_cpu_float64, 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_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_prod_cpu_float64, 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_unbiased_cpu_bfloat16, 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_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_noncontiguous_transposed_sum_cpu_float16, 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_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_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_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_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_prod_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_quantile_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_quantile_error_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_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_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_complex64, 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_int8, 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_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_amin_cpu_int16, 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_uint8, 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_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_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_mean_cpu_complex64, 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_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_float32, 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_int8, 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_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_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_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_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_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_all_cpu_bool, 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_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_amax_cpu_int64, 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_uint8, 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_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_any_cpu_int16, 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_argmax_cpu_float16, 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_argmin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_argmin_cpu_int16, 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_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_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_int16, 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_float32, 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_int64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_amin_cpu_int8, 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_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_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_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_int16, 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_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_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_int16, 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_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_float32, 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_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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nanmean_cpu_float16, 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_float64, test/test_reductions.py::TestReductionsCPU::test_ref_duplicate_values_nansum_cpu_uint8, 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_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_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_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_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_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_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_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_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_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_argmax_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_prod_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_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_ref_extremal_values_var_cpu_complex64, 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_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_amax_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_argmax_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_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_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_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_std_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_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_masked_argmax_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_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_large_input_2D_mean_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_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_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_count_nonzero_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_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_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_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_float64, 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_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_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_float16, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_amin_cpu_int16, 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_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_count_nonzero_cpu_float32, 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_uint8, 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_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_complex64, 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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_prod_cpu_int64, 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_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_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_bool, 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_int64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_all_cpu_int8, 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_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_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_amin_cpu_int16, 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_float16, 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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_argmin_cpu_int32, 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_complex128, 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_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_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_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_int8, 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_int16, 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_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_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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_masked_mean_cpu_int64, 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_float32, 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_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_complex64, 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_uint8, 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_int64, 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_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_int16, 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_mean_cpu_complex128, 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_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_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_prod_cpu_complex128, 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_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_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_scalar_input_var_cpu_complex128, 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_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_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_int32, 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_int16, 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_uint8, 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_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_count_nonzero_cpu_bool, 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_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_float16, 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_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_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input__refs_std_cpu_complex128, 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_int16, 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_float64, 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_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_all_cpu_uint8, 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_int32, 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_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_amin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_bool, 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_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_any_cpu_int32, 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_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_int64, 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_int16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_argmin_cpu_int32, 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_float32, 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_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_int32, 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_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_argmin_cpu_float64, 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_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_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_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_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_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_float16, 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_int32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_masked_std_cpu_int64, 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_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_var_cpu_float16, 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_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_bool, 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_int8, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_nansum_cpu_uint8, 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_float32, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_prod_cpu_float64, 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_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_complex128, 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_int64, test/test_reductions.py::TestReductionsCPU::test_ref_small_input_sum_cpu_int8, 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_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_amax_cpu_bfloat16, 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_int64, 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_int32, 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_float64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_argmax_cpu_int64, 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_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_mean_cpu_bfloat16, 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_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_mean_cpu_int16, 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_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_prod_cpu_int16, 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_float32, 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_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_sum_cpu_int32, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_reference_masked_masked_var_cpu_int16, 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_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_amax_cpu_uint8, 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_int8, 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_int8, 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_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_int16, 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_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_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_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_float16, 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_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_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_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_sum_cpu_uint8, 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_bool, 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_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_uint8, 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_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_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int32, 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_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_any_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_argmax_cpu_uint8, 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_int64, 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_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_linalg_vector_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float16, 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_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int8, 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_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_bfloat16, 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_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_complex64, 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_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float32, 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_norm_cpu_bfloat16, 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_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex128, 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_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float32, 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_uint8, 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_float64, 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_complex128, 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_int8, 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_float64, 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_nansum_cpu_bfloat16, 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_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8, 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_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16, 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_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_int8, 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_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16, 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_complex64, 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_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_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, 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_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_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_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_float32, 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_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
2024-11-01T17:46:34.8879075Z 
2024-11-01T17:46:34.8879624Z Running test_overrides 1/1 ... [2024-11-01 17:46:34.676117]
2024-11-01T17:46:34.8880210Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:46:34.8881975Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:46:34.676535]
2024-11-01T17:51:16.4414948Z 
2024-11-01T17:51:16.4417602Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_1dee04e21e67b1bf_.log
2024-11-01T17:51:16.5311535Z 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
2024-11-01T17:51:16.6185613Z 
2024-11-01T17:51:16.6186148Z Running doctests 1/1 ... [2024-11-01 17:51:16.444320]
2024-11-01T17:51:16.6187038Z Start doctest_module('/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch')
2024-11-01T17:51:16.6187720Z Listing tests
2024-11-01T17:51:16.8506386Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=431.
2024-11-01T17:51:16.8508632Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.8509524Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2024-11-01T17:51:16.8510099Z 
2024-11-01T17:51:16.8510357Z         This is helpful when you want to visualize data over some
2024-11-01T17:51:16.8511014Z         range of inputs. See below for a plotting example.
2024-11-01T17:51:16.8511401Z 
2024-11-01T17:51:16.8511745Z         Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2024-11-01T17:51:16.8513288Z         inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2024-11-01T17:51:16.8514236Z         this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2024-11-01T17:51:16.8515020Z         G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2024-11-01T17:51:16.8515729Z         the output :math:`G_i` is constructed by expanding :math:`T_i`
2024-11-01T17:51:16.8516322Z         to the result shape.
2024-11-01T17:51:16.8516590Z 
2024-11-01T17:51:16.8516730Z         .. note::
2024-11-01T17:51:16.8517165Z             0D inputs are treated equivalently to 1D inputs of a
2024-11-01T17:51:16.8517707Z             single element.
2024-11-01T17:51:16.8517963Z 
2024-11-01T17:51:16.8518080Z         .. warning::
2024-11-01T17:51:16.8518562Z             `torch.meshgrid(*tensors)` currently has the same behavior
2024-11-01T17:51:16.8519320Z             as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2024-11-01T17:51:16.8519727Z 
2024-11-01T17:51:16.8519961Z             In the future `torch.meshgrid` will transition to
2024-11-01T17:51:16.8520605Z             `indexing='xy'` as the default.
2024-11-01T17:51:16.8520936Z 
2024-11-01T17:51:16.8521197Z             https://github.com/pytorch/pytorch/issues/50276 tracks
2024-11-01T17:51:16.8521982Z             this issue with the goal of migrating to NumPy's behavior.
2024-11-01T17:51:16.8522425Z 
2024-11-01T17:51:16.8522555Z         .. seealso::
2024-11-01T17:51:16.8522767Z 
2024-11-01T17:51:16.8523023Z             :func:`torch.cartesian_prod` has the same effect but it
2024-11-01T17:51:16.8523636Z             collects the data in a tensor of vectors.
2024-11-01T17:51:16.8523985Z 
2024-11-01T17:51:16.8524093Z         Args:
2024-11-01T17:51:16.8524672Z             tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2024-11-01T17:51:16.8525483Z                 treated as tensors of size :math:`(1,)` automatically
2024-11-01T17:51:16.8525899Z 
2024-11-01T17:51:16.8526282Z             indexing: (str, optional): the indexing mode, either "xy"
2024-11-01T17:51:16.8527268Z                 or "ij", defaults to "ij". See warning for future changes.
2024-11-01T17:51:16.8527741Z 
2024-11-01T17:51:16.8528074Z                 If "xy" is selected, the first dimension corresponds
2024-11-01T17:51:16.8529167Z                 to the cardinality of the second input and the second
2024-11-01T17:51:16.8530251Z                 dimension corresponds to the cardinality of the first
2024-11-01T17:51:16.8530988Z                 input.
2024-11-01T17:51:16.8531210Z 
2024-11-01T17:51:16.8531441Z                 If "ij" is selected, the dimensions are in the same
2024-11-01T17:51:16.8532037Z                 order as the cardinality of the inputs.
2024-11-01T17:51:16.8532394Z 
2024-11-01T17:51:16.8532522Z         Returns:
2024-11-01T17:51:16.8532950Z             seq (sequence of Tensors): If the input has :math:`N`
2024-11-01T17:51:16.8533734Z             tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2024-11-01T17:51:16.8534411Z             output will also have :math:`N` tensors, where each tensor
2024-11-01T17:51:16.8535103Z             is of shape :math:`(S_0, ..., S_{N-1})`.
2024-11-01T17:51:16.8535476Z 
2024-11-01T17:51:16.8535598Z         Example::
2024-11-01T17:51:16.8535791Z 
2024-11-01T17:51:16.8535954Z             >>> x = torch.tensor([1, 2, 3])
2024-11-01T17:51:16.8536427Z             >>> y = torch.tensor([4, 5, 6])
2024-11-01T17:51:16.8536736Z 
2024-11-01T17:51:16.8537102Z             Observe the element-wise pairings across the grid, (1, 4),
2024-11-01T17:51:16.8537736Z             (1, 5), ..., (3, 6). This is the same thing as the
2024-11-01T17:51:16.8538246Z             cartesian product.
2024-11-01T17:51:16.8538819Z             >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2024-11-01T17:51:16.8539340Z             >>> grid_x
2024-11-01T17:51:16.8539672Z             tensor([[1, 1, 1],
2024-11-01T17:51:16.8540030Z                     [2, 2, 2],
2024-11-01T17:51:16.8540389Z                     [3, 3, 3]])
2024-11-01T17:51:16.8540757Z             >>> grid_y
2024-11-01T17:51:16.8541210Z             tensor([[4, 5, 6],
2024-11-01T17:51:16.8541578Z                     [4, 5, 6],
2024-11-01T17:51:16.8541928Z                     [4, 5, 6]])
2024-11-01T17:51:16.8542193Z 
2024-11-01T17:51:16.8542434Z             This correspondence can be seen when these grids are
2024-11-01T17:51:16.8542972Z             stacked properly.
2024-11-01T17:51:16.8543516Z             >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2024-11-01T17:51:16.8544178Z             ...             torch.cartesian_prod(x, y))
2024-11-01T17:51:16.8544655Z             True
2024-11-01T17:51:16.8544848Z 
2024-11-01T17:51:16.8545094Z             `torch.meshgrid` is commonly used to produce a grid for
2024-11-01T17:51:16.8545641Z             plotting.
2024-11-01T17:51:16.8546053Z             >>> # xdoctest: +REQUIRES(module:matplotlib)
2024-11-01T17:51:16.8546609Z             >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2024-11-01T17:51:16.8547150Z             >>> import matplotlib.pyplot as plt
2024-11-01T17:51:16.8547760Z             >>> xs = torch.linspace(-5, 5, steps=100)
2024-11-01T17:51:16.8548341Z             >>> ys = torch.linspace(-5, 5, steps=100)
2024-11-01T17:51:16.8548967Z             >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2024-11-01T17:51:16.8549532Z             >>> z = torch.sin(torch.sqrt(x * x + y * y))
2024-11-01T17:51:16.8550115Z             >>> ax = plt.axes(projection='3d')
2024-11-01T17:51:16.8550680Z             >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2024-11-01T17:51:16.8551183Z             >>> plt.show()
2024-11-01T17:51:16.8551428Z 
2024-11-01T17:51:16.8551600Z         .. image:: ../_static/img/meshgrid.png
2024-11-01T17:51:16.8552045Z             :width: 512
2024-11-01T17:51:16.8552274Z 
2024-11-01T17:51:16.8552376Z         
2024-11-01T17:51:16.8552965Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.8553476Z 
2024-11-01T17:51:16.8554604Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=820.
2024-11-01T17:51:16.8555917Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.8557085Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor]
2024-11-01T17:51:16.8557773Z 
2024-11-01T17:51:16.8557997Z     Returns the unique elements of the input tensor.
2024-11-01T17:51:16.8558509Z 
2024-11-01T17:51:16.8558955Z     .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2024-11-01T17:51:16.8559933Z         this function also eliminates non-consecutive duplicate values.
2024-11-01T17:51:16.8560394Z 
2024-11-01T17:51:16.8560731Z     .. note:: Currently in the CUDA implementation and the CPU implementation,
2024-11-01T17:51:16.8561665Z         `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2024-11-01T17:51:16.8562717Z         Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2024-11-01T17:51:16.8563582Z         :func:`torch.unique_consecutive` which avoids the sorting.
2024-11-01T17:51:16.8564001Z 
2024-11-01T17:51:16.8564121Z     Args:
2024-11-01T17:51:16.8564440Z         input (Tensor): the input tensor
2024-11-01T17:51:16.8565074Z         sorted (bool): Whether to sort the unique elements in ascending order
2024-11-01T17:51:16.8565698Z             before returning as output.
2024-11-01T17:51:16.8566316Z         return_inverse (bool): Whether to also return the indices for where
2024-11-01T17:51:16.8567112Z             elements in the original input ended up in the returned unique list.
2024-11-01T17:51:16.8567939Z         return_counts (bool): Whether to also return the counts for each unique
2024-11-01T17:51:16.8568544Z             element.
2024-11-01T17:51:16.8569042Z         dim (int, optional): the dimension to operate upon. If ``None``, the
2024-11-01T17:51:16.8569913Z             unique of the flattened input is returned. Otherwise, each of the
2024-11-01T17:51:16.8570689Z             tensors indexed by the given dimension is treated as one of the
2024-11-01T17:51:16.8571468Z             elements to apply the unique operation upon. See examples for more
2024-11-01T17:51:16.8572093Z             details. Default: ``None``
2024-11-01T17:51:16.8572390Z 
2024-11-01T17:51:16.8572511Z     Returns:
2024-11-01T17:51:16.8573099Z         (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2024-11-01T17:51:16.8573688Z 
2024-11-01T17:51:16.8574084Z             - **output** (*Tensor*): the output list of unique scalar elements.
2024-11-01T17:51:16.8574807Z             - **inverse_indices** (*Tensor*): (optional) if
2024-11-01T17:51:16.8575440Z               :attr:`return_inverse` is True, there will be an additional
2024-11-01T17:51:16.8576169Z               returned tensor (same shape as input) representing the indices
2024-11-01T17:51:16.8576930Z               for where elements in the original input map to in the output;
2024-11-01T17:51:16.8577659Z               otherwise, this function will only return a single tensor.
2024-11-01T17:51:16.8578312Z             - **counts** (*Tensor*): (optional) if
2024-11-01T17:51:16.8578915Z               :attr:`return_counts` is True, there will be an additional
2024-11-01T17:51:16.8579609Z               returned tensor (same shape as output or output.size(dim),
2024-11-01T17:51:16.8580339Z               if dim was specified) representing the number of occurrences
2024-11-01T17:51:16.8580957Z               for each unique value or tensor.
2024-11-01T17:51:16.8581278Z 
2024-11-01T17:51:16.8591609Z     Example::
2024-11-01T17:51:16.8591937Z 
2024-11-01T17:51:16.8592271Z         >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2024-11-01T17:51:16.8592880Z         >>> output
2024-11-01T17:51:16.8593195Z         tensor([1, 2, 3])
2024-11-01T17:51:16.8593419Z 
2024-11-01T17:51:16.8593600Z         >>> output, inverse_indices = torch.unique(
2024-11-01T17:51:16.8594676Z         ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2024-11-01T17:51:16.8595334Z         >>> output
2024-11-01T17:51:16.8595643Z         tensor([1, 2, 3])
2024-11-01T17:51:16.8595993Z         >>> inverse_indices
2024-11-01T17:51:16.8596353Z         tensor([0, 2, 1, 2])
2024-11-01T17:51:16.8596594Z 
2024-11-01T17:51:16.8596957Z         >>> output, inverse_indices = torch.unique(
2024-11-01T17:51:16.8597684Z         ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2024-11-01T17:51:16.8598333Z         >>> output
2024-11-01T17:51:16.8598650Z         tensor([1, 2, 3])
2024-11-01T17:51:16.8598996Z         >>> inverse_indices
2024-11-01T17:51:16.8599341Z         tensor([[0, 2],
2024-11-01T17:51:16.8599668Z                 [1, 2]])
2024-11-01T17:51:16.8599894Z 
2024-11-01T17:51:16.8600019Z         >>> a = torch.tensor([
2024-11-01T17:51:16.8600382Z         ...     [
2024-11-01T17:51:16.8600694Z         ...         [1, 1, 0, 0],
2024-11-01T17:51:16.8601071Z         ...         [1, 1, 0, 0],
2024-11-01T17:51:16.8601430Z         ...         [0, 0, 1, 1],
2024-11-01T17:51:16.8601793Z         ...     ],
2024-11-01T17:51:16.8602086Z         ...     [
2024-11-01T17:51:16.8602391Z         ...         [0, 0, 1, 1],
2024-11-01T17:51:16.8602765Z         ...         [0, 0, 1, 1],
2024-11-01T17:51:16.8603130Z         ...         [1, 1, 1, 1],
2024-11-01T17:51:16.8603492Z         ...     ],
2024-11-01T17:51:16.8603784Z         ...     [
2024-11-01T17:51:16.8604087Z         ...         [1, 1, 0, 0],
2024-11-01T17:51:16.8604456Z         ...         [1, 1, 0, 0],
2024-11-01T17:51:16.8604815Z         ...         [0, 0, 1, 1],
2024-11-01T17:51:16.8605176Z         ...     ],
2024-11-01T17:51:16.8605466Z         ... ])
2024-11-01T17:51:16.8605638Z 
2024-11-01T17:51:16.8605984Z         >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2024-11-01T17:51:16.8607263Z         >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2024-11-01T17:51:16.8607953Z         >>> # each other, so one of them will be removed.
2024-11-01T17:51:16.8608472Z         >>> (a[0, :, :] == a[2, :, :]).all()
2024-11-01T17:51:16.8608901Z         tensor(True)
2024-11-01T17:51:16.8609283Z         >>> a_unique_dim0 = torch.unique(a, dim=0)
2024-11-01T17:51:16.8609747Z         >>> a_unique_dim0
2024-11-01T17:51:16.8610098Z         tensor([[[0, 0, 1, 1],
2024-11-01T17:51:16.8610450Z                  [0, 0, 1, 1],
2024-11-01T17:51:16.8610809Z                  [1, 1, 1, 1]],
2024-11-01T17:51:16.8611177Z                 [[1, 1, 0, 0],
2024-11-01T17:51:16.8611531Z                  [1, 1, 0, 0],
2024-11-01T17:51:16.8611889Z                  [0, 0, 1, 1]]])
2024-11-01T17:51:16.8612140Z 
2024-11-01T17:51:16.8612631Z         >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2024-11-01T17:51:16.8613242Z         >>> # `a_unique_dim0`:
2024-11-01T17:51:16.8613678Z         >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2024-11-01T17:51:16.8614145Z         tensor(True)
2024-11-01T17:51:16.8614530Z         >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2024-11-01T17:51:16.8614996Z         tensor(True)
2024-11-01T17:51:16.8615195Z 
2024-11-01T17:51:16.8615501Z         >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2024-11-01T17:51:16.8616263Z         >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2024-11-01T17:51:16.8616853Z         >>> # them will be removed.
2024-11-01T17:51:16.8617286Z         >>> (a[:, 0, :] == a[:, 1, :]).all()
2024-11-01T17:51:16.8617714Z         tensor(True)
2024-11-01T17:51:16.8618038Z         >>> torch.unique(a, dim=1)
2024-11-01T17:51:16.8618437Z         tensor([[[0, 0, 1, 1],
2024-11-01T17:51:16.8618798Z                  [1, 1, 0, 0]],
2024-11-01T17:51:16.8619167Z                 [[1, 1, 1, 1],
2024-11-01T17:51:16.8619521Z                  [0, 0, 1, 1]],
2024-11-01T17:51:16.8619872Z                 [[0, 0, 1, 1],
2024-11-01T17:51:16.8620234Z                  [1, 1, 0, 0]]])
2024-11-01T17:51:16.8620496Z 
2024-11-01T17:51:16.8620818Z         >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2024-11-01T17:51:16.8621572Z         >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2024-11-01T17:51:16.8622275Z         >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2024-11-01T17:51:16.8623051Z         >>> # sub-tensors will be removed.
2024-11-01T17:51:16.8623506Z         >>> (a[:, :, 0] == a[:, :, 1]).all()
2024-11-01T17:51:16.8623935Z         tensor(True)
2024-11-01T17:51:16.8624286Z         >>> (a[:, :, 2] == a[:, :, 3]).all()
2024-11-01T17:51:16.8624715Z         tensor(True)
2024-11-01T17:51:16.8625057Z         >>> torch.unique(a, dim=2)
2024-11-01T17:51:16.8625451Z         tensor([[[0, 1],
2024-11-01T17:51:16.8625772Z                  [0, 1],
2024-11-01T17:51:16.8626098Z                  [1, 0]],
2024-11-01T17:51:16.8626428Z                 [[1, 0],
2024-11-01T17:51:16.8626760Z                  [1, 0],
2024-11-01T17:51:16.8627082Z                  [1, 1]],
2024-11-01T17:51:16.8627399Z                 [[0, 1],
2024-11-01T17:51:16.8627722Z                  [0, 1],
2024-11-01T17:51:16.8628044Z                  [1, 0]]])
2024-11-01T17:51:16.8628366Z     
2024-11-01T17:51:16.8628945Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.8629463Z 
2024-11-01T17:51:16.8717145Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560.
2024-11-01T17:51:16.8718859Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.8719582Z 
2024-11-01T17:51:16.8719819Z Load a model from a github repo or a local directory.
2024-11-01T17:51:16.8720205Z 
2024-11-01T17:51:16.8720539Z Note: Loading a model is the typical use case, but this can also be used to
2024-11-01T17:51:16.8721346Z for loading other objects such as tokenizers, loss functions, etc.
2024-11-01T17:51:16.8722010Z 
2024-11-01T17:51:16.8722331Z If ``source`` is 'github', ``repo_or_dir`` is expected to be
2024-11-01T17:51:16.8722985Z of the form ``repo_owner/repo_name[:ref]`` with an optional
2024-11-01T17:51:16.8723632Z ref (a tag or a branch).
2024-11-01T17:51:16.8723919Z 
2024-11-01T17:51:16.8724521Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2024-11-01T17:51:16.8725223Z path to a local directory.
2024-11-01T17:51:16.8725479Z 
2024-11-01T17:51:16.8725577Z Args:
2024-11-01T17:51:16.8725968Z     repo_or_dir (str): If ``source`` is 'github',
2024-11-01T17:51:16.8726735Z         this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2024-11-01T17:51:16.8727890Z         an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2024-11-01T17:51:16.8729297Z         the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2024-11-01T17:51:16.8730657Z         If ``source`` is 'local'  then it should be a path to a local directory.
2024-11-01T17:51:16.8731427Z     model (str): the name of a callable (entrypoint) defined in the
2024-11-01T17:51:16.8732068Z         repo/dir's ``hubconf.py``.
2024-11-01T17:51:16.8732639Z     *args (optional): the corresponding args for callable ``model``.
2024-11-01T17:51:16.8733409Z     source (str, optional): 'github' or 'local'. Specifies how
2024-11-01T17:51:16.8734159Z         ``repo_or_dir`` is to be interpreted. Default is 'github'.
2024-11-01T17:51:16.8734895Z     trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2024-11-01T17:51:16.8735713Z         This parameter was introduced in v1.12 and helps ensuring that users
2024-11-01T17:51:16.8736386Z         only run code from repos that they trust.
2024-11-01T17:51:16.8736742Z 
2024-11-01T17:51:16.8737103Z         - If ``False``, a prompt will ask the user whether the repo should
2024-11-01T17:51:16.8737677Z           be trusted.
2024-11-01T17:51:16.8738259Z         - If ``True``, the repo will be added to the trusted list and loaded
2024-11-01T17:51:16.8738893Z           without requiring explicit confirmation.
2024-11-01T17:51:16.8739577Z         - If ``"check"``, the repo will be checked against the list of
2024-11-01T17:51:16.8740304Z           trusted repos in the cache. If it is not present in that list, the
2024-11-01T17:51:16.8741216Z           behaviour will fall back onto the ``trust_repo=False`` option.
2024-11-01T17:51:16.8742029Z         - If ``None``: this will raise a warning, inviting the user to set
2024-11-01T17:51:16.8742753Z           ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2024-11-01T17:51:16.8743488Z           is only present for backward compatibility and will be removed in
2024-11-01T17:51:16.8744067Z           v2.0.
2024-11-01T17:51:16.8744258Z 
2024-11-01T17:51:16.8744567Z         Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2024-11-01T17:51:16.8745349Z     force_reload (bool, optional): whether to force a fresh download of
2024-11-01T17:51:16.8746096Z         the github repo unconditionally. Does not have any effect if
2024-11-01T17:51:16.8746782Z         ``source = 'local'``. Default is ``False``.
2024-11-01T17:51:16.8747416Z     verbose (bool, optional): If ``False``, mute messages about hitting
2024-11-01T17:51:16.8748188Z         local caches. Note that the message about first download cannot be
2024-11-01T17:51:16.8748978Z         muted. Does not have any effect if ``source = 'local'``.
2024-11-01T17:51:16.8749512Z         Default is ``True``.
2024-11-01T17:51:16.8750180Z     skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2024-11-01T17:51:16.8751186Z         specified by the ``github`` argument properly belongs to the repo owner. This will make
2024-11-01T17:51:16.8752253Z         requests to the GitHub API; you can specify a non-default GitHub token by setting the
2024-11-01T17:51:16.8753090Z         ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2024-11-01T17:51:16.8754015Z     **kwargs (optional): the corresponding kwargs for callable ``model``.
2024-11-01T17:51:16.8754504Z 
2024-11-01T17:51:16.8754610Z Returns:
2024-11-01T17:51:16.8755051Z     The output of the ``model`` callable when called with the given
2024-11-01T17:51:16.8755627Z     ``*args`` and ``**kwargs``.
2024-11-01T17:51:16.8755888Z 
2024-11-01T17:51:16.8755997Z Example:
2024-11-01T17:51:16.8756352Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2024-11-01T17:51:16.8756836Z     >>> # from a github repo
2024-11-01T17:51:16.8757214Z     >>> repo = "pytorch/vision"
2024-11-01T17:51:16.8757618Z     >>> model = torch.hub.load(
2024-11-01T17:51:16.8758148Z     ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2024-11-01T17:51:16.8758675Z     ... )
2024-11-01T17:51:16.8758965Z     >>> # from a local directory
2024-11-01T17:51:16.8759410Z     >>> path = "/some/local/path/pytorch/vision"
2024-11-01T17:51:16.8759878Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:16.8760471Z     >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2024-11-01T17:51:16.8760997Z 
2024-11-01T17:51:16.8761430Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.8761955Z 
2024-11-01T17:51:16.8762825Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687.
2024-11-01T17:51:16.8764114Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.8764848Z Download object at the given URL to a local path.
2024-11-01T17:51:16.8765223Z 
2024-11-01T17:51:16.8765327Z     Args:
2024-11-01T17:51:16.8765667Z         url (str): URL of the object to download
2024-11-01T17:51:16.8766437Z         dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2024-11-01T17:51:16.8767466Z         hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2024-11-01T17:51:16.8768228Z             Default: None
2024-11-01T17:51:16.8768814Z         progress (bool, optional): whether or not to display a progress bar to stderr
2024-11-01T17:51:16.8769468Z             Default: True
2024-11-01T17:51:16.8769690Z 
2024-11-01T17:51:16.8769811Z     Example:
2024-11-01T17:51:16.8770183Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2024-11-01T17:51:16.8770771Z         >>> # xdoctest: +REQUIRES(POSIX)
2024-11-01T17:51:16.8771238Z         >>> torch.hub.download_url_to_file(
2024-11-01T17:51:16.8771983Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2024-11-01T17:51:16.8772609Z         ...     "/tmp/temporary_file",
2024-11-01T17:51:16.8773013Z         ... )
2024-11-01T17:51:16.8773181Z 
2024-11-01T17:51:16.8773290Z     
2024-11-01T17:51:16.8773852Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.8774373Z 
2024-11-01T17:51:16.8775217Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812.
2024-11-01T17:51:16.8776524Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.8777262Z Loads the Torch serialized object at the given URL.
2024-11-01T17:51:16.8777636Z 
2024-11-01T17:51:16.8777903Z     If downloaded file is a zip file, it will be automatically
2024-11-01T17:51:16.8778451Z     decompressed.
2024-11-01T17:51:16.8778644Z 
2024-11-01T17:51:16.8779047Z     If the object is already present in `model_dir`, it's deserialized and
2024-11-01T17:51:16.8779630Z     returned.
2024-11-01T17:51:16.8780113Z     The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2024-11-01T17:51:16.8780886Z     ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2024-11-01T17:51:16.8781341Z 
2024-11-01T17:51:16.8781458Z     Args:
2024-11-01T17:51:16.8781793Z         url (str): URL of the object to download
2024-11-01T17:51:16.8782478Z         model_dir (str, optional): directory in which to save the object
2024-11-01T17:51:16.8783444Z         map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2024-11-01T17:51:16.8784484Z         progress (bool, optional): whether or not to display a progress bar to stderr.
2024-11-01T17:51:16.8785146Z             Default: True
2024-11-01T17:51:16.8785873Z         check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2024-11-01T17:51:16.8786932Z             ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2024-11-01T17:51:16.8787763Z             digits of the SHA256 hash of the contents of the file. The hash is used to
2024-11-01T17:51:16.8788541Z             ensure unique names and to verify the contents of the file.
2024-11-01T17:51:16.8789098Z             Default: False
2024-11-01T17:51:16.8789828Z         file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2024-11-01T17:51:16.8790961Z         weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2024-11-01T17:51:16.8791958Z             Recommended for untrusted sources. See :func:`~torch.load` for more details.
2024-11-01T17:51:16.8792494Z 
2024-11-01T17:51:16.8792614Z     Example:
2024-11-01T17:51:16.8792975Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2024-11-01T17:51:16.8793560Z         >>> state_dict = torch.hub.load_state_dict_from_url(
2024-11-01T17:51:16.8794469Z         ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2024-11-01T17:51:16.8795059Z         ... )
2024-11-01T17:51:16.8795227Z 
2024-11-01T17:51:16.8795335Z     
2024-11-01T17:51:16.8795914Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.8796427Z 
2024-11-01T17:51:16.8807973Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=367.
2024-11-01T17:51:16.8809308Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:16.8810177Z Registers the function implementation as the fallback for the given key.
2024-11-01T17:51:16.8810685Z 
2024-11-01T17:51:16.8810994Z         This function only works for a library with global namespace ("_").
2024-11-01T17:51:16.8811484Z 
2024-11-01T17:51:16.8811760Z         Args:
2024-11-01T17:51:16.8812363Z             fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2024-11-01T17:51:16.8813103Z                 to register a fallthrough.
2024-11-01T17:51:16.8813901Z             dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2024-11-01T17:51:16.8814775Z                           the dispatch key that the library was created with.
2024-11-01T17:51:16.8815722Z             with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2024-11-01T17:51:16.8816934Z                          to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2024-11-01T17:51:16.8817615Z 
2024-11-01T17:51:16.8817746Z         Example::
2024-11-01T17:51:16.8818105Z             >>> my_lib = Library("_", "IMPL")
2024-11-01T17:51:16.8818622Z             >>> def fallback_kernel(op, *args, **kwargs):
2024-11-01T17:51:16.8819162Z             >>>     # Handle all autocast ops generically
2024-11-01T17:51:16.8819636Z             >>>     # ...
2024-11-01T17:51:16.8820068Z             >>> my_lib.fallback(fallback_kernel, "Autocast")
2024-11-01T17:51:16.8820547Z         
2024-11-01T17:51:16.8821769Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2024-11-01T17:51:16.8822806Z 
2024-11-01T17:51:16.8822993Z my_lib.fallback(fallback_kernel, "Autocast")
2024-11-01T17:51:16.8823420Z ^
2024-11-01T17:51:16.8880358Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=732.
2024-11-01T17:51:16.8882142Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:16.8882979Z Register a FakeTensor implementation ("fake impl") for this operator.
2024-11-01T17:51:16.8883522Z 
2024-11-01T17:51:16.8883784Z     Also sometimes known as a "meta kernel", "abstract impl".
2024-11-01T17:51:16.8884253Z 
2024-11-01T17:51:16.8884593Z     An "FakeTensor implementation" specifies the behavior of this operator on
2024-11-01T17:51:16.8885495Z     Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2024-11-01T17:51:16.8886333Z     certain properties (sizes/strides/storage_offset/device), it specifies
2024-11-01T17:51:16.8887028Z     what the properties of the output Tensors are.
2024-11-01T17:51:16.8887389Z 
2024-11-01T17:51:16.8887709Z     The FakeTensor implementation has the same signature as the operator.
2024-11-01T17:51:16.8888529Z     It is run for both FakeTensors and meta tensors. To write a FakeTensor
2024-11-01T17:51:16.8889550Z     implementation, assume that all Tensor inputs to the operator are
2024-11-01T17:51:16.8890602Z     regular CPU/CUDA/Meta tensors, but they do not have storage, and
2024-11-01T17:51:16.8891919Z     you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2024-11-01T17:51:16.8892724Z     The FakeTensor implementation must consist of only PyTorch operations
2024-11-01T17:51:16.8893497Z     (and may not directly access the storage or data of any input or
2024-11-01T17:51:16.8894092Z     intermediate Tensors).
2024-11-01T17:51:16.8894346Z 
2024-11-01T17:51:16.8894563Z     This API may be used as a decorator (see examples).
2024-11-01T17:51:16.8894961Z 
2024-11-01T17:51:16.8895153Z     For a detailed guide on custom ops, please see
2024-11-01T17:51:16.8895852Z     https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2024-11-01T17:51:16.8896341Z 
2024-11-01T17:51:16.8896468Z     Examples:
2024-11-01T17:51:16.8896751Z         >>> import torch
2024-11-01T17:51:16.8897109Z         >>> import numpy as np
2024-11-01T17:51:16.8897507Z         >>> from torch import Tensor
2024-11-01T17:51:16.8897904Z         >>>
2024-11-01T17:51:16.8898451Z         >>> # Example 1: an operator without data-dependent output shape
2024-11-01T17:51:16.8899368Z         >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2024-11-01T17:51:16.8900241Z         >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2024-11-01T17:51:16.8900989Z         >>>     raise NotImplementedError("Implementation goes here")
2024-11-01T17:51:16.8901522Z         >>>
2024-11-01T17:51:16.8901934Z         >>> @torch.library.register_fake("mylib::custom_linear")
2024-11-01T17:51:16.8902478Z         >>> def _(x, weight, bias):
2024-11-01T17:51:16.8902898Z         >>>     assert x.dim() == 2
2024-11-01T17:51:16.8903316Z         >>>     assert weight.dim() == 2
2024-11-01T17:51:16.8903765Z         >>>     assert bias.dim() == 1
2024-11-01T17:51:16.8904242Z         >>>     assert x.shape[1] == weight.shape[1]
2024-11-01T17:51:16.8904780Z         >>>     assert weight.shape[0] == bias.shape[0]
2024-11-01T17:51:16.8905310Z         >>>     assert x.device == weight.device
2024-11-01T17:51:16.8905739Z         >>>
2024-11-01T17:51:16.8906064Z         >>>     return (x @ weight.t()) + bias
2024-11-01T17:51:16.8906504Z         >>>
2024-11-01T17:51:16.8907200Z         >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2024-11-01T17:51:16.8907759Z         >>>     x = torch.randn(2, 3)
2024-11-01T17:51:16.8908180Z         >>>     w = torch.randn(3, 3)
2024-11-01T17:51:16.8908601Z         >>>     b = torch.randn(3)
2024-11-01T17:51:16.8909079Z         >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2024-11-01T17:51:16.8909555Z         >>>
2024-11-01T17:51:16.8909852Z         >>> assert y.shape == (2, 3)
2024-11-01T17:51:16.8910236Z         >>>
2024-11-01T17:51:16.8910905Z         >>> # Example 2: an operator with data-dependent output shape
2024-11-01T17:51:16.8911664Z         >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2024-11-01T17:51:16.8912402Z         >>> def custom_nonzero(x: Tensor) -> Tensor:
2024-11-01T17:51:16.8912909Z         >>>     x_np = x.numpy(force=True)
2024-11-01T17:51:16.8913423Z         >>>     res = np.stack(np.nonzero(x_np), axis=1)
2024-11-01T17:51:16.8914063Z         >>>     return torch.tensor(res, device=x.device)
2024-11-01T17:51:16.8914536Z         >>>
2024-11-01T17:51:16.8914949Z         >>> @torch.library.register_fake("mylib::custom_nonzero")
2024-11-01T17:51:16.8915481Z         >>> def _(x):
2024-11-01T17:51:16.8915979Z         >>> # Number of nonzero-elements is data-dependent.
2024-11-01T17:51:16.8916600Z         >>> # Since we cannot peek at the data in an fake impl,
2024-11-01T17:51:16.8917227Z         >>> # we use the ctx object to construct a new symint that
2024-11-01T17:51:16.8917876Z         >>> # represents the data-dependent size.
2024-11-01T17:51:16.8918393Z         >>>     ctx = torch.library.get_ctx()
2024-11-01T17:51:16.8918879Z         >>>     nnz = ctx.new_dynamic_size()
2024-11-01T17:51:16.8919341Z         >>>     shape = [nnz, x.dim()]
2024-11-01T17:51:16.8919834Z         >>>     result = x.new_empty(shape, dtype=torch.int64)
2024-11-01T17:51:16.8920341Z         >>>     return result
2024-11-01T17:51:16.8920700Z         >>>
2024-11-01T17:51:16.8921126Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2024-11-01T17:51:16.8921659Z         >>>
2024-11-01T17:51:16.8921986Z         >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2024-11-01T17:51:16.8922664Z         >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2024-11-01T17:51:16.8923331Z         >>> trace.print_readable()
2024-11-01T17:51:16.8923712Z         >>>
2024-11-01T17:51:16.8924202Z         >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2024-11-01T17:51:16.8924691Z 
2024-11-01T17:51:16.8924801Z     
2024-11-01T17:51:16.8925832Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2024-11-01T17:51:16.8926741Z 
2024-11-01T17:51:16.8926845Z _._ = None
2024-11-01T17:51:16.8927106Z ^
2024-11-01T17:51:16.8928150Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=853.
2024-11-01T17:51:16.8929631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.8930360Z Register a backward formula for this custom op.
2024-11-01T17:51:16.8930718Z 
2024-11-01T17:51:16.8931041Z     In order for an operator to work with autograd, you need to register
2024-11-01T17:51:16.8931650Z     a backward formula:
2024-11-01T17:51:16.8932207Z     1. You must tell us how to compute gradients during the backward pass
2024-11-01T17:51:16.8932865Z     by providing us a "backward" function.
2024-11-01T17:51:16.8933528Z     2. If you need any values from the forward to compute gradients, you can
2024-11-01T17:51:16.8934230Z     use `setup_context` to save values for backward.
2024-11-01T17:51:16.8934597Z 
2024-11-01T17:51:16.8934935Z     ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2024-11-01T17:51:16.8935798Z     - ``grads`` is one or more gradients. The number of gradients matches
2024-11-01T17:51:16.8936423Z     the number of outputs of the operator.
2024-11-01T17:51:16.8937083Z     The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2024-11-01T17:51:16.8937929Z     :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2024-11-01T17:51:16.8938671Z     same as :meth:`torch.autograd.Function.backward`.
2024-11-01T17:51:16.8939059Z 
2024-11-01T17:51:16.8939366Z     ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2024-11-01T17:51:16.8940151Z     Please save quantities needed for backward onto the ``ctx`` object via
2024-11-01T17:51:16.8941071Z     either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2024-11-01T17:51:16.8942091Z     or assigning them as attributes of ``ctx``. If your custom op has
2024-11-01T17:51:16.8943044Z     kwarg-only arguments, we expect the signature of ``setup_context``
2024-11-01T17:51:16.8943983Z     to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2024-11-01T17:51:16.8944859Z 
2024-11-01T17:51:16.8945293Z     Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2024-11-01T17:51:16.8946125Z     they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2024-11-01T17:51:16.8947079Z     not depend on or mutate global state. If you need a non-traceable backward,
2024-11-01T17:51:16.8947932Z     you can make it a separate custom_op that you call inside ``backward_fn``.
2024-11-01T17:51:16.8948454Z 
2024-11-01T17:51:16.8948563Z     Examples:
2024-11-01T17:51:16.8948863Z         >>> import torch
2024-11-01T17:51:16.8949225Z         >>> import numpy as np
2024-11-01T17:51:16.8949629Z         >>> from torch import Tensor
2024-11-01T17:51:16.8950026Z         >>>
2024-11-01T17:51:16.8950473Z         >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2024-11-01T17:51:16.8951154Z         >>> def numpy_sin(x: Tensor) -> Tensor:
2024-11-01T17:51:16.8951625Z         >>>     x_np = x.cpu().numpy()
2024-11-01T17:51:16.8952058Z         >>>     y_np = np.sin(x_np)
2024-11-01T17:51:16.8952567Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2024-11-01T17:51:16.8953048Z         >>>
2024-11-01T17:51:16.8953497Z         >>> def setup_context(ctx, inputs, output) -> Tensor:
2024-11-01T17:51:16.8954104Z         >>>     x, = inputs
2024-11-01T17:51:16.8954493Z         >>>     ctx.save_for_backward(x)
2024-11-01T17:51:16.8954905Z         >>>
2024-11-01T17:51:16.8955189Z         >>> def backward(ctx, grad):
2024-11-01T17:51:16.8955622Z         >>>     x, = ctx.saved_tensors
2024-11-01T17:51:16.8956055Z         >>>     return grad * x.cos()
2024-11-01T17:51:16.8956454Z         >>>
2024-11-01T17:51:16.8956785Z         >>> torch.library.register_autograd(
2024-11-01T17:51:16.8957387Z         ...     "mylib::numpy_sin", backward, setup_context=setup_context
2024-11-01T17:51:16.8957920Z         ... )
2024-11-01T17:51:16.8958194Z         >>>
2024-11-01T17:51:16.8958532Z         >>> x = torch.randn(3, requires_grad=True)
2024-11-01T17:51:16.8959166Z         >>> y = numpy_sin(x)
2024-11-01T17:51:16.8959779Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2024-11-01T17:51:16.8960368Z         >>> assert torch.allclose(grad_x, x.cos())
2024-11-01T17:51:16.8960820Z         >>>
2024-11-01T17:51:16.8961223Z         >>> # Example with a keyword-only arg
2024-11-01T17:51:16.8961844Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2024-11-01T17:51:16.8962590Z         >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2024-11-01T17:51:16.8963118Z         >>>     x_np = x.cpu().numpy()
2024-11-01T17:51:16.8963531Z         >>>     y_np = x_np * val
2024-11-01T17:51:16.8964023Z         >>>     return torch.from_numpy(y_np).to(device=x.device)
2024-11-01T17:51:16.8964522Z         >>>
2024-11-01T17:51:16.8965092Z         >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2024-11-01T17:51:16.8965757Z         >>>     ctx.val = keyword_only_inputs["val"]
2024-11-01T17:51:16.8966207Z         >>>
2024-11-01T17:51:16.8966510Z         >>> def backward(ctx, grad):
2024-11-01T17:51:16.8966943Z         >>>     return grad * ctx.val
2024-11-01T17:51:16.8967343Z         >>>
2024-11-01T17:51:16.8967679Z         >>> torch.library.register_autograd(
2024-11-01T17:51:16.8968260Z         ...     "mylib::numpy_mul", backward, setup_context=setup_context
2024-11-01T17:51:16.8968800Z         ... )
2024-11-01T17:51:16.8969083Z         >>>
2024-11-01T17:51:16.8969426Z         >>> x = torch.randn(3, requires_grad=True)
2024-11-01T17:51:16.8969914Z         >>> y = numpy_mul(x, val=3.14)
2024-11-01T17:51:16.8970579Z         >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2024-11-01T17:51:16.8971242Z         >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2024-11-01T17:51:16.8971673Z 
2024-11-01T17:51:16.8971770Z     
2024-11-01T17:51:16.8972352Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.8972860Z 
2024-11-01T17:51:16.8973743Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1261.
2024-11-01T17:51:16.8975016Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.8975867Z Given an operator and some sample arguments, tests if the operator is
2024-11-01T17:51:16.8976483Z     registered correctly.
2024-11-01T17:51:16.8976715Z 
2024-11-01T17:51:16.8977047Z     That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2024-11-01T17:51:16.8977886Z     custom op, you specified metadata (e.g. mutability info) about the custom op
2024-11-01T17:51:16.8978751Z     and these APIs require that the functions you pass them satisfy certain
2024-11-01T17:51:16.8979586Z     properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2024-11-01T17:51:16.8980294Z     ``opcheck`` tests these metadata and properties.
2024-11-01T17:51:16.8980660Z 
2024-11-01T17:51:16.8980821Z     Concretely, we test the following:
2024-11-01T17:51:16.8981134Z 
2024-11-01T17:51:16.8981464Z     - test_schema: If the schema matches the implementation of
2024-11-01T17:51:16.8982211Z       the operator. For example: if the schema specifies a Tensor is mutated,
2024-11-01T17:51:16.8983004Z       then we check the implementation mutates the Tensor. If the schema
2024-11-01T17:51:16.8983754Z       specifies that we return a new Tensor, then we check that the
2024-11-01T17:51:16.8984507Z       implementation returns a new Tensor (instead of an existing one or
2024-11-01T17:51:16.8985123Z       a view of an existing one).
2024-11-01T17:51:16.8985758Z     - test_autograd_registration: If the operator supports training
2024-11-01T17:51:16.8986475Z       (autograd): we check that its autograd formula is registered via
2024-11-01T17:51:16.8987223Z       torch.library.register_autograd or a manual registration to one
2024-11-01T17:51:16.8988060Z       or more DispatchKey::Autograd keys. Any other DispatchKey-based
2024-11-01T17:51:16.8988851Z       registrations may lead to undefined behavior.
2024-11-01T17:51:16.8989531Z     - test_faketensor: If the operator has a FakeTensor kernel
2024-11-01T17:51:16.8990222Z       (and if it is correct). The FakeTensor kernel is necessary (
2024-11-01T17:51:16.8990989Z       but not sufficient) for the operator to work with PyTorch compilation
2024-11-01T17:51:16.8991774Z       APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2024-11-01T17:51:16.8992588Z       (also sometimes known as a meta kernel) was registered for the
2024-11-01T17:51:16.8993314Z       operator and that it is correct. This test takes the result of
2024-11-01T17:51:16.8994175Z       running the operator on real tensors and the result of running
2024-11-01T17:51:16.8994917Z       the operator on FakeTensors and checks that they have the same
2024-11-01T17:51:16.8995580Z       Tensor metadata (sizes/strides/dtype/device/etc).
2024-11-01T17:51:16.8996318Z     - test_aot_dispatch_dynamic: If the operator has correct behavior
2024-11-01T17:51:16.8997028Z       with PyTorch compilation APIs (torch.compile/export/FX).
2024-11-01T17:51:16.8997762Z       This checks that the outputs (and gradients, if applicable) are the
2024-11-01T17:51:16.8998516Z       same under eager-mode PyTorch and torch.compile.
2024-11-01T17:51:16.8999196Z       This test is a superset of ``test_faketensor`` and is an e2e test;
2024-11-01T17:51:16.8999889Z       other things it tests are that the operator supports
2024-11-01T17:51:16.9000574Z       functionalization and that the backward pass (if it exists) also
2024-11-01T17:51:16.9001233Z       supports FakeTensor and functionalization.
2024-11-01T17:51:16.9001708Z 
2024-11-01T17:51:16.9001994Z     For best results, please call ``opcheck`` multiple times with a
2024-11-01T17:51:16.9002692Z     representative set of inputs. If your operator supports
2024-11-01T17:51:16.9003451Z     autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2024-11-01T17:51:16.9004288Z     if your operator supports multiple devices (e.g. CPU and CUDA), please
2024-11-01T17:51:16.9004995Z     use ``opcheck`` with inputs on all supported devices.
2024-11-01T17:51:16.9005391Z 
2024-11-01T17:51:16.9005495Z     Args:
2024-11-01T17:51:16.9005916Z         op: The operator. Must either be a function decorated with
2024-11-01T17:51:16.9006979Z             :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2024-11-01T17:51:16.9007787Z             found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2024-11-01T17:51:16.9008432Z         args: The args to the operator
2024-11-01T17:51:16.9008888Z         kwargs: The kwargs to the operator
2024-11-01T17:51:16.9009472Z         test_utils: Tests that we should run. Default: all of them.
2024-11-01T17:51:16.9010090Z             Example: ("test_schema", "test_faketensor")
2024-11-01T17:51:16.9010734Z         raise_exception: If we should raise an exception on the first
2024-11-01T17:51:16.9011424Z             error. If False, we will return a dict with information
2024-11-01T17:51:16.9011985Z             on if each test passed or not.
2024-11-01T17:51:16.9012313Z 
2024-11-01T17:51:16.9012432Z     .. warning::
2024-11-01T17:51:16.9012630Z 
2024-11-01T17:51:16.9012951Z         opcheck and :func:`torch.autograd.gradcheck` test different things;
2024-11-01T17:51:16.9013745Z         opcheck tests if your usage of torch.library APIs is correct while
2024-11-01T17:51:16.9014534Z         :func:`torch.autograd.gradcheck` tests if your autograd formula is
2024-11-01T17:51:16.9015310Z         mathematically correct. Use both to test custom ops that support
2024-11-01T17:51:16.9015913Z         gradient computation.
2024-11-01T17:51:16.9016169Z 
2024-11-01T17:51:16.9016273Z     Example:
2024-11-01T17:51:16.9016454Z 
2024-11-01T17:51:16.9016655Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:16.9017307Z         >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2024-11-01T17:51:16.9018055Z         >>> def numpy_add(x: Tensor, y: float) -> Tensor:
2024-11-01T17:51:16.9018785Z         >>>     x_np = x.numpy(force=True)
2024-11-01T17:51:16.9019231Z         >>>     z_np = x_np + y
2024-11-01T17:51:16.9019678Z         >>>     return torch.from_numpy(z_np).to(x.device)
2024-11-01T17:51:16.9020150Z         >>>
2024-11-01T17:51:16.9020452Z         >>> @numpy_sin.register_fake
2024-11-01T17:51:16.9020866Z         >>> def _(x, y):
2024-11-01T17:51:16.9021247Z         >>>     return torch.empty_like(x)
2024-11-01T17:51:16.9021653Z         >>>
2024-11-01T17:51:16.9021991Z         >>> def setup_context(ctx, inputs, output):
2024-11-01T17:51:16.9022465Z         >>>     y, = inputs
2024-11-01T17:51:16.9022817Z         >>>     ctx.y = y
2024-11-01T17:51:16.9023146Z         >>>
2024-11-01T17:51:16.9023432Z         >>> def backward(ctx, grad):
2024-11-01T17:51:16.9023873Z         >>>     return grad * ctx.y, None
2024-11-01T17:51:16.9024284Z         >>>
2024-11-01T17:51:16.9024754Z         >>> numpy_sin.register_autograd(backward, setup_context=setup_context)
2024-11-01T17:51:16.9025342Z         >>>
2024-11-01T17:51:16.9025617Z         >>> sample_inputs = [
2024-11-01T17:51:16.9026007Z         >>>     (torch.randn(3), 3.14),
2024-11-01T17:51:16.9026572Z         >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2024-11-01T17:51:16.9027139Z         >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2024-11-01T17:51:16.9027903Z         >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2024-11-01T17:51:16.9028467Z         >>> ]
2024-11-01T17:51:16.9028731Z         >>>
2024-11-01T17:51:16.9029036Z         >>> for args in sample_inputs:
2024-11-01T17:51:16.9029625Z         >>>     torch.library.opcheck(foo, args)
2024-11-01T17:51:16.9029971Z 
2024-11-01T17:51:16.9030084Z     
2024-11-01T17:51:16.9030660Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.9031170Z 
2024-11-01T17:51:16.9465839Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1171.
2024-11-01T17:51:16.9467186Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:16.9468197Z load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args)
2024-11-01T17:51:16.9468896Z 
2024-11-01T17:51:16.9469146Z     Loads an object saved with :func:`torch.save` from a file.
2024-11-01T17:51:16.9469582Z 
2024-11-01T17:51:16.9470010Z     :func:`torch.load` uses Python's unpickling facilities but treats storages,
2024-11-01T17:51:16.9470851Z     which underlie tensors, specially. They are first deserialized on the
2024-11-01T17:51:16.9471683Z     CPU and are then moved to the device they were saved from. If this fails
2024-11-01T17:51:16.9472630Z     (e.g. because the run time system doesn't have certain devices), an exception
2024-11-01T17:51:16.9473502Z     is raised. However, storages can be dynamically remapped to an alternative
2024-11-01T17:51:16.9474358Z     set of devices using the :attr:`map_location` argument.
2024-11-01T17:51:16.9474772Z 
2024-11-01T17:51:16.9475135Z     If :attr:`map_location` is a callable, it will be called once for each serialized
2024-11-01T17:51:16.9476007Z     storage with two arguments: storage and location. The storage argument
2024-11-01T17:51:16.9476841Z     will be the initial deserialization of the storage, residing on the CPU.
2024-11-01T17:51:16.9477663Z     Each serialized storage has a location tag associated with it which
2024-11-01T17:51:16.9478446Z     identifies the device it was saved from, and this tag is the second
2024-11-01T17:51:16.9479380Z     argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2024-11-01T17:51:16.9480341Z     for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2024-11-01T17:51:16.9481150Z     :attr:`map_location` should return either ``None`` or a storage. If
2024-11-01T17:51:16.9481977Z     :attr:`map_location` returns a storage, it will be used as the final deserialized
2024-11-01T17:51:16.9483158Z     object, already moved to the right device. Otherwise, :func:`torch.load` will
2024-11-01T17:51:16.9484141Z     fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2024-11-01T17:51:16.9484675Z 
2024-11-01T17:51:16.9485044Z     If :attr:`map_location` is a :class:`torch.device` object or a string containing
2024-11-01T17:51:16.9485924Z     a device tag, it indicates the location where all tensors should be loaded.
2024-11-01T17:51:16.9486437Z 
2024-11-01T17:51:16.9486818Z     Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2024-11-01T17:51:16.9487693Z     appearing in the file (keys), to ones that specify where to put the
2024-11-01T17:51:16.9488294Z     storages (values).
2024-11-01T17:51:16.9488524Z 
2024-11-01T17:51:16.9488833Z     User extensions can register their own location tags and tagging and
2024-11-01T17:51:16.9489688Z     deserialization methods using :func:`torch.serialization.register_package`.
2024-11-01T17:51:16.9490227Z 
2024-11-01T17:51:16.9490342Z     Args:
2024-11-01T17:51:16.9491089Z         f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2024-11-01T17:51:16.9492010Z             or a string or os.PathLike object containing a file name
2024-11-01T17:51:16.9492904Z         map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2024-11-01T17:51:16.9493654Z             locations
2024-11-01T17:51:16.9494188Z         pickle_module: module used for unpickling metadata and objects (has to
2024-11-01T17:51:16.9495053Z             match the :attr:`pickle_module` used to serialize file)
2024-11-01T17:51:16.9495761Z         weights_only: Indicates whether unpickler should be restricted to
2024-11-01T17:51:16.9496476Z             loading only tensors, primitive types, dictionaries
2024-11-01T17:51:16.9497208Z             and any types added via :func:`torch.serialization.add_safe_globals`.
2024-11-01T17:51:16.9498207Z         mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory.
2024-11-01T17:51:16.9499369Z             Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2024-11-01T17:51:16.9500560Z             are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2024-11-01T17:51:16.9501903Z             second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2024-11-01T17:51:16.9502938Z             tensor storages from disk to CPU memory in the first step, ``f`` is mmaped.
2024-11-01T17:51:16.9503801Z         pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2024-11-01T17:51:16.9504636Z             :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2024-11-01T17:51:16.9505251Z             :attr:`errors=...`.
2024-11-01T17:51:16.9505513Z 
2024-11-01T17:51:16.9505668Z     .. warning::
2024-11-01T17:51:16.9506176Z         :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2024-11-01T17:51:16.9507266Z         uses ``pickle`` module implicitly, which is known to be insecure.
2024-11-01T17:51:16.9508140Z         It is possible to construct malicious pickle data which will execute arbitrary code
2024-11-01T17:51:16.9509062Z         during unpickling. Never load data that could have come from an untrusted
2024-11-01T17:51:16.9510036Z         source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2024-11-01T17:51:16.9510655Z 
2024-11-01T17:51:16.9510783Z     .. note::
2024-11-01T17:51:16.9511369Z         When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2024-11-01T17:51:16.9512460Z         will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2024-11-01T17:51:16.9513431Z         and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2024-11-01T17:51:16.9514264Z 
2024-11-01T17:51:16.9514376Z     .. note::
2024-11-01T17:51:16.9515029Z         By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2024-11-01T17:51:16.9515969Z         case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2024-11-01T17:51:16.9516743Z         when loading files saved by Python 2 in Python 3.  If this default
2024-11-01T17:51:16.9517601Z         is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2024-11-01T17:51:16.9518644Z         these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2024-11-01T17:51:16.9520099Z         to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2024-11-01T17:51:16.9521524Z         as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2024-11-01T17:51:16.9522290Z 
2024-11-01T17:51:16.9522460Z     Example:
2024-11-01T17:51:16.9522970Z         >>> # xdoctest: +SKIP("undefined filepaths")
2024-11-01T17:51:16.9523839Z         >>> torch.load("tensors.pt", weights_only=True)
2024-11-01T17:51:16.9524744Z         # Load all tensors onto the CPU
2024-11-01T17:51:16.9525887Z         >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True)
2024-11-01T17:51:16.9527236Z         # Load all tensors onto the CPU, using a function
2024-11-01T17:51:16.9528163Z         >>> torch.load(
2024-11-01T17:51:16.9529195Z         ...     "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True
2024-11-01T17:51:16.9530349Z         ... )
2024-11-01T17:51:16.9530962Z         # Load all tensors onto GPU 1
2024-11-01T17:51:16.9531862Z         >>> torch.load(
2024-11-01T17:51:16.9532467Z         ...     "tensors.pt",
2024-11-01T17:51:16.9541135Z         ...     map_location=lambda storage, loc: storage.cuda(1),
2024-11-01T17:51:16.9541694Z         ...     weights_only=True,
2024-11-01T17:51:16.9542278Z         ... )  # type: ignore[attr-defined]
2024-11-01T17:51:16.9542759Z         # Map tensors from GPU 1 to GPU 0
2024-11-01T17:51:16.9543440Z         >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True)
2024-11-01T17:51:16.9544146Z         # Load tensor from io.BytesIO object
2024-11-01T17:51:16.9544844Z         # Loading from a buffer setting weights_only=False, warning this can be unsafe
2024-11-01T17:51:16.9545539Z         >>> with open("tensor.pt", "rb") as f:
2024-11-01T17:51:16.9546030Z         ...     buffer = io.BytesIO(f.read())
2024-11-01T17:51:16.9546518Z         >>> torch.load(buffer, weights_only=False)
2024-11-01T17:51:16.9547175Z         # Load a module with 'ascii' encoding for unpickling
2024-11-01T17:51:16.9547933Z         # Loading from a module setting weights_only=False, warning this can be unsafe
2024-11-01T17:51:16.9548742Z         >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2024-11-01T17:51:16.9549289Z     
2024-11-01T17:51:16.9549868Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:16.9550400Z 
2024-11-01T17:51:17.0797682Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=21.
2024-11-01T17:51:17.0799040Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:17.0799849Z Check if there is an available :ref:`accelerator<accelerators>`.
2024-11-01T17:51:17.0800329Z 
2024-11-01T17:51:17.0800438Z     Returns:
2024-11-01T17:51:17.0801020Z         bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2024-11-01T17:51:17.0801595Z 
2024-11-01T17:51:17.0801759Z     Example::
2024-11-01T17:51:17.0801932Z 
2024-11-01T17:51:17.0802323Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:17.0802979Z     
2024-11-01T17:51:17.0804058Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2024-11-01T17:51:17.0805332Z 
2024-11-01T17:51:17.0805690Z assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:17.0806352Z                                         ^
2024-11-01T17:51:17.0818520Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=110.
2024-11-01T17:51:17.0819884Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:17.0820709Z Wait for all kernels in all streams on the given device to complete.
2024-11-01T17:51:17.0821209Z 
2024-11-01T17:51:17.0821342Z     Args:
2024-11-01T17:51:17.0821952Z         device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2024-11-01T17:51:17.0822920Z             the current :ref:`accelerator<accelerators>` device type. If not given,
2024-11-01T17:51:17.0823706Z             use :func:`torch.accelerator.current_device_idx` by default.
2024-11-01T17:51:17.0824179Z 
2024-11-01T17:51:17.0824781Z     .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2024-11-01T17:51:17.0825443Z 
2024-11-01T17:51:17.0825556Z     Example::
2024-11-01T17:51:17.0825731Z 
2024-11-01T17:51:17.0825942Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:17.0826697Z         >>> assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:17.0827438Z         >>> start_event = torch.Event(enable_timing=True)
2024-11-01T17:51:17.0828183Z         >>> end_event = torch.Event(enable_timing=True)
2024-11-01T17:51:17.0828684Z         >>> start_event.record()
2024-11-01T17:51:17.0829306Z         >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2024-11-01T17:51:17.0829966Z         >>> sum = torch.sum(tensor)
2024-11-01T17:51:17.0830385Z         >>> end_event.record()
2024-11-01T17:51:17.0830801Z         >>> torch.accelerator.synchronize()
2024-11-01T17:51:17.0831384Z         >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2024-11-01T17:51:17.0831889Z     
2024-11-01T17:51:17.0832985Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2024-11-01T17:51:17.0834025Z 
2024-11-01T17:51:17.0834398Z assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:17.0835061Z                                         ^
2024-11-01T17:51:17.2409667Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=345.
2024-11-01T17:51:17.2410968Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:17.2411642Z Retrieves the CUDA runtime API module.
2024-11-01T17:51:17.2412001Z 
2024-11-01T17:51:17.2412007Z 
2024-11-01T17:51:17.2412361Z     This function initializes the CUDA runtime environment if it is not already
2024-11-01T17:51:17.2413221Z     initialized and returns the CUDA runtime API module (_cudart). The CUDA
2024-11-01T17:51:17.2414037Z     runtime API module provides access to various CUDA runtime functions.
2024-11-01T17:51:17.2414518Z 
2024-11-01T17:51:17.2414633Z     Args:
2024-11-01T17:51:17.2414897Z         ``None``
2024-11-01T17:51:17.2415078Z 
2024-11-01T17:51:17.2415178Z     Returns:
2024-11-01T17:51:17.2415552Z         module: The CUDA runtime API module (_cudart).
2024-11-01T17:51:17.2415928Z 
2024-11-01T17:51:17.2416034Z     Raises:
2024-11-01T17:51:17.2416616Z         RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2024-11-01T17:51:17.2417640Z         AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2024-11-01T17:51:17.2418325Z 
2024-11-01T17:51:17.2418520Z     Example of CUDA operations with profiling:
2024-11-01T17:51:17.2418978Z         >>> import torch
2024-11-01T17:51:17.2419400Z         >>> from torch.cuda import cudart, check_error
2024-11-01T17:51:17.2420216Z         >>> import os
2024-11-01T17:51:17.2420535Z         >>>
2024-11-01T17:51:17.2420924Z         >>> os.environ['CUDA_PROFILE'] = '1'
2024-11-01T17:51:17.2421346Z         >>>
2024-11-01T17:51:17.2421710Z         >>> def perform_cuda_operations_with_streams():
2024-11-01T17:51:17.2422241Z         >>>     stream = torch.cuda.Stream()
2024-11-01T17:51:17.2422736Z         >>>     with torch.cuda.stream(stream):
2024-11-01T17:51:17.2423334Z         >>>         x = torch.randn(100, 100, device='cuda')
2024-11-01T17:51:17.2423962Z         >>>         y = torch.randn(100, 100, device='cuda')
2024-11-01T17:51:17.2424446Z         >>>         z = torch.mul(x, y)
2024-11-01T17:51:17.2424854Z         >>>     return z
2024-11-01T17:51:17.2425179Z         >>>
2024-11-01T17:51:17.2425480Z         >>> torch.cuda.synchronize()
2024-11-01T17:51:17.2425969Z         >>> print("====== Start nsys profiling ======")
2024-11-01T17:51:17.2426504Z         >>> check_error(cudart().cudaProfilerStart())
2024-11-01T17:51:17.2427068Z         >>> with torch.autograd.profiler.emit_nvtx():
2024-11-01T17:51:17.2427650Z         >>>     result = perform_cuda_operations_with_streams()
2024-11-01T17:51:17.2428214Z         >>>     print("CUDA operations completed.")
2024-11-01T17:51:17.2428798Z         >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2024-11-01T17:51:17.2429380Z         >>> print("====== End nsys profiling ======")
2024-11-01T17:51:17.2429722Z 
2024-11-01T17:51:17.2430011Z     To run this example and save the profiling information, execute:
2024-11-01T17:51:17.2431203Z         >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2024-11-01T17:51:17.2431887Z 
2024-11-01T17:51:17.2432239Z     This command profiles the CUDA operations in the provided script and saves
2024-11-01T17:51:17.2433038Z     the profiling information to a file named `trace_name.prof`.
2024-11-01T17:51:17.2434002Z     The `--profile-from-start off` option ensures that profiling starts only
2024-11-01T17:51:17.2434735Z     after the `cudaProfilerStart` call in the script.
2024-11-01T17:51:17.2435511Z     The `--csv` and `--print-summary` options format the profiling output as a
2024-11-01T17:51:17.2436200Z     CSV file and print a summary, respectively.
2024-11-01T17:51:17.2436984Z     The `-o` option specifies the output file name, and the `-f` option forces the
2024-11-01T17:51:17.2437723Z     overwrite of the output file if it already exists.
2024-11-01T17:51:17.2438203Z     
2024-11-01T17:51:17.2439432Z Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2024-11-01T17:51:17.2440493Z 
2024-11-01T17:51:17.2441084Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2024-11-01T17:51:17.2441827Z ^
2024-11-01T17:51:17.2544551Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101.
2024-11-01T17:51:17.2545916Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:17.2546461Z 
2024-11-01T17:51:17.2546818Z Append the given callback function to this ``Future``, which will be run
2024-11-01T17:51:17.2547626Z when the ``Future`` is completed.  Multiple callbacks can be added to
2024-11-01T17:51:17.2548406Z the same ``Future``, but the order in which they will be executed cannot
2024-11-01T17:51:17.2549145Z be guaranteed (to enforce a certain order consider chaining:
2024-11-01T17:51:17.2549889Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2024-11-01T17:51:17.2550659Z is the reference to this ``Future``. The callback function can use the
2024-11-01T17:51:17.2551433Z :meth:`value` method to get the value. Note that if this ``Future`` is
2024-11-01T17:51:17.2552212Z already completed, the given callback will be run immediately inline.
2024-11-01T17:51:17.2552891Z 
2024-11-01T17:51:17.2553265Z If the ``Future``'s value contains tensors that reside on GPUs, the
2024-11-01T17:51:17.2554146Z callback might be invoked while the async kernels that are populating
2024-11-01T17:51:17.2555260Z those tensors haven't yet finished executing on the device. However, the
2024-11-01T17:51:17.2556309Z callback will be invoked with some dedicated streams set as current
2024-11-01T17:51:17.2557085Z (fetched from a global pool) which will be synchronized with those
2024-11-01T17:51:17.2557866Z kernels. Hence any operation performed by the callback on these tensors
2024-11-01T17:51:17.2558669Z will be scheduled on the device after the kernels complete. In other
2024-11-01T17:51:17.2559520Z words, as long as the callback doesn't switch streams, it can safely
2024-11-01T17:51:17.2560307Z manipulate the result without any additional synchronization. This is
2024-11-01T17:51:17.2561063Z similar to the non-blocking behavior of :meth:`wait`.
2024-11-01T17:51:17.2561459Z 
2024-11-01T17:51:17.2561762Z Similarly, if the callback returns a value that contains tensors that
2024-11-01T17:51:17.2562543Z reside on a GPU, it can do so even if the kernels that are producing
2024-11-01T17:51:17.2563322Z these tensors are still running on the device, as long as the callback
2024-11-01T17:51:17.2564158Z didn't change streams during its execution. If one wants to change
2024-11-01T17:51:17.2564998Z streams, one must be careful to re-synchronize them with the original
2024-11-01T17:51:17.2565777Z streams, that is, those that were current when the callback was invoked.
2024-11-01T17:51:17.2566269Z 
2024-11-01T17:51:17.2566496Z Args:
2024-11-01T17:51:17.2566941Z     callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2024-11-01T17:51:17.2567556Z                             the only argument.
2024-11-01T17:51:17.2567873Z 
2024-11-01T17:51:17.2567986Z Returns:
2024-11-01T17:51:17.2568395Z     A new ``Future`` object that holds the return value of the
2024-11-01T17:51:17.2569055Z     ``callback`` and will be marked as completed when the given
2024-11-01T17:51:17.2569597Z     ``callback`` finishes.
2024-11-01T17:51:17.2569843Z 
2024-11-01T17:51:17.2570104Z .. note:: Note that if the callback function throws, either
2024-11-01T17:51:17.2570814Z     through the original future being completed with an exception and
2024-11-01T17:51:17.2571561Z     calling ``fut.wait()``, or through other code in the callback, the
2024-11-01T17:51:17.2572304Z     future returned by ``then`` will be marked appropriately with the
2024-11-01T17:51:17.2573021Z     encountered error. However, if this callback later completes
2024-11-01T17:51:17.2573761Z     additional futures, those futures are not marked as completed with
2024-11-01T17:51:17.2574545Z     an error and the user is responsible for handling completion/waiting
2024-11-01T17:51:17.2575171Z     on those futures independently.
2024-11-01T17:51:17.2575461Z 
2024-11-01T17:51:17.2575579Z Example::
2024-11-01T17:51:17.2575949Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2024-11-01T17:51:17.2576442Z     >>> def callback(fut):
2024-11-01T17:51:17.2576876Z     ...     print(f"RPC return value is {fut.wait()}.")
2024-11-01T17:51:17.2577387Z     >>> fut = torch.futures.Future()
2024-11-01T17:51:17.2577933Z     >>> # The inserted callback will print the return value when
2024-11-01T17:51:17.2578517Z     >>> # receiving the response from "worker1"
2024-11-01T17:51:17.2578979Z     >>> cb_fut = fut.then(callback)
2024-11-01T17:51:17.2579396Z     >>> chain_cb_fut = cb_fut.then(
2024-11-01T17:51:17.2579897Z     ...     lambda x : print(f"Chained cb done. {x.wait()}")
2024-11-01T17:51:17.2580390Z     ... )
2024-11-01T17:51:17.2580667Z     >>> fut.set_result(5)
2024-11-01T17:51:17.2581013Z     RPC return value is 5.
2024-11-01T17:51:17.2581381Z     Chained cb done. None
2024-11-01T17:51:17.2581624Z 
2024-11-01T17:51:17.2582064Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:17.2582583Z 
2024-11-01T17:51:17.2583596Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209.
2024-11-01T17:51:17.2584947Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:17.2585487Z 
2024-11-01T17:51:17.2585793Z Set the result for this ``Future``, which will mark this ``Future`` as
2024-11-01T17:51:17.2586584Z completed and trigger all attached callbacks. Note that a ``Future``
2024-11-01T17:51:17.2587211Z cannot be marked completed twice.
2024-11-01T17:51:17.2587487Z 
2024-11-01T17:51:17.2587803Z If the result contains tensors that reside on GPUs, this method can be
2024-11-01T17:51:17.2588576Z called even if the asynchronous kernels that are populating those
2024-11-01T17:51:17.2589421Z tensors haven't yet completed running on the device, provided that the
2024-11-01T17:51:17.2590224Z streams on which those kernels were enqueued are set as the current ones
2024-11-01T17:51:17.2591107Z when this method is called. Put simply, it's safe to call this method
2024-11-01T17:51:17.2591882Z immediately after launching those kernels, without any additional
2024-11-01T17:51:17.2592728Z synchronization, as long as one doesn't change streams in between. This
2024-11-01T17:51:17.2593544Z method will record events on all the relevant current streams and will
2024-11-01T17:51:17.2594428Z use them to ensure proper scheduling for all the consumers of this
2024-11-01T17:51:17.2594998Z ``Future``.
2024-11-01T17:51:17.2595158Z 
2024-11-01T17:51:17.2595268Z Args:
2024-11-01T17:51:17.2595749Z     result (object): the result object of this ``Future``.
2024-11-01T17:51:17.2596140Z 
2024-11-01T17:51:17.2596250Z Example::
2024-11-01T17:51:17.2596624Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2024-11-01T17:51:17.2597122Z     >>> import threading
2024-11-01T17:51:17.2597466Z     >>> import time
2024-11-01T17:51:17.2597809Z     >>> def slow_set_future(fut, value):
2024-11-01T17:51:17.2598226Z     ...     time.sleep(0.5)
2024-11-01T17:51:17.2598603Z     ...     fut.set_result(value)
2024-11-01T17:51:17.2599020Z     >>> fut = torch.futures.Future()
2024-11-01T17:51:17.2599478Z     >>> t = threading.Thread(
2024-11-01T17:51:17.2599863Z     ...     target=slow_set_future,
2024-11-01T17:51:17.2600284Z     ...     args=(fut, torch.ones(2) * 3)
2024-11-01T17:51:17.2600681Z     ... )
2024-11-01T17:51:17.2600953Z     >>> t.start()
2024-11-01T17:51:17.2601262Z     >>> print(fut.wait())
2024-11-01T17:51:17.2601602Z     tensor([3., 3.])
2024-11-01T17:51:17.2601913Z     >>> t.join()
2024-11-01T17:51:17.2602096Z 
2024-11-01T17:51:17.2602535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:17.2603064Z 
2024-11-01T17:51:17.2851389Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202.
2024-11-01T17:51:17.2852891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:17.2853701Z Return the sum of each row of the given sparse tensor.
2024-11-01T17:51:17.2854145Z 
2024-11-01T17:51:17.2854491Z     Returns the sum of each row of the sparse tensor :attr:`input` in the given
2024-11-01T17:51:17.2855288Z     dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2024-11-01T17:51:17.2856054Z     reduce over all of them. When sum over all ``sparse_dim``, this method
2024-11-01T17:51:17.2856760Z     returns a dense tensor instead of a sparse tensor.
2024-11-01T17:51:17.2857132Z 
2024-11-01T17:51:17.2857513Z     All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2024-11-01T17:51:17.2858355Z     tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2024-11-01T17:51:17.2858799Z 
2024-11-01T17:51:17.2859109Z     During backward, only gradients at ``nnz`` locations of :attr:`input`
2024-11-01T17:51:17.2859936Z     will propagate back. Note that the gradients of :attr:`input` is coalesced.
2024-11-01T17:51:17.2860451Z 
2024-11-01T17:51:17.2860837Z     Args:
2024-11-01T17:51:17.2861180Z         input (Tensor): the input sparse tensor
2024-11-01T17:51:17.2861953Z         dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2024-11-01T17:51:17.2862657Z             over all dims.
2024-11-01T17:51:17.2863266Z         dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2024-11-01T17:51:17.2863975Z             Default: dtype of :attr:`input`.
2024-11-01T17:51:17.2864301Z 
2024-11-01T17:51:17.2864441Z     Example::
2024-11-01T17:51:17.2864616Z 
2024-11-01T17:51:17.2864733Z         >>> nnz = 3
2024-11-01T17:51:17.2865045Z         >>> dims = [5, 5, 2, 3]
2024-11-01T17:51:17.2865544Z         >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2024-11-01T17:51:17.2866225Z                            torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2024-11-01T17:51:17.2866848Z         >>> V = torch.randn(nnz, dims[2], dims[3])
2024-11-01T17:51:17.2867324Z         >>> size = torch.Size(dims)
2024-11-01T17:51:17.2867906Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:17.2868445Z         >>> S = torch.sparse_coo_tensor(I, V, size)
2024-11-01T17:51:17.2868907Z         >>> S
2024-11-01T17:51:17.2869227Z         tensor(indices=tensor([[2, 0, 3],
2024-11-01T17:51:17.2869676Z                                [2, 4, 1]]),
2024-11-01T17:51:17.2870250Z                values=tensor([[[-0.6438, -1.6467,  1.4004],
2024-11-01T17:51:17.2870854Z                                [ 0.3411,  0.0918, -0.2312]],
2024-11-01T17:51:17.2871190Z 
2024-11-01T17:51:17.2871637Z                               [[ 0.5348,  0.0634, -2.0494],
2024-11-01T17:51:17.2872222Z                                [-0.7125, -1.0646,  2.1844]],
2024-11-01T17:51:17.2872570Z 
2024-11-01T17:51:17.2872800Z                               [[ 0.1276,  0.1874, -0.6334],
2024-11-01T17:51:17.2873391Z                                [-1.9682, -0.5340,  0.7483]]]),
2024-11-01T17:51:17.2874082Z                size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2024-11-01T17:51:17.2874475Z 
2024-11-01T17:51:17.2874777Z         # when sum over only part of sparse_dims, return a sparse tensor
2024-11-01T17:51:17.2875373Z         >>> torch.sparse.sum(S, [1, 3])
2024-11-01T17:51:17.2875841Z         tensor(indices=tensor([[0, 2, 3]]),
2024-11-01T17:51:17.2876392Z                values=tensor([[-1.4512,  0.4073],
2024-11-01T17:51:17.2876931Z                               [-0.8901,  0.2017],
2024-11-01T17:51:17.2877450Z                               [-0.3183, -1.7539]]),
2024-11-01T17:51:17.2877974Z                size=(5, 2), nnz=3, layout=torch.sparse_coo)
2024-11-01T17:51:17.2878339Z 
2024-11-01T17:51:17.2878562Z         # when sum over all sparse dim, return a dense tensor
2024-11-01T17:51:17.2879098Z         # with summed dims squeezed
2024-11-01T17:51:17.2879535Z         >>> torch.sparse.sum(S, [0, 1, 3])
2024-11-01T17:51:17.2880034Z         tensor([-2.6596, -1.1450])
2024-11-01T17:51:17.2880402Z     
2024-11-01T17:51:17.2880972Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:17.2881498Z 
2024-11-01T17:51:17.9173131Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40.
2024-11-01T17:51:17.9174643Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:17.9175197Z 
2024-11-01T17:51:17.9175522Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2024-11-01T17:51:17.9176314Z maps ``func`` over some dimension of the inputs. Semantically, vmap
2024-11-01T17:51:17.9177167Z pushes the map into PyTorch operations called by ``func``, effectively
2024-11-01T17:51:17.9177796Z vectorizing those operations.
2024-11-01T17:51:17.9178061Z 
2024-11-01T17:51:17.9178391Z vmap is useful for handling batch dimensions: one can write a function
2024-11-01T17:51:17.9179167Z ``func`` that runs on examples and then lift it to a function that can
2024-11-01T17:51:17.9180248Z take batches of examples with ``vmap(func)``. vmap can also be used to
2024-11-01T17:51:17.9180961Z compute batched gradients when composed with autograd.
2024-11-01T17:51:17.9181372Z 
2024-11-01T17:51:17.9181499Z .. note::
2024-11-01T17:51:17.9181927Z     :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2024-11-01T17:51:17.9182605Z     convenience. Use whichever one you'd like.
2024-11-01T17:51:17.9182950Z 
2024-11-01T17:51:17.9183051Z Args:
2024-11-01T17:51:17.9183508Z     func (function): A Python function that takes one or more arguments.
2024-11-01T17:51:17.9184136Z         Must return one or more Tensors.
2024-11-01T17:51:17.9184763Z     in_dims (int or nested structure): Specifies which dimension of the
2024-11-01T17:51:17.9185480Z         inputs should be mapped over. ``in_dims`` should have a
2024-11-01T17:51:17.9186170Z         structure like the inputs. If the ``in_dim`` for a particular
2024-11-01T17:51:17.9186881Z         input is None, then that indicates there is no map dimension.
2024-11-01T17:51:17.9187444Z         Default: 0.
2024-11-01T17:51:17.9187939Z     out_dims (int or Tuple[int]): Specifies where the mapped dimension
2024-11-01T17:51:17.9188682Z         should appear in the outputs. If ``out_dims`` is a Tuple, then
2024-11-01T17:51:17.9189349Z         it should have one element per output. Default: 0.
2024-11-01T17:51:17.9189990Z     randomness (str): Specifies whether the randomness in this
2024-11-01T17:51:17.9190800Z         vmap should be the same or different across batches. If 'different',
2024-11-01T17:51:17.9191657Z         the randomness for each batch will be different. If 'same', the
2024-11-01T17:51:17.9192660Z         randomness will be the same across batches. If 'error', any calls to
2024-11-01T17:51:17.9193703Z         random functions will error. Default: 'error'. WARNING: this flag
2024-11-01T17:51:17.9194587Z         only applies to random PyTorch operations and does not apply to
2024-11-01T17:51:17.9195356Z         Python's random module or numpy randomness.
2024-11-01T17:51:17.9196069Z     chunk_size (None or int): If None (default), apply a single vmap over inputs.
2024-11-01T17:51:17.9196927Z         If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2024-11-01T17:51:17.9197919Z         Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2024-11-01T17:51:17.9198965Z         If you run into memory issues computing the vmap, please try a non-None chunk_size.
2024-11-01T17:51:17.9199532Z 
2024-11-01T17:51:17.9199646Z Returns:
2024-11-01T17:51:17.9200087Z     Returns a new "batched" function. It takes the same inputs as
2024-11-01T17:51:17.9200781Z     ``func``, except each input has an extra dimension at the index
2024-11-01T17:51:17.9201494Z     specified by ``in_dims``. It takes returns the same outputs as
2024-11-01T17:51:17.9202202Z     ``func``, except each output has an extra dimension at the index
2024-11-01T17:51:17.9202775Z     specified by ``out_dims``.
2024-11-01T17:51:17.9203036Z 
2024-11-01T17:51:17.9203155Z .. warning:
2024-11-01T17:51:17.9203695Z     :func:`vmap` works best with functional-style code. Please do not
2024-11-01T17:51:17.9204479Z     perform any side-effects in ``func``, with the exception of
2024-11-01T17:51:17.9205327Z     in-place PyTorch operations. Examples of side-effects include mutating
2024-11-01T17:51:17.9206156Z     Python data structures and assigning values to variables not captured
2024-11-01T17:51:17.9207021Z     in ``func``.
2024-11-01T17:51:17.9207207Z 
2024-11-01T17:51:17.9207571Z One example of using :func:`vmap` is to compute batched dot products. PyTorch
2024-11-01T17:51:17.9208495Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2024-11-01T17:51:17.9209282Z rummaging through docs, use :func:`vmap` to construct a new function.
2024-11-01T17:51:17.9209767Z 
2024-11-01T17:51:17.9210057Z     >>> torch.dot                            # [D], [D] -> []
2024-11-01T17:51:17.9210802Z     >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2024-11-01T17:51:17.9211611Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2024-11-01T17:51:17.9212091Z     >>> batched_dot(x, y)
2024-11-01T17:51:17.9212320Z 
2024-11-01T17:51:17.9212678Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2024-11-01T17:51:17.9213313Z model authoring experience.
2024-11-01T17:51:17.9213579Z 
2024-11-01T17:51:17.9213722Z     >>> batch_size, feature_size = 3, 5
2024-11-01T17:51:17.9214267Z     >>> weights = torch.randn(feature_size, requires_grad=True)
2024-11-01T17:51:17.9214788Z     >>>
2024-11-01T17:51:17.9215071Z     >>> def model(feature_vec):
2024-11-01T17:51:17.9215532Z     >>>     # Very simple linear model with activation
2024-11-01T17:51:17.9216052Z     >>>     return feature_vec.dot(weights).relu()
2024-11-01T17:51:17.9216497Z     >>>
2024-11-01T17:51:17.9216862Z     >>> examples = torch.randn(batch_size, feature_size)
2024-11-01T17:51:17.9217410Z     >>> result = torch.vmap(model)(examples)
2024-11-01T17:51:17.9217741Z 
2024-11-01T17:51:17.9218120Z :func:`vmap` can also help vectorize computations that were previously difficult
2024-11-01T17:51:17.9219061Z or impossible to batch. One example is higher-order gradient computation.
2024-11-01T17:51:17.9219968Z The PyTorch autograd engine computes vjps (vector-Jacobian products).
2024-11-01T17:51:17.9220853Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2024-11-01T17:51:17.9221731Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2024-11-01T17:51:17.9222609Z we can vectorize the whole computation, computing the Jacobian in a single
2024-11-01T17:51:17.9223354Z call to ``autograd.grad``.
2024-11-01T17:51:17.9223593Z 
2024-11-01T17:51:17.9223714Z     >>> # Setup
2024-11-01T17:51:17.9223992Z     >>> N = 5
2024-11-01T17:51:17.9224296Z     >>> f = lambda x: x ** 2
2024-11-01T17:51:17.9224720Z     >>> x = torch.randn(N, requires_grad=True)
2024-11-01T17:51:17.9225167Z     >>> y = f(x)
2024-11-01T17:51:17.9225474Z     >>> I_N = torch.eye(N)
2024-11-01T17:51:17.9225805Z     >>>
2024-11-01T17:51:17.9226086Z     >>> # Sequential approach
2024-11-01T17:51:17.9226649Z     >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2024-11-01T17:51:17.9227284Z     >>>                  for v in I_N.unbind()]
2024-11-01T17:51:17.9227777Z     >>> jacobian = torch.stack(jacobian_rows)
2024-11-01T17:51:17.9228208Z     >>>
2024-11-01T17:51:17.9228512Z     >>> # vectorized gradient computation
2024-11-01T17:51:17.9228945Z     >>> def get_vjp(v):
2024-11-01T17:51:17.9229343Z     >>>     return torch.autograd.grad(y, x, v)
2024-11-01T17:51:17.9229848Z     >>> jacobian = torch.vmap(get_vjp)(I_N)
2024-11-01T17:51:17.9230166Z 
2024-11-01T17:51:17.9230558Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2024-11-01T17:51:17.9231131Z 
2024-11-01T17:51:17.9231416Z     >>> torch.dot                            # [D], [D] -> []
2024-11-01T17:51:17.9232299Z     >>> batched_dot = torch.vmap(torch.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2024-11-01T17:51:17.9233093Z     >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2024-11-01T17:51:17.9233642Z     >>> batched_dot(x, y) # tensor of size [2, 3]
2024-11-01T17:51:17.9234164Z 
2024-11-01T17:51:17.9234536Z If the inputs are not batched along the first dimension, ``in_dims`` specifies
2024-11-01T17:51:17.9235280Z the dimension that each inputs are batched along as
2024-11-01T17:51:17.9235657Z 
2024-11-01T17:51:17.9235939Z     >>> torch.dot                            # [N], [N] -> []
2024-11-01T17:51:17.9236731Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2024-11-01T17:51:17.9237405Z     >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2024-11-01T17:51:17.9238128Z     >>> batched_dot(x, y)   # output is [5] instead of [2] if batched along the 0th dimension
2024-11-01T17:51:17.9238668Z 
2024-11-01T17:51:17.9239059Z If there are multiple inputs each of which is batched along different dimensions,
2024-11-01T17:51:17.9240018Z ``in_dims`` must be a tuple with the batch dimension for each input as
2024-11-01T17:51:17.9240481Z 
2024-11-01T17:51:17.9240776Z     >>> torch.dot                            # [D], [D] -> []
2024-11-01T17:51:17.9241576Z     >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2024-11-01T17:51:17.9242271Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2024-11-01T17:51:17.9243081Z     >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None
2024-11-01T17:51:17.9243636Z 
2024-11-01T17:51:17.9244006Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2024-11-01T17:51:17.9244685Z matching the shape of the input:
2024-11-01T17:51:17.9244961Z 
2024-11-01T17:51:17.9245237Z     >>> f = lambda dict: torch.dot(dict['x'], dict['y'])
2024-11-01T17:51:17.9245764Z     >>> x, y = torch.randn(2, 5), torch.randn(5)
2024-11-01T17:51:17.9246280Z     >>> input = {'x': x, 'y': y}
2024-11-01T17:51:17.9246872Z     >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
2024-11-01T17:51:17.9247428Z     >>> batched_dot(input)
2024-11-01T17:51:17.9247661Z 
2024-11-01T17:51:17.9248075Z By default, the output is batched along the first dimension. However, it can be batched
2024-11-01T17:51:17.9248818Z along any dimension by using ``out_dims``
2024-11-01T17:51:17.9249135Z 
2024-11-01T17:51:17.9249259Z     >>> f = lambda x: x ** 2
2024-11-01T17:51:17.9249637Z     >>> x = torch.randn(2, 5)
2024-11-01T17:51:17.9250068Z     >>> batched_pow = torch.vmap(f, out_dims=1)
2024-11-01T17:51:17.9250533Z     >>> batched_pow(x) # [5, 2]
2024-11-01T17:51:17.9250791Z 
2024-11-01T17:51:17.9251304Z For any function that uses kwargs, the returned function will not batch the kwargs but will
2024-11-01T17:51:17.9252023Z accept kwargs
2024-11-01T17:51:17.9252200Z 
2024-11-01T17:51:17.9252329Z     >>> x = torch.randn([2, 5])
2024-11-01T17:51:17.9252721Z     >>> def fn(x, scale=4.):
2024-11-01T17:51:17.9253089Z     >>>   return x * scale
2024-11-01T17:51:17.9253433Z     >>>
2024-11-01T17:51:17.9253723Z     >>> batched_pow = torch.vmap(fn)
2024-11-01T17:51:17.9254201Z     >>> assert torch.allclose(batched_pow(x), x * 4)
2024-11-01T17:51:17.9254898Z     >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]
2024-11-01T17:51:17.9255413Z 
2024-11-01T17:51:17.9255533Z .. note::
2024-11-01T17:51:17.9256099Z     vmap does not provide general autobatching or handle variable-length
2024-11-01T17:51:17.9256720Z     sequences out of the box.
2024-11-01T17:51:17.9256974Z 
2024-11-01T17:51:17.9257408Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:17.9257921Z 
2024-11-01T17:51:19.2923246Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=17.
2024-11-01T17:51:19.2924749Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.2925655Z Create a custom operator whose implementation is backed by 1+ triton kernels.
2024-11-01T17:51:19.2926208Z 
2024-11-01T17:51:19.2926577Z     Use this instead of :func:`torch.library.custom_op` when the implementation
2024-11-01T17:51:19.2927545Z     consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2024-11-01T17:51:19.2928253Z     custom operators as opaque (:func:`torch.compile` and
2024-11-01T17:51:19.2928996Z     :func:`torch.export.export` will never trace into them), but ``triton_op``
2024-11-01T17:51:19.2930049Z     makes the implementation visible to these subsystems, allowing them
2024-11-01T17:51:19.2930783Z     to optimize the triton kernel(s).
2024-11-01T17:51:19.2931102Z 
2024-11-01T17:51:19.2931673Z     Note that ``fn`` must only consist of calls to PyTorch-understood
2024-11-01T17:51:19.2932823Z     operators and triton kernels. Any triton kernels called inside ``fn``
2024-11-01T17:51:19.2933618Z     must be wrapped in a call to :func:`torch._library.capture_triton``.
2024-11-01T17:51:19.2934109Z 
2024-11-01T17:51:19.2934550Z     Args:
2024-11-01T17:51:19.2935120Z         name (str): A name for the custom op that looks like "{namespace}::{name}",
2024-11-01T17:51:19.2936094Z             e.g. "mylib::my_linear". The name is used as the op's stable identifier
2024-11-01T17:51:19.2936841Z             in PyTorch subsystems (e.g. torch.export, FX graphs).
2024-11-01T17:51:19.2937604Z             To avoid name collisions, please use your project name as the namespace;
2024-11-01T17:51:19.2938658Z             e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2024-11-01T17:51:19.2939549Z         mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2024-11-01T17:51:19.2940481Z             This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2024-11-01T17:51:19.2941393Z             it pessimistically assumes that all inputs to the operator are being mutated.
2024-11-01T17:51:19.2942219Z         schema (None | str): A schema string for the operator. If None
2024-11-01T17:51:19.2943078Z             (recommended) we'll infer a schema for the operator from its type
2024-11-01T17:51:19.2943843Z             annotations. We recommend letting us infer a schema unless you
2024-11-01T17:51:19.2944452Z             have a specific reason not to.
2024-11-01T17:51:19.2945071Z             Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2024-11-01T17:51:19.2945467Z 
2024-11-01T17:51:19.2945581Z     Example::
2024-11-01T17:51:19.2945773Z 
2024-11-01T17:51:19.2945971Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:19.2946473Z         >>> import torch
2024-11-01T17:51:19.2947120Z         >>> from torch._library import triton_op, capture_triton
2024-11-01T17:51:19.2947619Z         >>>
2024-11-01T17:51:19.2947909Z         >>> import triton
2024-11-01T17:51:19.2948314Z         >>> from triton import language as tl
2024-11-01T17:51:19.2948764Z         >>>
2024-11-01T17:51:19.2949045Z         >>> @triton.jit
2024-11-01T17:51:19.2949375Z         >>> def add_kernel(
2024-11-01T17:51:19.2949740Z         >>>     in_ptr0,
2024-11-01T17:51:19.2950080Z         >>>     in_ptr1,
2024-11-01T17:51:19.2950412Z         >>>     out_ptr,
2024-11-01T17:51:19.2950749Z         >>>     n_elements,
2024-11-01T17:51:19.2951130Z         >>>     BLOCK_SIZE: "tl.constexpr",
2024-11-01T17:51:19.2951566Z         >>> ):
2024-11-01T17:51:19.2951905Z         >>>     pid = tl.program_id(axis=0)
2024-11-01T17:51:19.2952395Z         >>>     block_start = pid * BLOCK_SIZE
2024-11-01T17:51:19.2952951Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2024-11-01T17:51:19.2953486Z         >>>     mask = offsets < n_elements
2024-11-01T17:51:19.2954147Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2024-11-01T17:51:19.2954705Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2024-11-01T17:51:19.2955193Z         >>>     output = x + y
2024-11-01T17:51:19.2955667Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2024-11-01T17:51:19.2956162Z         >>>
2024-11-01T17:51:19.2956512Z         >>> @triton_op("mylib::add", mutates_args={})
2024-11-01T17:51:19.2957244Z         >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2024-11-01T17:51:19.2957846Z         >>>     output = torch.empty_like(x)
2024-11-01T17:51:19.2958337Z         >>>     n_elements = output.numel()
2024-11-01T17:51:19.2958769Z         >>>
2024-11-01T17:51:19.2959048Z         >>>     def grid(meta):
2024-11-01T17:51:19.2959564Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2024-11-01T17:51:19.2960088Z         >>>
2024-11-01T17:51:19.2960562Z         >>>     # NB: we need to wrap the triton kernel in a call to capture_triton
2024-11-01T17:51:19.2961339Z         >>>     capture_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2024-11-01T17:51:19.2961925Z         >>>     return output
2024-11-01T17:51:19.2962275Z         >>>
2024-11-01T17:51:19.2962559Z         >>> @torch.compile
2024-11-01T17:51:19.2962921Z         >>> def f(x, y):
2024-11-01T17:51:19.2963544Z         >>>     return add(x, y)
2024-11-01T17:51:19.2963914Z         >>>
2024-11-01T17:51:19.2964225Z         >>> x = torch.randn(3, device="cuda")
2024-11-01T17:51:19.2964715Z         >>> y = torch.randn(3, device="cuda")
2024-11-01T17:51:19.2965150Z         >>>
2024-11-01T17:51:19.2965431Z         >>> z = f(x, y)
2024-11-01T17:51:19.2965806Z         >>> assert torch.allclose(z, x + y)
2024-11-01T17:51:19.2966128Z 
2024-11-01T17:51:19.2966228Z     
2024-11-01T17:51:19.2966816Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.2967342Z 
2024-11-01T17:51:19.2968298Z msg = Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=163.
2024-11-01T17:51:19.2969646Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.2970451Z Allows capture of a triton kernel into a graph via make_fx or
2024-11-01T17:51:19.2971082Z     non-strict export (coming soon).
2024-11-01T17:51:19.2971386Z 
2024-11-01T17:51:19.2971722Z     These technologies perform Dispatcher-based tracing (via
2024-11-01T17:51:19.2972425Z     ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2024-11-01T17:51:19.2973153Z     The ``capture_triton`` API returns a new callable that can actually
2024-11-01T17:51:19.2973747Z     be traced into a graph.
2024-11-01T17:51:19.2974005Z 
2024-11-01T17:51:19.2974113Z     Examples:
2024-11-01T17:51:19.2974288Z 
2024-11-01T17:51:19.2974428Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:19.2974800Z         >>> import torch
2024-11-01T17:51:19.2975224Z         >>> import triton
2024-11-01T17:51:19.2975624Z         >>> from triton import language as tl
2024-11-01T17:51:19.2976220Z         >>> from torch.fx.experimental.proxy_tensor import make_fx
2024-11-01T17:51:19.2976977Z         >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton
2024-11-01T17:51:19.2977579Z         >>>
2024-11-01T17:51:19.2977865Z         >>> @triton.jit
2024-11-01T17:51:19.2978192Z         >>> def add_kernel(
2024-11-01T17:51:19.2978547Z         >>>     in_ptr0,
2024-11-01T17:51:19.2978880Z         >>>     in_ptr1,
2024-11-01T17:51:19.2979209Z         >>>     out_ptr,
2024-11-01T17:51:19.2979540Z         >>>     n_elements,
2024-11-01T17:51:19.2979920Z         >>>     BLOCK_SIZE: "tl.constexpr",
2024-11-01T17:51:19.2980348Z         >>> ):
2024-11-01T17:51:19.2980676Z         >>>     pid = tl.program_id(axis=0)
2024-11-01T17:51:19.2981159Z         >>>     block_start = pid * BLOCK_SIZE
2024-11-01T17:51:19.2981717Z         >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2024-11-01T17:51:19.2982240Z         >>>     mask = offsets < n_elements
2024-11-01T17:51:19.2982751Z         >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2024-11-01T17:51:19.2983297Z         >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2024-11-01T17:51:19.2983780Z         >>>     output = x + y
2024-11-01T17:51:19.2984250Z         >>>     tl.store(out_ptr + offsets, output, mask=mask)
2024-11-01T17:51:19.2984742Z         >>>
2024-11-01T17:51:19.2985015Z         >>> def add(x, y):
2024-11-01T17:51:19.2985412Z         >>>     output = torch.empty_like(x)
2024-11-01T17:51:19.2985896Z         >>>     n_elements = output.numel()
2024-11-01T17:51:19.2986328Z         >>>
2024-11-01T17:51:19.2986619Z         >>>     def grid_fn(meta):
2024-11-01T17:51:19.2987128Z         >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2024-11-01T17:51:19.2987650Z         >>>
2024-11-01T17:51:19.2988125Z         >>>     capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2024-11-01T17:51:19.2988725Z         >>>     return output
2024-11-01T17:51:19.2989081Z         >>>
2024-11-01T17:51:19.2989391Z         >>> x = torch.randn(3, device="cuda")
2024-11-01T17:51:19.2989878Z         >>> y = torch.randn(3, device="cuda")
2024-11-01T17:51:19.2990341Z         >>> gm = make_fx(add)(x, y)
2024-11-01T17:51:19.2990749Z         >>> print(gm.code)
2024-11-01T17:51:19.2991254Z         >>> # def forward(self, x_1, y_1):
2024-11-01T17:51:19.2991922Z         >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2024-11-01T17:51:19.2992756Z         >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2024-11-01T17:51:19.2993462Z         >>> #         kernel_idx = 0, constant_args_idx = 0,
2024-11-01T17:51:19.2994146Z         >>> #         grid = [(1, 1, 1)], kwargs = {
2024-11-01T17:51:19.2994873Z         >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2024-11-01T17:51:19.2995551Z         >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2024-11-01T17:51:19.2996028Z         >>> #         })
2024-11-01T17:51:19.2996368Z         >>> #     return empty_like
2024-11-01T17:51:19.2996667Z 
2024-11-01T17:51:19.2996766Z     
2024-11-01T17:51:19.2997340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.2997852Z 
2024-11-01T17:51:19.3828882Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330.
2024-11-01T17:51:19.3830805Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.3831765Z 
2024-11-01T17:51:19.3832133Z Raises an AssertionError if two items are not equal up to desired
2024-11-01T17:51:19.3832714Z precision.
2024-11-01T17:51:19.3832877Z 
2024-11-01T17:51:19.3833159Z .. note:: It is recommended to use one of `assert_allclose`,
2024-11-01T17:51:19.3833919Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2024-11-01T17:51:19.3834958Z           instead of this function for more consistent floating point
2024-11-01T17:51:19.3835741Z           comparisons.
2024-11-01T17:51:19.3836079Z 
2024-11-01T17:51:19.3836492Z The test verifies that the elements of `actual` and `desired` satisfy.
2024-11-01T17:51:19.3837330Z 
2024-11-01T17:51:19.3837924Z     ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2024-11-01T17:51:19.3838619Z 
2024-11-01T17:51:19.3838959Z That is a looser test than originally documented, but agrees with what the
2024-11-01T17:51:19.3839799Z actual implementation in `assert_array_almost_equal` did up to rounding
2024-11-01T17:51:19.3840619Z vagaries. An exception is raised at conflicting values. For ndarrays this
2024-11-01T17:51:19.3841386Z delegates to assert_array_almost_equal
2024-11-01T17:51:19.3841804Z 
2024-11-01T17:51:19.3841994Z Parameters
2024-11-01T17:51:19.3842342Z ----------
2024-11-01T17:51:19.3842745Z actual : array_like
2024-11-01T17:51:19.3843282Z     The object to check.
2024-11-01T17:51:19.3843991Z desired : array_like
2024-11-01T17:51:19.3844495Z     The expected object.
2024-11-01T17:51:19.3844840Z decimal : int, optional
2024-11-01T17:51:19.3845215Z     Desired precision, default is 7.
2024-11-01T17:51:19.3845641Z err_msg : str, optional
2024-11-01T17:51:19.3846084Z     The error message to be printed in case of failure.
2024-11-01T17:51:19.3846592Z verbose : bool, optional
2024-11-01T17:51:19.3847123Z     If True, the conflicting values are appended to the error message.
2024-11-01T17:51:19.3847603Z 
2024-11-01T17:51:19.3847705Z Raises
2024-11-01T17:51:19.3847998Z ------
2024-11-01T17:51:19.3848274Z AssertionError
2024-11-01T17:51:19.3848749Z   If actual and desired are not equal up to specified precision.
2024-11-01T17:51:19.3849195Z 
2024-11-01T17:51:19.3849313Z See Also
2024-11-01T17:51:19.3849591Z --------
2024-11-01T17:51:19.3850074Z assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:19.3850814Z                  relative and/or absolute precision.
2024-11-01T17:51:19.3851447Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:19.3859889Z 
2024-11-01T17:51:19.3860062Z Examples
2024-11-01T17:51:19.3860458Z --------
2024-11-01T17:51:19.3860877Z >>> from torch._numpy.testing import assert_almost_equal
2024-11-01T17:51:19.3861452Z >>> assert_almost_equal(2.3333333333333, 2.33333334)
2024-11-01T17:51:19.3862302Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2024-11-01T17:51:19.3862863Z Traceback (most recent call last):
2024-11-01T17:51:19.3863262Z     ...
2024-11-01T17:51:19.3863536Z AssertionError:
2024-11-01T17:51:19.3863896Z Arrays are not almost equal to 10 decimals
2024-11-01T17:51:19.3864337Z  ACTUAL: 2.3333333333333
2024-11-01T17:51:19.3864681Z  DESIRED: 2.33333334
2024-11-01T17:51:19.3864897Z 
2024-11-01T17:51:19.3865101Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
2024-11-01T17:51:19.3865660Z ...                     np.array([1.0,2.33333334]), decimal=9)
2024-11-01T17:51:19.3866183Z Traceback (most recent call last):
2024-11-01T17:51:19.3866562Z     ...
2024-11-01T17:51:19.3866837Z AssertionError:
2024-11-01T17:51:19.3867190Z Arrays are not almost equal to 9 decimals
2024-11-01T17:51:19.3867624Z <BLANKLINE>
2024-11-01T17:51:19.3867931Z Mismatched elements: 1 / 2 (50%)
2024-11-01T17:51:19.3868452Z Max absolute difference: 6.666699636781459e-09
2024-11-01T17:51:19.3869040Z Max relative difference: 2.8571569790287484e-09
2024-11-01T17:51:19.3869577Z  x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2024-11-01T17:51:19.3870124Z  y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2024-11-01T17:51:19.3870480Z 
2024-11-01T17:51:19.3870485Z 
2024-11-01T17:51:19.3870929Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.3871441Z 
2024-11-01T17:51:19.3872397Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455.
2024-11-01T17:51:19.3874013Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.3874548Z 
2024-11-01T17:51:19.3874863Z Raises an AssertionError if two items are not equal up to significant
2024-11-01T17:51:19.3875458Z digits.
2024-11-01T17:51:19.3875621Z 
2024-11-01T17:51:19.3875890Z .. note:: It is recommended to use one of `assert_allclose`,
2024-11-01T17:51:19.3876563Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2024-11-01T17:51:19.3877263Z           instead of this function for more consistent floating point
2024-11-01T17:51:19.3877828Z           comparisons.
2024-11-01T17:51:19.3878040Z 
2024-11-01T17:51:19.3878297Z Given two numbers, check that they are approximately equal.
2024-11-01T17:51:19.3879028Z Approximately equal is defined as the number of significant digits
2024-11-01T17:51:19.3879613Z that agree.
2024-11-01T17:51:19.3879791Z 
2024-11-01T17:51:19.3879898Z Parameters
2024-11-01T17:51:19.3880203Z ----------
2024-11-01T17:51:19.3880481Z actual : scalar
2024-11-01T17:51:19.3880787Z     The object to check.
2024-11-01T17:51:19.3881132Z desired : scalar
2024-11-01T17:51:19.3881449Z     The expected object.
2024-11-01T17:51:19.3881814Z significant : int, optional
2024-11-01T17:51:19.3882212Z     Desired precision, default is 7.
2024-11-01T17:51:19.3882625Z err_msg : str, optional
2024-11-01T17:51:19.3883069Z     The error message to be printed in case of failure.
2024-11-01T17:51:19.3883588Z verbose : bool, optional
2024-11-01T17:51:19.3884122Z     If True, the conflicting values are appended to the error message.
2024-11-01T17:51:19.3884587Z 
2024-11-01T17:51:19.3884698Z Raises
2024-11-01T17:51:19.3884963Z ------
2024-11-01T17:51:19.3885240Z AssertionError
2024-11-01T17:51:19.3885711Z   If actual and desired are not equal up to specified precision.
2024-11-01T17:51:19.3886157Z 
2024-11-01T17:51:19.3886274Z See Also
2024-11-01T17:51:19.3886561Z --------
2024-11-01T17:51:19.3887031Z assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:19.3887718Z                  relative and/or absolute precision.
2024-11-01T17:51:19.3888352Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:19.3888814Z 
2024-11-01T17:51:19.3888917Z Examples
2024-11-01T17:51:19.3889207Z --------
2024-11-01T17:51:19.3889848Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)  # doctest: +SKIP
2024-11-01T17:51:19.3890996Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,  # doctest: +SKIP
2024-11-01T17:51:19.3891692Z ...                                significant=8)
2024-11-01T17:51:19.3892464Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,  # doctest: +SKIP
2024-11-01T17:51:19.3893153Z ...                                significant=8)
2024-11-01T17:51:19.3893637Z Traceback (most recent call last):
2024-11-01T17:51:19.3894030Z     ...
2024-11-01T17:51:19.3894289Z AssertionError:
2024-11-01T17:51:19.3894665Z Items are not equal to 8 significant digits:
2024-11-01T17:51:19.3895175Z  ACTUAL: 1.234567e-21
2024-11-01T17:51:19.3895546Z  DESIRED: 1.2345672e-21
2024-11-01T17:51:19.3895765Z 
2024-11-01T17:51:19.3895996Z the evaluated condition that raises the exception is
2024-11-01T17:51:19.3896377Z 
2024-11-01T17:51:19.3896707Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
2024-11-01T17:51:19.3897224Z True
2024-11-01T17:51:19.3897382Z 
2024-11-01T17:51:19.3897386Z 
2024-11-01T17:51:19.3897810Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.3898334Z 
2024-11-01T17:51:19.3899475Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734.
2024-11-01T17:51:19.3901041Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.3901907Z 
2024-11-01T17:51:19.3902322Z Raises an AssertionError if two array_like objects are not equal.
2024-11-01T17:51:19.3902794Z 
2024-11-01T17:51:19.3903205Z Given two array_like objects, check that the shape is equal and all
2024-11-01T17:51:19.3903990Z elements of these objects are equal (but see the Notes for the special
2024-11-01T17:51:19.3904771Z handling of a scalar). An exception is raised at shape mismatch or
2024-11-01T17:51:19.3905546Z conflicting values. In contrast to the standard usage in numpy, NaNs
2024-11-01T17:51:19.3906355Z are compared like numbers, no assertion is raised if both objects have
2024-11-01T17:51:19.3907263Z NaNs in the same positions.
2024-11-01T17:51:19.3907531Z 
2024-11-01T17:51:19.3907851Z The usual caution for verifying equality with floating point numbers is
2024-11-01T17:51:19.3908459Z advised.
2024-11-01T17:51:19.3908613Z 
2024-11-01T17:51:19.3908736Z Parameters
2024-11-01T17:51:19.3909063Z ----------
2024-11-01T17:51:19.3909324Z x : array_like
2024-11-01T17:51:19.3909644Z     The actual object to check.
2024-11-01T17:51:19.3910030Z y : array_like
2024-11-01T17:51:19.3910354Z     The desired, expected object.
2024-11-01T17:51:19.3910761Z err_msg : str, optional
2024-11-01T17:51:19.3911189Z     The error message to be printed in case of failure.
2024-11-01T17:51:19.3911703Z verbose : bool, optional
2024-11-01T17:51:19.3912245Z     If True, the conflicting values are appended to the error message.
2024-11-01T17:51:19.3912837Z strict : bool, optional
2024-11-01T17:51:19.3913410Z     If True, raise an AssertionError when either the shape or the data
2024-11-01T17:51:19.3914234Z     type of the array_like objects does not match. The special
2024-11-01T17:51:19.3914942Z     handling for scalars mentioned in the Notes section is disabled.
2024-11-01T17:51:19.3915419Z 
2024-11-01T17:51:19.3915519Z Raises
2024-11-01T17:51:19.3915800Z ------
2024-11-01T17:51:19.3916067Z AssertionError
2024-11-01T17:51:19.3916440Z     If actual and desired objects are not equal.
2024-11-01T17:51:19.3916785Z 
2024-11-01T17:51:19.3916905Z See Also
2024-11-01T17:51:19.3917180Z --------
2024-11-01T17:51:19.3917676Z assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:19.3918361Z                  relative and/or absolute precision.
2024-11-01T17:51:19.3919000Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:19.3919446Z 
2024-11-01T17:51:19.3919559Z Notes
2024-11-01T17:51:19.3919822Z -----
2024-11-01T17:51:19.3920270Z When one of `x` and `y` is a scalar and the other is array_like, the
2024-11-01T17:51:19.3921259Z function checks that each element of the array_like object is equal to
2024-11-01T17:51:19.3922078Z the scalar. This behaviour can be disabled with the `strict` parameter.
2024-11-01T17:51:19.3922572Z 
2024-11-01T17:51:19.3922686Z Examples
2024-11-01T17:51:19.3922980Z --------
2024-11-01T17:51:19.3923312Z The first assert does not raise an exception:
2024-11-01T17:51:19.3923674Z 
2024-11-01T17:51:19.3923889Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
2024-11-01T17:51:19.3924462Z ...                               [np.exp(0),2.33333, np.nan])
2024-11-01T17:51:19.3924874Z 
2024-11-01T17:51:19.3925221Z Use `assert_allclose` or one of the nulp (number of floating point values)
2024-11-01T17:51:19.3925871Z functions for these cases instead:
2024-11-01T17:51:19.3926156Z 
2024-11-01T17:51:19.3926366Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
2024-11-01T17:51:19.3926914Z ...                            [1, np.sqrt(np.pi)**2, np.nan],
2024-11-01T17:51:19.3927508Z ...                            rtol=1e-10, atol=0)
2024-11-01T17:51:19.3927849Z 
2024-11-01T17:51:19.3928148Z As mentioned in the Notes section, `assert_array_equal` has special
2024-11-01T17:51:19.3928944Z handling for scalars. Here the test checks that each value in `x` is 3:
2024-11-01T17:51:19.3929436Z 
2024-11-01T17:51:19.3929592Z >>> x = np.full((2, 5), fill_value=3)
2024-11-01T17:51:19.3930044Z >>> np.testing.assert_array_equal(x, 3)
2024-11-01T17:51:19.3930355Z 
2024-11-01T17:51:19.3930677Z Use `strict` to raise an AssertionError when comparing a scalar with an
2024-11-01T17:51:19.3931274Z array:
2024-11-01T17:51:19.3931550Z 
2024-11-01T17:51:19.3931760Z >>> np.testing.assert_array_equal(x, 3, strict=True)
2024-11-01T17:51:19.3932289Z Traceback (most recent call last):
2024-11-01T17:51:19.3932688Z     ...
2024-11-01T17:51:19.3932965Z AssertionError:
2024-11-01T17:51:19.3933313Z Arrays are not equal
2024-11-01T17:51:19.3933640Z <BLANKLINE>
2024-11-01T17:51:19.3933950Z (shapes (2, 5), () mismatch)
2024-11-01T17:51:19.3934345Z  x: torch.ndarray([[3, 3, 3, 3, 3],
2024-11-01T17:51:19.3934750Z         [3, 3, 3, 3, 3]])
2024-11-01T17:51:19.3935086Z  y: torch.ndarray(3)
2024-11-01T17:51:19.3935310Z 
2024-11-01T17:51:19.3935615Z The `strict` parameter also ensures that the array data types match:
2024-11-01T17:51:19.3936095Z 
2024-11-01T17:51:19.3936217Z >>> x = np.array([2, 2, 2])
2024-11-01T17:51:19.3936640Z >>> y = np.array([2., 2., 2.], dtype=np.float32)
2024-11-01T17:51:19.3937197Z >>> np.testing.assert_array_equal(x, y, strict=True)
2024-11-01T17:51:19.3937706Z Traceback (most recent call last):
2024-11-01T17:51:19.3938101Z     ...
2024-11-01T17:51:19.3938379Z AssertionError:
2024-11-01T17:51:19.3938693Z Arrays are not equal
2024-11-01T17:51:19.3939017Z <BLANKLINE>
2024-11-01T17:51:19.3939372Z (dtypes dtype("int64"), dtype("float32") mismatch)
2024-11-01T17:51:19.3939869Z  x: torch.ndarray([2, 2, 2])
2024-11-01T17:51:19.3940255Z  y: torch.ndarray([2., 2., 2.])
2024-11-01T17:51:19.3940532Z 
2024-11-01T17:51:19.3940980Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.3941490Z 
2024-11-01T17:51:19.3942491Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840.
2024-11-01T17:51:19.3943926Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.3944459Z 
2024-11-01T17:51:19.3944772Z Raises an AssertionError if two objects are not equal up to desired
2024-11-01T17:51:19.3945522Z precision.
2024-11-01T17:51:19.3945702Z 
2024-11-01T17:51:19.3945985Z .. note:: It is recommended to use one of `assert_allclose`,
2024-11-01T17:51:19.3946655Z           `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2024-11-01T17:51:19.3947350Z           instead of this function for more consistent floating point
2024-11-01T17:51:19.3947910Z           comparisons.
2024-11-01T17:51:19.3948120Z 
2024-11-01T17:51:19.3948599Z The test verifies identical shapes and that the elements of ``actual`` and
2024-11-01T17:51:19.3949211Z ``desired`` satisfy.
2024-11-01T17:51:19.3949434Z 
2024-11-01T17:51:19.3949692Z     ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2024-11-01T17:51:19.3950054Z 
2024-11-01T17:51:19.3950394Z That is a looser test than originally documented, but agrees with what the
2024-11-01T17:51:19.3951249Z actual implementation did up to rounding vagaries. An exception is raised
2024-11-01T17:51:19.3952100Z at shape mismatch or conflicting values. In contrast to the standard usage
2024-11-01T17:51:19.3952942Z in numpy, NaNs are compared like numbers, no assertion is raised if both
2024-11-01T17:51:19.3953604Z objects have NaNs in the same positions.
2024-11-01T17:51:19.3954041Z 
2024-11-01T17:51:19.3954151Z Parameters
2024-11-01T17:51:19.3954455Z ----------
2024-11-01T17:51:19.3954736Z x : array_like
2024-11-01T17:51:19.3955055Z     The actual object to check.
2024-11-01T17:51:19.3955440Z y : array_like
2024-11-01T17:51:19.3955757Z     The desired, expected object.
2024-11-01T17:51:19.3956165Z decimal : int, optional
2024-11-01T17:51:19.3956545Z     Desired precision, default is 6.
2024-11-01T17:51:19.3956967Z err_msg : str, optional
2024-11-01T17:51:19.3957411Z   The error message to be printed in case of failure.
2024-11-01T17:51:19.3957907Z verbose : bool, optional
2024-11-01T17:51:19.3958439Z     If True, the conflicting values are appended to the error message.
2024-11-01T17:51:19.3958916Z 
2024-11-01T17:51:19.3959019Z Raises
2024-11-01T17:51:19.3959299Z ------
2024-11-01T17:51:19.3959566Z AssertionError
2024-11-01T17:51:19.3960135Z     If actual and desired are not equal up to specified precision.
2024-11-01T17:51:19.3960599Z 
2024-11-01T17:51:19.3960704Z See Also
2024-11-01T17:51:19.3960998Z --------
2024-11-01T17:51:19.3961488Z assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:19.3962166Z                  relative and/or absolute precision.
2024-11-01T17:51:19.3962805Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:19.3963259Z 
2024-11-01T17:51:19.3963363Z Examples
2024-11-01T17:51:19.3963654Z --------
2024-11-01T17:51:19.3963985Z the first assert does not raise an exception
2024-11-01T17:51:19.3964327Z 
2024-11-01T17:51:19.3964586Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
2024-11-01T17:51:19.3965174Z ...                                      [1.0,2.333,np.nan])
2024-11-01T17:51:19.3965514Z 
2024-11-01T17:51:19.3965772Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2024-11-01T17:51:19.3966374Z ...                                      [1.0,2.33339,np.nan], decimal=5)
2024-11-01T17:51:19.3966897Z Traceback (most recent call last):
2024-11-01T17:51:19.3967293Z     ...
2024-11-01T17:51:19.3967566Z AssertionError:
2024-11-01T17:51:19.3967922Z Arrays are not almost equal to 5 decimals
2024-11-01T17:51:19.3968343Z <BLANKLINE>
2024-11-01T17:51:19.3968655Z Mismatched elements: 1 / 3 (33.3%)
2024-11-01T17:51:19.3969188Z Max absolute difference: 5.999999999994898e-05
2024-11-01T17:51:19.3969767Z Max relative difference: 2.5713661239633743e-05
2024-11-01T17:51:19.3970342Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2024-11-01T17:51:19.3970952Z  y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2024-11-01T17:51:19.3971423Z 
2024-11-01T17:51:19.3971781Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2024-11-01T17:51:19.3972401Z ...                                      [1.0,2.33333, 5], decimal=5)
2024-11-01T17:51:19.3972909Z Traceback (most recent call last):
2024-11-01T17:51:19.3973299Z     ...
2024-11-01T17:51:19.3973578Z AssertionError:
2024-11-01T17:51:19.3973924Z Arrays are not almost equal to 5 decimals
2024-11-01T17:51:19.3974359Z <BLANKLINE>
2024-11-01T17:51:19.3974660Z x and y nan location mismatch:
2024-11-01T17:51:19.3975156Z  x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2024-11-01T17:51:19.3975775Z  y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2024-11-01T17:51:19.3976265Z 
2024-11-01T17:51:19.3976271Z 
2024-11-01T17:51:19.3976725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.3977240Z 
2024-11-01T17:51:19.3978300Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790.
2024-11-01T17:51:19.3979725Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.3980543Z Context manager that resets warning registry for catching warnings
2024-11-01T17:51:19.3981015Z 
2024-11-01T17:51:19.3981366Z     Warnings can be slippery, because, whenever a warning is triggered, Python
2024-11-01T17:51:19.3982205Z     adds a ``__warningregistry__`` member to the *calling* module.  This makes
2024-11-01T17:51:19.3983085Z     it impossible to retrigger the warning in this module, whatever you put in
2024-11-01T17:51:19.3983942Z     the warnings filters.  This context manager accepts a sequence of `modules`
2024-11-01T17:51:19.3984650Z     as a keyword argument to its constructor and:
2024-11-01T17:51:19.3985002Z 
2024-11-01T17:51:19.3985333Z     * stores and removes any ``__warningregistry__`` entries in given `modules`
2024-11-01T17:51:19.3985944Z       on entry;
2024-11-01T17:51:19.3986392Z     * resets ``__warningregistry__`` to its previous state on exit.
2024-11-01T17:51:19.3986834Z 
2024-11-01T17:51:19.3987162Z     This makes it possible to trigger any warning afresh inside the context
2024-11-01T17:51:19.3987907Z     manager without disturbing the state of warnings outside.
2024-11-01T17:51:19.3988319Z 
2024-11-01T17:51:19.3988794Z     For compatibility with Python 3.0, please consider all arguments to be
2024-11-01T17:51:19.3989431Z     keyword-only.
2024-11-01T17:51:19.3989638Z 
2024-11-01T17:51:19.3989747Z     Parameters
2024-11-01T17:51:19.3990065Z     ----------
2024-11-01T17:51:19.3990373Z     record : bool, optional
2024-11-01T17:51:19.3990884Z         Specifies whether warnings should be captured by a custom
2024-11-01T17:51:19.3991649Z         implementation of ``warnings.showwarning()`` and be appended to a list
2024-11-01T17:51:19.3992443Z         returned by the context manager. Otherwise None is returned by the
2024-11-01T17:51:19.3993237Z         context manager. The objects appended to the list are arguments whose
2024-11-01T17:51:19.3994099Z         attributes mirror the arguments to ``showwarning()``.
2024-11-01T17:51:19.3994641Z     modules : sequence, optional
2024-11-01T17:51:19.3995241Z         Sequence of modules for which to reset warnings registry on entry and
2024-11-01T17:51:19.3996101Z         restore on exit. To work correctly, all 'ignore' filters should
2024-11-01T17:51:19.3996692Z         filter by one of these modules.
2024-11-01T17:51:19.3997010Z 
2024-11-01T17:51:19.3997117Z     Examples
2024-11-01T17:51:19.3997427Z     --------
2024-11-01T17:51:19.3997727Z     >>> import warnings
2024-11-01T17:51:19.3998225Z     >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2024-11-01T17:51:19.3998835Z     ...         modules=[np.core.fromnumeric]):
2024-11-01T17:51:19.3999397Z     ...     warnings.simplefilter('always')
2024-11-01T17:51:19.4000114Z     ...     warnings.filterwarnings('ignore', module='np.core.fromnumeric')
2024-11-01T17:51:19.4000854Z     ...     # do something that raises a warning but ignore those in
2024-11-01T17:51:19.4001411Z     ...     # np.core.fromnumeric
2024-11-01T17:51:19.4001789Z     
2024-11-01T17:51:19.4002348Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.4002874Z 
2024-11-01T17:51:19.7157263Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=355.
2024-11-01T17:51:19.7158722Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.7159564Z Applies a 1D convolution over a quantized input signal composed of
2024-11-01T17:51:19.7160173Z     several quantized input planes.
2024-11-01T17:51:19.7160813Z 
2024-11-01T17:51:19.7161141Z     For details on input arguments, parameters, and implementation see
2024-11-01T17:51:19.7161741Z     :class:`~torch.nn.Conv1d`.
2024-11-01T17:51:19.7162016Z 
2024-11-01T17:51:19.7162143Z     .. note::
2024-11-01T17:51:19.7162613Z         Only `zeros` is supported for the :attr:`padding_mode` argument.
2024-11-01T17:51:19.7163069Z 
2024-11-01T17:51:19.7163190Z     .. note::
2024-11-01T17:51:19.7163628Z         Only `torch.quint8` is supported for the input data type.
2024-11-01T17:51:19.7164048Z 
2024-11-01T17:51:19.7164053Z 
2024-11-01T17:51:19.7164183Z     Attributes:
2024-11-01T17:51:19.7164663Z         weight (Tensor):     packed tensor derived from the learnable weight
2024-11-01T17:51:19.7165272Z                              parameter.
2024-11-01T17:51:19.7165770Z         scale (Tensor):      scalar for the output scale
2024-11-01T17:51:19.7166373Z         zero_point (Tensor): scalar for the output zero point
2024-11-01T17:51:19.7166769Z 
2024-11-01T17:51:19.7166996Z     See :class:`~torch.nn.Conv1d` for other attributes.
2024-11-01T17:51:19.7167374Z 
2024-11-01T17:51:19.7167497Z     Examples::
2024-11-01T17:51:19.7167677Z 
2024-11-01T17:51:19.7167889Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2024-11-01T17:51:19.7168471Z         >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2024-11-01T17:51:19.7168995Z         >>> input = torch.randn(20, 16, 100)
2024-11-01T17:51:19.7169470Z         >>> # quantize input to quint8
2024-11-01T17:51:19.7169898Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:19.7170581Z         >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2024-11-01T17:51:19.7171249Z         ...                                     dtype=torch.quint8)
2024-11-01T17:51:19.7171748Z         >>> output = m(q_input)
2024-11-01T17:51:19.7172025Z 
2024-11-01T17:51:19.7172123Z     
2024-11-01T17:51:19.7172722Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.7173243Z 
2024-11-01T17:51:19.7368391Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11.
2024-11-01T17:51:19.7369815Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.7370582Z A quantized long short-term memory (LSTM).
2024-11-01T17:51:19.7370914Z 
2024-11-01T17:51:19.7371402Z     For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2024-11-01T17:51:19.7371993Z 
2024-11-01T17:51:19.7372103Z     Attributes:
2024-11-01T17:51:19.7372483Z         layers : instances of the `_LSTMLayer`
2024-11-01T17:51:19.7372835Z 
2024-11-01T17:51:19.7372963Z     .. note::
2024-11-01T17:51:19.7373459Z         To access the weights and biases, you need to access them per layer.
2024-11-01T17:51:19.7374199Z         See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2024-11-01T17:51:19.7374608Z 
2024-11-01T17:51:19.7374721Z     Examples::
2024-11-01T17:51:19.7375039Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:19.7375433Z         >>> custom_module_config = {
2024-11-01T17:51:19.7376000Z         ...     'float_to_observed_custom_module_class': {
2024-11-01T17:51:19.7376540Z         ...         nn.LSTM: nn.quantizable.LSTM,
2024-11-01T17:51:19.7376991Z         ...     },
2024-11-01T17:51:19.7377454Z         ...     'observed_to_quantized_custom_module_class': {
2024-11-01T17:51:19.7378048Z         ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2024-11-01T17:51:19.7378535Z         ...     }
2024-11-01T17:51:19.7378829Z         ... }
2024-11-01T17:51:19.7379317Z         >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2024-11-01T17:51:19.7380087Z         >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2024-11-01T17:51:19.7380667Z     
2024-11-01T17:51:19.7381243Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.7381769Z 
2024-11-01T17:51:19.8300626Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227.
2024-11-01T17:51:19.8302252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.8303030Z Squashes the sparse masks into the appropriate tensors.
2024-11-01T17:51:19.8303460Z 
2024-11-01T17:51:19.8303782Z         If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2024-11-01T17:51:19.8304531Z         the module will have a `sparse_params` dict attached to it.
2024-11-01T17:51:19.8304964Z 
2024-11-01T17:51:19.8305084Z         Args:
2024-11-01T17:51:19.8305539Z             params_to_keep: List of keys to save in the module or a dict
2024-11-01T17:51:19.8306222Z                             representing the modules and keys that will have
2024-11-01T17:51:19.8307006Z                             sparsity parameters saved
2024-11-01T17:51:19.8307664Z             params_to_keep_per_layer: Dict to specify the params that should be
2024-11-01T17:51:19.8308387Z                             saved for specific layers. The keys in the dict
2024-11-01T17:51:19.8309018Z                             should be the module fqn, while the values should
2024-11-01T17:51:19.8309678Z                             be a list of strings with the names of the variables
2024-11-01T17:51:19.8310284Z                             to save in the `sparse_params`
2024-11-01T17:51:19.8310643Z 
2024-11-01T17:51:19.8310755Z         Examples:
2024-11-01T17:51:19.8311143Z             >>> # xdoctest: +SKIP("locals are undefined")
2024-11-01T17:51:19.8311959Z             >>> # Don't save any sparse params
2024-11-01T17:51:19.8312440Z             >>> sparsifier.squash_mask()
2024-11-01T17:51:19.8313032Z             >>> hasattr(model.submodule1, 'sparse_params')
2024-11-01T17:51:19.8313513Z             False
2024-11-01T17:51:19.8313713Z 
2024-11-01T17:51:19.8313965Z             >>> # Keep sparse params per layer
2024-11-01T17:51:19.8314455Z             >>> sparsifier.squash_mask(
2024-11-01T17:51:19.8314924Z             ...     params_to_keep_per_layer={
2024-11-01T17:51:19.8315501Z             ...         'submodule1.linear1': ('foo', 'bar'),
2024-11-01T17:51:19.8316118Z             ...         'submodule2.linear42': ('baz',)
2024-11-01T17:51:19.8316586Z             ...     })
2024-11-01T17:51:19.8317024Z             >>> print(model.submodule1.linear1.sparse_params)
2024-11-01T17:51:19.8317596Z             {'foo': 42, 'bar': 24}
2024-11-01T17:51:19.8318086Z             >>> print(model.submodule2.linear42.sparse_params)
2024-11-01T17:51:19.8318645Z             {'baz': 0.1}
2024-11-01T17:51:19.8318881Z 
2024-11-01T17:51:19.8319057Z             >>> # Keep sparse params for all layers
2024-11-01T17:51:19.8319725Z             >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar'))
2024-11-01T17:51:19.8320366Z             >>> print(model.submodule1.linear1.sparse_params)
2024-11-01T17:51:19.8320935Z             {'foo': 42, 'bar': 24}
2024-11-01T17:51:19.8321433Z             >>> print(model.submodule2.linear42.sparse_params)
2024-11-01T17:51:19.8322002Z             {'foo': 42, 'bar': 24}
2024-11-01T17:51:19.8322280Z 
2024-11-01T17:51:19.8322578Z             >>> # Keep some sparse params for all layers, and specific ones for
2024-11-01T17:51:19.8323178Z             >>> # some other layers
2024-11-01T17:51:19.8323610Z             >>> sparsifier.squash_mask(
2024-11-01T17:51:19.8324142Z             ...     params_to_keep=('foo', 'bar'),
2024-11-01T17:51:19.8324629Z             ...     params_to_keep_per_layer={
2024-11-01T17:51:19.8325210Z             ...         'submodule2.linear42': ('baz',)
2024-11-01T17:51:19.8325675Z             ...     })
2024-11-01T17:51:19.8326110Z             >>> print(model.submodule1.linear1.sparse_params)
2024-11-01T17:51:19.8326677Z             {'foo': 42, 'bar': 24}
2024-11-01T17:51:19.8327165Z             >>> print(model.submodule2.linear42.sparse_params)
2024-11-01T17:51:19.8327772Z             {'foo': 42, 'bar': 24, 'baz': 0.1}
2024-11-01T17:51:19.8328328Z         
2024-11-01T17:51:19.8328917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.8329429Z 
2024-11-01T17:51:19.9206474Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181.
2024-11-01T17:51:19.9209587Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:19.9210124Z 
2024-11-01T17:51:19.9210545Z Config object that specifies the supported data types passed as arguments to
2024-11-01T17:51:19.9211454Z quantize ops in the reference model spec, for input and output activations,
2024-11-01T17:51:19.9212106Z weights, and biases.
2024-11-01T17:51:19.9212317Z 
2024-11-01T17:51:19.9212534Z For example, consider the following reference model:
2024-11-01T17:51:19.9212930Z 
2024-11-01T17:51:19.9213222Z   quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2024-11-01T17:51:19.9213630Z 
2024-11-01T17:51:19.9213948Z The pattern in the square brackets refers to the reference pattern of
2024-11-01T17:51:19.9214787Z statically quantized linear. Setting the input dtype as `torch.quint8`
2024-11-01T17:51:19.9215624Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2024-11-01T17:51:19.9216458Z to the first quantize op (quant1). Similarly, setting the output dtype as
2024-11-01T17:51:19.9217286Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2024-11-01T17:51:19.9217907Z the second quantize op (quant2).
2024-11-01T17:51:19.9218194Z 
2024-11-01T17:51:19.9218808Z Note that the dtype here does not refer to the interface dtypes of the
2024-11-01T17:51:19.9219612Z op. For example, the "input dtype" here is not the dtype of the input
2024-11-01T17:51:19.9220419Z tensor passed to the quantized linear op. Though it can still be the
2024-11-01T17:51:19.9221194Z same as the interface dtype, this is not always the case, e.g. the
2024-11-01T17:51:19.9221975Z interface dtype is fp32 in dynamic quantization but the "input dtype"
2024-11-01T17:51:19.9222766Z specified in the DTypeConfig would still be quint8. The semantics of
2024-11-01T17:51:19.9223582Z dtypes here are the same as the semantics of the dtypes specified in
2024-11-01T17:51:19.9224223Z the observers.
2024-11-01T17:51:19.9224405Z 
2024-11-01T17:51:19.9224755Z These dtypes are matched against the ones specified in the user's
2024-11-01T17:51:19.9225550Z QConfig. If there is a match, and the QConfig satisfies the constraints
2024-11-01T17:51:19.9226364Z specified in the DTypeConfig (if any), then we will quantize the given
2024-11-01T17:51:19.9227171Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2024-11-01T17:51:19.9227805Z the pattern will not be quantized.
2024-11-01T17:51:19.9228104Z 
2024-11-01T17:51:19.9228246Z Example usage::
2024-11-01T17:51:19.9228431Z 
2024-11-01T17:51:19.9228576Z     >>> # xdoctest: +SKIP(failing)
2024-11-01T17:51:19.9229007Z     >>> dtype_config1 = DTypeConfig(
2024-11-01T17:51:19.9229453Z     ...     input_dtype=torch.quint8,
2024-11-01T17:51:19.9229872Z     ...     output_dtype=torch.quint8,
2024-11-01T17:51:19.9230311Z     ...     weight_dtype=torch.qint8,
2024-11-01T17:51:19.9230741Z     ...     bias_dtype=torch.float)
2024-11-01T17:51:19.9231024Z 
2024-11-01T17:51:19.9231180Z     >>> dtype_config2 = DTypeConfig(
2024-11-01T17:51:19.9231664Z     ...     input_dtype=DTypeWithConstraints(
2024-11-01T17:51:19.9232126Z     ...         dtype=torch.quint8,
2024-11-01T17:51:19.9232551Z     ...         quant_min_lower_bound=0,
2024-11-01T17:51:19.9233011Z     ...         quant_max_upper_bound=255,
2024-11-01T17:51:19.9233437Z     ...     ),
2024-11-01T17:51:19.9233798Z     ...     output_dtype=DTypeWithConstraints(
2024-11-01T17:51:19.9234401Z     ...         dtype=torch.quint8,
2024-11-01T17:51:19.9234834Z     ...         quant_min_lower_bound=0,
2024-11-01T17:51:19.9235282Z     ...         quant_max_upper_bound=255,
2024-11-01T17:51:19.9235921Z     ...     ),
2024-11-01T17:51:19.9236281Z     ...     weight_dtype=DTypeWithConstraints(
2024-11-01T17:51:19.9236754Z     ...         dtype=torch.qint8,
2024-11-01T17:51:19.9237253Z     ...         quant_min_lower_bound=-128,
2024-11-01T17:51:19.9237716Z     ...         quant_max_upper_bound=127,
2024-11-01T17:51:19.9238135Z     ...     ),
2024-11-01T17:51:19.9238447Z     ...     bias_dtype=torch.float)
2024-11-01T17:51:19.9238728Z 
2024-11-01T17:51:19.9238880Z     >>> dtype_config1.input_dtype
2024-11-01T17:51:19.9239262Z     torch.quint8
2024-11-01T17:51:19.9239462Z 
2024-11-01T17:51:19.9239602Z     >>> dtype_config2.input_dtype
2024-11-01T17:51:19.9239992Z     torch.quint8
2024-11-01T17:51:19.9240181Z 
2024-11-01T17:51:19.9240375Z     >>> dtype_config2.input_dtype_with_constraints
2024-11-01T17:51:19.9241480Z     DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2024-11-01T17:51:19.9242385Z 
2024-11-01T17:51:19.9242832Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:19.9243342Z 
2024-11-01T17:51:20.0421931Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301.
2024-11-01T17:51:20.0424326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.0424864Z 
2024-11-01T17:51:20.0425304Z Takes in optional filter values and generates two tables with desired information.
2024-11-01T17:51:20.0426209Z 
2024-11-01T17:51:20.0426586Z The generated tables are presented in both a list-of-lists format
2024-11-01T17:51:20.0427257Z 
2024-11-01T17:51:20.0427623Z The reason for the two tables are that they handle different things:
2024-11-01T17:51:20.0428616Z 1.) the first table handles all tensor level information
2024-11-01T17:51:20.0429678Z 2.) the second table handles and displays all channel based information
2024-11-01T17:51:20.0430200Z 
2024-11-01T17:51:20.0430697Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2024-11-01T17:51:20.0432581Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2024-11-01T17:51:20.0433764Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2024-11-01T17:51:20.0434595Z 
2024-11-01T17:51:20.0434718Z Tensor table columns:
2024-11-01T17:51:20.0435236Z     idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:20.0435977Z     ----  ---------  ---------   ---------   ---------        ---------
2024-11-01T17:51:20.0436351Z 
2024-11-01T17:51:20.0436524Z Per-Channel table columns:
2024-11-01T17:51:20.0437103Z     idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:20.0437913Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2024-11-01T17:51:20.0438337Z 
2024-11-01T17:51:20.0438438Z Args:
2024-11-01T17:51:20.0438972Z     feature_filter (str, optional): Filters the features presented to only those that
2024-11-01T17:51:20.0439672Z         contain this filter substring
2024-11-01T17:51:20.0440198Z         Default = "", results in all the features being printed
2024-11-01T17:51:20.0440975Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:20.0441896Z         Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:20.0442450Z 
2024-11-01T17:51:20.0442599Z Returns a dictionary with two keys:
2024-11-01T17:51:20.0443148Z     (Dict[str, Tuple[List, List]]) A dict containing two keys:
2024-11-01T17:51:20.0443730Z     "tensor_level_info", "channel_level_info"
2024-11-01T17:51:20.0444200Z         Each key maps to a tuple with:
2024-11-01T17:51:20.0444678Z             A list of the headers of each table
2024-11-01T17:51:20.0445596Z             A list of lists containing the table information row by row
2024-11-01T17:51:20.0446296Z             The 0th index row will contain the headers of the columns
2024-11-01T17:51:20.0446901Z             The rest of the rows will contain data
2024-11-01T17:51:20.0447248Z 
2024-11-01T17:51:20.0447373Z Example Use:
2024-11-01T17:51:20.0447720Z     >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.0448288Z     >>> mod_report_visualizer.generate_filtered_tables(
2024-11-01T17:51:20.0448848Z     ...     feature_filter = "per_channel_min",
2024-11-01T17:51:20.0449344Z     ...     module_fqn_filter = "block1"
2024-11-01T17:51:20.0450047Z     ... ) # generates table with per_channel_min info for all modules in block 1 of the model
2024-11-01T17:51:20.0450611Z 
2024-11-01T17:51:20.0451069Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.0451580Z 
2024-11-01T17:51:20.0453024Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400.
2024-11-01T17:51:20.0454852Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.0455394Z 
2024-11-01T17:51:20.0455785Z Takes in optional filter values and prints out formatted tables of the information.
2024-11-01T17:51:20.0456361Z 
2024-11-01T17:51:20.0456876Z The reason for the two tables printed out instead of one large one are that they handle different things:
2024-11-01T17:51:20.0457875Z 1.) the first table handles all tensor level information
2024-11-01T17:51:20.0458598Z 2.) the second table handles and displays all channel based information
2024-11-01T17:51:20.0459082Z 
2024-11-01T17:51:20.0459587Z The reasoning for this is that having all the info in one table can make it ambiguous which collected
2024-11-01T17:51:20.0460844Z     statistics are global, and which are actually per-channel, so it's better to split it up into two
2024-11-01T17:51:20.0462005Z     tables. This also makes the information much easier to digest given the plethora of statistics collected
2024-11-01T17:51:20.0462705Z 
2024-11-01T17:51:20.0462827Z Tensor table columns:
2024-11-01T17:51:20.0463330Z  idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:20.0464057Z ----  ---------  ---------   ---------   ---------        ---------
2024-11-01T17:51:20.0464429Z 
2024-11-01T17:51:20.0464615Z Per-Channel table columns:
2024-11-01T17:51:20.0464862Z 
2024-11-01T17:51:20.0465203Z  idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:20.0465994Z ----  ---------  -------  ---------   ---------   ---------        ---------
2024-11-01T17:51:20.0466389Z 
2024-11-01T17:51:20.0466492Z Args:
2024-11-01T17:51:20.0467020Z     feature_filter (str, optional): Filters the features presented to only those that
2024-11-01T17:51:20.0467720Z         contain this filter substring
2024-11-01T17:51:20.0468263Z         Default = "", results in all the features being printed
2024-11-01T17:51:20.0469040Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:20.0469957Z         Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:20.0470494Z 
2024-11-01T17:51:20.0470607Z Example Use:
2024-11-01T17:51:20.0470970Z     >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.0471550Z     >>> mod_report_visualizer.generate_table_visualization(
2024-11-01T17:51:20.0472132Z     ...     feature_filter = "per_channel_min",
2024-11-01T17:51:20.0472617Z     ...     module_fqn_filter = "block1"
2024-11-01T17:51:20.0473032Z     ... )
2024-11-01T17:51:20.0473452Z     >>> # prints out neatly formatted table with per_channel_min info
2024-11-01T17:51:20.0474180Z     >>> # for all modules in block 1 of the model
2024-11-01T17:51:20.0474535Z 
2024-11-01T17:51:20.0474967Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.0475571Z 
2024-11-01T17:51:20.0477050Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=565.
2024-11-01T17:51:20.0478882Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.0479429Z 
2024-11-01T17:51:20.0479777Z Takes in a feature and optional module_filter and plots of the desired data.
2024-11-01T17:51:20.0480295Z 
2024-11-01T17:51:20.0480714Z For per channel features, it averages the value across the channels and plots a point
2024-11-01T17:51:20.0481679Z per module. The reason for this is that for models with hundreds of channels, it can
2024-11-01T17:51:20.0482655Z be hard to differentiate one channel line from another, and so the point of generating
2024-11-01T17:51:20.0483617Z a single average point per module is to give a sense of general trends that encourage
2024-11-01T17:51:20.0484315Z further deep dives.
2024-11-01T17:51:20.0484532Z 
2024-11-01T17:51:20.0484630Z Note:
2024-11-01T17:51:20.0485180Z     Only features in the report that have tensor value data are plottable by this class
2024-11-01T17:51:20.0485993Z     When the tensor information is plotted, it will plot:
2024-11-01T17:51:20.0486587Z         idx as the x val, feature value as the y_val
2024-11-01T17:51:20.0487180Z     When the channel information is plotted, it will plot:
2024-11-01T17:51:20.0488079Z         the first idx of each module as the x val, feature value as the y_val [for each channel]
2024-11-01T17:51:20.0489018Z         The reason for this is that we want to be able to compare values across the
2024-11-01T17:51:20.0489902Z         channels for same layer, and it will be hard if values are staggered by idx
2024-11-01T17:51:20.0490674Z         This means each module is represented by only 1 x value
2024-11-01T17:51:20.0491186Z Args:
2024-11-01T17:51:20.0491653Z     feature_filter (str): Filters the features presented to only those that
2024-11-01T17:51:20.0492295Z         contain this filter substring
2024-11-01T17:51:20.0492970Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:20.0493891Z         Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:20.0494428Z 
2024-11-01T17:51:20.0494552Z Example Use:
2024-11-01T17:51:20.0494918Z     >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.0495500Z     >>> mod_report_visualizer.generate_plot_visualization(
2024-11-01T17:51:20.0496184Z     ...     feature_filter = "per_channel_min",
2024-11-01T17:51:20.0496675Z     ...     module_fqn_filter = "block1"
2024-11-01T17:51:20.0497091Z     ... )
2024-11-01T17:51:20.0497513Z     >>> # outputs line plot of per_channel_min information for all
2024-11-01T17:51:20.0498285Z     >>> # modules in block1 of model each channel gets it's own line,
2024-11-01T17:51:20.0499079Z     >>> # and it's plotted across the in-order modules on the x-axis
2024-11-01T17:51:20.0499530Z 
2024-11-01T17:51:20.0499950Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.0500474Z 
2024-11-01T17:51:20.0501929Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=645.
2024-11-01T17:51:20.0503787Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.0504315Z 
2024-11-01T17:51:20.0504730Z Takes in a feature and optional module_filter and plots the histogram of desired data.
2024-11-01T17:51:20.0505303Z 
2024-11-01T17:51:20.0505411Z Note:
2024-11-01T17:51:20.0505955Z     Only features in the report that have tensor value data can be viewed as a histogram
2024-11-01T17:51:20.0507238Z     If you want to plot a histogram from all the channel values of a specific feature for
2024-11-01T17:51:20.0508370Z         a specific model, make sure to specify both the model and the feature properly
2024-11-01T17:51:20.0509287Z         in the filters and you should be able to see a distribution of the channel data
2024-11-01T17:51:20.0509829Z 
2024-11-01T17:51:20.0509938Z Args:
2024-11-01T17:51:20.0510462Z     feature_filter (str, optional): Filters the features presented to only those that
2024-11-01T17:51:20.0511150Z         contain this filter substring
2024-11-01T17:51:20.0511668Z         Default = "", results in all the features being printed
2024-11-01T17:51:20.0512445Z     module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:20.0513366Z         Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:20.0514338Z     num_bins (int, optional): The number of bins to create the histogram with
2024-11-01T17:51:20.0515118Z         Default = 10, the values will be split into 10 equal sized bins
2024-11-01T17:51:20.0515567Z 
2024-11-01T17:51:20.0515688Z Example Use:
2024-11-01T17:51:20.0515983Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.0516642Z     >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2024-11-01T17:51:20.0517404Z     ...     feature_filter = "per_channel_min",
2024-11-01T17:51:20.0517891Z     ...     module_fqn_filter = "block1"
2024-11-01T17:51:20.0518312Z     ... )
2024-11-01T17:51:20.0518848Z     # outputs histogram of per_channel_min information for all modules in block1 of model
2024-11-01T17:51:20.0519892Z         information is gathered across all channels for all modules in block 1 for the
2024-11-01T17:51:20.0520755Z         per_channel_min and is displayed in a histogram of equally sized bins
2024-11-01T17:51:20.0521249Z 
2024-11-01T17:51:20.0521713Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.0522225Z 
2024-11-01T17:51:20.3084078Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=660.
2024-11-01T17:51:20.3085590Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:20.3086106Z 
2024-11-01T17:51:20.3086507Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2024-11-01T17:51:20.3087457Z The submesh created consists of the dimensions and the communicators indicated by
2024-11-01T17:51:20.3088121Z ``mesh_dim_names``
2024-11-01T17:51:20.3088310Z 
2024-11-01T17:51:20.3096425Z Args:
2024-11-01T17:51:20.3097108Z     mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2024-11-01T17:51:20.3097913Z     mesh dimension of the DeviceMesh to create the submesh for.
2024-11-01T17:51:20.3098452Z Returns:
2024-11-01T17:51:20.3098736Z     A :class:`DeviceMesh` object
2024-11-01T17:51:20.3099026Z 
2024-11-01T17:51:20.3099443Z The following program runs on each process/rank in an SPMD manner in a world size of 8.
2024-11-01T17:51:20.3100177Z In the first example:
2024-11-01T17:51:20.3100797Z     Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2024-11-01T17:51:20.3101811Z     Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2024-11-01T17:51:20.3102734Z     Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2024-11-01T17:51:20.3103594Z     Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2024-11-01T17:51:20.3104476Z     Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2024-11-01T17:51:20.3105352Z     Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2024-11-01T17:51:20.3105884Z 
2024-11-01T17:51:20.3106008Z In the second example:
2024-11-01T17:51:20.3106959Z     Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2024-11-01T17:51:20.3108398Z     Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2024-11-01T17:51:20.3109435Z     Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2024-11-01T17:51:20.3110451Z     Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2024-11-01T17:51:20.3111067Z 
2024-11-01T17:51:20.3111206Z Example::
2024-11-01T17:51:20.3111512Z     >>> # xdoctest: +SKIP("no rank")
2024-11-01T17:51:20.3112063Z     >>> from torch.distributed.device_mesh import DeviceMesh
2024-11-01T17:51:20.3112593Z     >>>
2024-11-01T17:51:20.3113042Z     >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2024-11-01T17:51:20.3114042Z     >>> # of cross-host(dim 0), and within-host (dim 1).
2024-11-01T17:51:20.3114794Z     >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2024-11-01T17:51:20.3115468Z     >>> tp_mesh = mesh_2d["tp"]
2024-11-01T17:51:20.3115871Z     >>> dp_mesh = mesh_2d["dp"]
2024-11-01T17:51:20.3116235Z     >>>
2024-11-01T17:51:20.3116504Z     >>> # Initialize a 3D mesh.
2024-11-01T17:51:20.3117191Z     >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2024-11-01T17:51:20.3118211Z     >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2024-11-01T17:51:20.3118980Z     >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2024-11-01T17:51:20.3119444Z     >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2024-11-01T17:51:20.3119750Z 
2024-11-01T17:51:20.3121018Z Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2024-11-01T17:51:20.3122117Z 
2024-11-01T17:51:20.3122485Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2024-11-01T17:51:20.3123188Z                                                                                  ^
2024-11-01T17:51:20.3495884Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2940.
2024-11-01T17:51:20.3497358Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.3497939Z 
2024-11-01T17:51:20.3498247Z Gathers picklable objects from the whole group in a single process.
2024-11-01T17:51:20.3498735Z 
2024-11-01T17:51:20.3499076Z Similar to :func:`gather`, but Python objects can be passed in. Note that the
2024-11-01T17:51:20.3499809Z object must be picklable in order to be gathered.
2024-11-01T17:51:20.3500173Z 
2024-11-01T17:51:20.3500283Z Args:
2024-11-01T17:51:20.3500617Z     obj (Any): Input object. Must be picklable.
2024-11-01T17:51:20.3501248Z     object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2024-11-01T17:51:20.3501989Z         should be correctly sized as the size of the group for this
2024-11-01T17:51:20.3502831Z         collective and will contain the output. Must be ``None`` on non-dst
2024-11-01T17:51:20.3503446Z         ranks. (default is ``None``)
2024-11-01T17:51:20.3504292Z     dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0)
2024-11-01T17:51:20.3505327Z     group: (ProcessGroup, optional): The process group to work on. If None,
2024-11-01T17:51:20.3506083Z         the default process group will be used. Default is ``None``.
2024-11-01T17:51:20.3506531Z 
2024-11-01T17:51:20.3506794Z Returns:
2024-11-01T17:51:20.3507248Z     None. On the ``dst`` rank, ``object_gather_list`` will contain the
2024-11-01T17:51:20.3507835Z     output of the collective.
2024-11-01T17:51:20.3508093Z 
2024-11-01T17:51:20.3508451Z .. note:: Note that this API differs slightly from the gather collective
2024-11-01T17:51:20.3509267Z     since it does not provide an async_op handle and thus will be a blocking
2024-11-01T17:51:20.3509867Z     call.
2024-11-01T17:51:20.3510266Z 
2024-11-01T17:51:20.3510696Z .. note:: For NCCL-based processed groups, internal tensor representations
2024-11-01T17:51:20.3511520Z     of objects must be moved to the GPU device before communication takes
2024-11-01T17:51:20.3512217Z     place. In this case, the device used is given by
2024-11-01T17:51:20.3512979Z     ``torch.cuda.current_device()`` and it is the user's responsiblity to
2024-11-01T17:51:20.3513776Z     ensure that this is set so that each rank has an individual GPU, via
2024-11-01T17:51:20.3514476Z     ``torch.cuda.set_device()``.
2024-11-01T17:51:20.3514761Z 
2024-11-01T17:51:20.3514879Z .. warning::
2024-11-01T17:51:20.3515353Z     :func:`gather_object` uses ``pickle`` module implicitly, which is
2024-11-01T17:51:20.3516141Z     known to be insecure. It is possible to construct malicious pickle data
2024-11-01T17:51:20.3516955Z     which will execute arbitrary code during unpickling. Only call this
2024-11-01T17:51:20.3517574Z     function with data you trust.
2024-11-01T17:51:20.3517859Z 
2024-11-01T17:51:20.3517970Z .. warning::
2024-11-01T17:51:20.3518456Z     Calling :func:`gather_object` with GPU tensors is not well supported
2024-11-01T17:51:20.3519344Z     and inefficient as it incurs GPU -> CPU transfer since tensors would be
2024-11-01T17:51:20.3520082Z     pickled. Please consider using :func:`gather` instead.
2024-11-01T17:51:20.3520482Z 
2024-11-01T17:51:20.3520601Z Example::
2024-11-01T17:51:20.3520957Z     >>> # xdoctest: +SKIP("need process group init")
2024-11-01T17:51:20.3521561Z     >>> # Note: Process group initialization omitted on each rank.
2024-11-01T17:51:20.3522266Z     >>> import torch.distributed as dist
2024-11-01T17:51:20.3522866Z     >>> # Assumes world_size of 3.
2024-11-01T17:51:20.3523407Z     >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2024-11-01T17:51:20.3524011Z     >>> output = [None for _ in gather_objects]
2024-11-01T17:51:20.3524485Z     >>> dist.gather_object(
2024-11-01T17:51:20.3524886Z     ...     gather_objects[dist.get_rank()],
2024-11-01T17:51:20.3525415Z     ...     output if dist.get_rank() == 0 else None,
2024-11-01T17:51:20.3525879Z     ...     dst=0
2024-11-01T17:51:20.3526172Z     ... )
2024-11-01T17:51:20.3526440Z     >>> # On rank 0
2024-11-01T17:51:20.3526734Z     >>> output
2024-11-01T17:51:20.3527090Z     ['foo', 12, {1: 2}]
2024-11-01T17:51:20.3527315Z 
2024-11-01T17:51:20.3527740Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.3528249Z 
2024-11-01T17:51:20.3695139Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2.
2024-11-01T17:51:20.3696691Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.3697230Z 
2024-11-01T17:51:20.3697463Z Module ``torch.distributed.launch``.
2024-11-01T17:51:20.3697783Z 
2024-11-01T17:51:20.3698143Z ``torch.distributed.launch`` is a module that spawns up multiple distributed
2024-11-01T17:51:20.3698881Z training processes on each of the training nodes.
2024-11-01T17:51:20.3699250Z 
2024-11-01T17:51:20.3699390Z .. warning::
2024-11-01T17:51:20.3699566Z 
2024-11-01T17:51:20.3700051Z     This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2024-11-01T17:51:20.3700603Z 
2024-11-01T17:51:20.3701053Z The utility can be used for single-node distributed training, in which one or
2024-11-01T17:51:20.3701912Z more processes per node will be spawned. The utility can be used for either
2024-11-01T17:51:20.3702753Z CPU training or GPU training. If the utility is used for GPU training,
2024-11-01T17:51:20.3703608Z each distributed process will be operating on a single GPU. This can achieve
2024-11-01T17:51:20.3704536Z well-improved single-node training performance. It can also be used in
2024-11-01T17:51:20.3705481Z multi-node distributed training, by spawning up multiple processes on each node
2024-11-01T17:51:20.3706497Z for well-improved multi-node distributed training performance as well.
2024-11-01T17:51:20.3707805Z This will especially be beneficial for systems with multiple Infiniband
2024-11-01T17:51:20.3708757Z interfaces that have direct-GPU support, since all of them can be utilized for
2024-11-01T17:51:20.3709448Z aggregated communication bandwidth.
2024-11-01T17:51:20.3709746Z 
2024-11-01T17:51:20.3710184Z In both cases of single-node distributed training or multi-node distributed
2024-11-01T17:51:20.3711042Z training, this utility will launch the given number of processes per node
2024-11-01T17:51:20.3711963Z (``--nproc-per-node``). If used for GPU training, this number needs to be less
2024-11-01T17:51:20.3712811Z or equal to the number of GPUs on the current system (``nproc_per_node``),
2024-11-01T17:51:20.3713609Z and each process will be operating on a single GPU from *GPU 0 to
2024-11-01T17:51:20.3714345Z GPU (nproc_per_node - 1)*.
2024-11-01T17:51:20.3714600Z 
2024-11-01T17:51:20.3714727Z **How to use this module:**
2024-11-01T17:51:20.3714972Z 
2024-11-01T17:51:20.3715255Z 1. Single-Node multi-process distributed training
2024-11-01T17:51:20.3715625Z 
2024-11-01T17:51:20.3715747Z ::
2024-11-01T17:51:20.3715884Z 
2024-11-01T17:51:20.3716287Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2024-11-01T17:51:20.3717134Z                YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2024-11-01T17:51:20.3717747Z                arguments of your training script)
2024-11-01T17:51:20.3718101Z 
2024-11-01T17:51:20.3718469Z 2. Multi-Node multi-process distributed training: (e.g. two nodes)
2024-11-01T17:51:20.3718933Z 
2024-11-01T17:51:20.3718951Z 
2024-11-01T17:51:20.3719273Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2024-11-01T17:51:20.3719648Z 
2024-11-01T17:51:20.3719761Z ::
2024-11-01T17:51:20.3719898Z 
2024-11-01T17:51:20.3720324Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2024-11-01T17:51:20.3721112Z                --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2024-11-01T17:51:20.3721888Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2024-11-01T17:51:20.3722572Z                and all other arguments of your training script)
2024-11-01T17:51:20.3722972Z 
2024-11-01T17:51:20.3723072Z Node 2:
2024-11-01T17:51:20.3723223Z 
2024-11-01T17:51:20.3723332Z ::
2024-11-01T17:51:20.3723470Z 
2024-11-01T17:51:20.3723886Z     python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2024-11-01T17:51:20.3724683Z                --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2024-11-01T17:51:20.3725435Z                --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2024-11-01T17:51:20.3726113Z                and all other arguments of your training script)
2024-11-01T17:51:20.3726512Z 
2024-11-01T17:51:20.3726752Z 3. To look up what optional arguments this module offers:
2024-11-01T17:51:20.3727168Z 
2024-11-01T17:51:20.3727266Z ::
2024-11-01T17:51:20.3727403Z 
2024-11-01T17:51:20.3727665Z     python -m torch.distributed.launch --help
2024-11-01T17:51:20.3728021Z 
2024-11-01T17:51:20.3728025Z 
2024-11-01T17:51:20.3728159Z **Important Notices:**
2024-11-01T17:51:20.3728380Z 
2024-11-01T17:51:20.3728710Z 1. This utility and multi-process distributed (single-node or
2024-11-01T17:51:20.3729561Z multi-node) GPU training currently only achieves the best performance using
2024-11-01T17:51:20.3730438Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2024-11-01T17:51:20.3731091Z use for GPU training.
2024-11-01T17:51:20.3731304Z 
2024-11-01T17:51:20.3731704Z 2. In your training program, you must parse the command-line argument:
2024-11-01T17:51:20.3732596Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2024-11-01T17:51:20.3733429Z If your training program uses GPUs, you should ensure that your code only
2024-11-01T17:51:20.3734212Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2024-11-01T17:51:20.3734682Z 
2024-11-01T17:51:20.3734819Z Parsing the local_rank argument
2024-11-01T17:51:20.3735200Z 
2024-11-01T17:51:20.3735301Z ::
2024-11-01T17:51:20.3735439Z 
2024-11-01T17:51:20.3735579Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.3735937Z     >>> import argparse
2024-11-01T17:51:20.3736322Z     >>> parser = argparse.ArgumentParser()
2024-11-01T17:51:20.3737019Z     >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2024-11-01T17:51:20.3737604Z     >>> args = parser.parse_args()
2024-11-01T17:51:20.3737902Z 
2024-11-01T17:51:20.3738065Z Set your device to local rank using either
2024-11-01T17:51:20.3738395Z 
2024-11-01T17:51:20.3738507Z ::
2024-11-01T17:51:20.3738646Z 
2024-11-01T17:51:20.3738949Z     >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2024-11-01T17:51:20.3739406Z 
2024-11-01T17:51:20.3739519Z or
2024-11-01T17:51:20.3739660Z 
2024-11-01T17:51:20.3739757Z ::
2024-11-01T17:51:20.3739906Z 
2024-11-01T17:51:20.3740088Z     >>> with torch.cuda.device(args.local_rank):
2024-11-01T17:51:20.3740565Z     >>>    # your code to run
2024-11-01T17:51:20.3740925Z     >>>    ...
2024-11-01T17:51:20.3741098Z 
2024-11-01T17:51:20.3741239Z .. versionchanged:: 2.0.0
2024-11-01T17:51:20.3741473Z 
2024-11-01T17:51:20.3741919Z     The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2024-11-01T17:51:20.3742874Z     From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2024-11-01T17:51:20.3743648Z     previously used underscored ``--local_rank``.
2024-11-01T17:51:20.3744015Z 
2024-11-01T17:51:20.3744358Z     For backward compatibility, it may be necessary for users to handle both
2024-11-01T17:51:20.3745443Z     cases in their argument parsing code. This means including both ``"--local-rank"``
2024-11-01T17:51:20.3746407Z     and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2024-11-01T17:51:20.3747262Z     provided, the launcher will trigger an error: "error: unrecognized arguments:
2024-11-01T17:51:20.3748195Z     --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2024-11-01T17:51:20.3748972Z     including ``"--local-rank"`` should be sufficient.
2024-11-01T17:51:20.3749360Z 
2024-11-01T17:51:20.3749702Z 3. In your training program, you are supposed to call the following function
2024-11-01T17:51:20.3750568Z at the beginning to start the distributed backend. It is strongly recommended
2024-11-01T17:51:20.3751421Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2024-11-01T17:51:20.3752201Z but ``env://`` is the one that is officially supported by this module.
2024-11-01T17:51:20.3752663Z 
2024-11-01T17:51:20.3752775Z ::
2024-11-01T17:51:20.3752911Z 
2024-11-01T17:51:20.3753278Z     >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2024-11-01T17:51:20.3754106Z     >>>                                      init_method='env://')
2024-11-01T17:51:20.3754482Z 
2024-11-01T17:51:20.3754834Z 4. In your training program, you can either use regular distributed functions
2024-11-01T17:51:20.3755704Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2024-11-01T17:51:20.3756533Z training program uses GPUs for training and you would like to use
2024-11-01T17:51:20.3757264Z :func:`torch.nn.parallel.DistributedDataParallel` module,
2024-11-01T17:51:20.3757823Z here is how to configure it.
2024-11-01T17:51:20.3758091Z 
2024-11-01T17:51:20.3758192Z ::
2024-11-01T17:51:20.3758344Z 
2024-11-01T17:51:20.3758626Z     >>> model = torch.nn.parallel.DistributedDataParallel(model,
2024-11-01T17:51:20.3759288Z     >>>                                                   device_ids=[args.local_rank],
2024-11-01T17:51:20.3759904Z     >>>                                                   output_device=args.local_rank)
2024-11-01T17:51:20.3760294Z 
2024-11-01T17:51:20.3760651Z Please ensure that ``device_ids`` argument is set to be the only GPU device id
2024-11-01T17:51:20.3761511Z that your code will be operating on. This is generally the local rank of the
2024-11-01T17:51:20.3762375Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2024-11-01T17:51:20.3763293Z and ``output_device`` needs to be ``args.local_rank`` in order to use this
2024-11-01T17:51:20.3763887Z utility
2024-11-01T17:51:20.3764036Z 
2024-11-01T17:51:20.3764414Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2024-11-01T17:51:20.3765269Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2024-11-01T17:51:20.3766163Z ``--use-env=True``. You must adjust the subprocess example above to replace
2024-11-01T17:51:20.3767001Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2024-11-01T17:51:20.3767757Z will not pass ``--local-rank`` when you specify this flag.
2024-11-01T17:51:20.3768178Z 
2024-11-01T17:51:20.3768289Z .. warning::
2024-11-01T17:51:20.3768459Z 
2024-11-01T17:51:20.3768773Z     ``local_rank`` is NOT globally unique: it is only unique per process
2024-11-01T17:51:20.3769599Z     on a machine.  Thus, don't use it to decide if you should, e.g.,
2024-11-01T17:51:20.3770222Z     write to a networked filesystem.  See
2024-11-01T17:51:20.3770831Z     https://github.com/pytorch/pytorch/issues/12042 for an example of
2024-11-01T17:51:20.3771610Z     how things can go wrong if you don't do this correctly.
2024-11-01T17:51:20.3772021Z 
2024-11-01T17:51:20.3772026Z 
2024-11-01T17:51:20.3772031Z 
2024-11-01T17:51:20.3772035Z 
2024-11-01T17:51:20.3772455Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.3772981Z 
2024-11-01T17:51:20.4468712Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361.
2024-11-01T17:51:20.4470388Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.4470944Z 
2024-11-01T17:51:20.4471291Z Creates an :class:`ShardedTensor` from local shards and the global metadata.
2024-11-01T17:51:20.4472019Z Needs to be called on all ranks in an SPMD fashion.
2024-11-01T17:51:20.4472408Z 
2024-11-01T17:51:20.4472519Z Args:
2024-11-01T17:51:20.4473070Z     local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2024-11-01T17:51:20.4474026Z         of shards that represent the local shards on this rank.
2024-11-01T17:51:20.4474790Z     global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2024-11-01T17:51:20.4475481Z         shape of the overall sharded tensor.
2024-11-01T17:51:20.4475877Z 
2024-11-01T17:51:20.4475986Z Keyword args:
2024-11-01T17:51:20.4476553Z     process_group (ProcessGroup, optional): The process group to work on. If None,
2024-11-01T17:51:20.4477268Z         the default process group will be used.
2024-11-01T17:51:20.4477842Z     init_rrefs (bool, optional): Whether or not to initialize
2024-11-01T17:51:20.4478562Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2024-11-01T17:51:20.4479320Z         Need to initialize the RPC Framework if specified as ``True``.
2024-11-01T17:51:20.4479908Z         Default: ``False``.
2024-11-01T17:51:20.4480147Z 
2024-11-01T17:51:20.4480262Z Returns:
2024-11-01T17:51:20.4480644Z     A :class:`ShardedTensor` object handle on this rank
2024-11-01T17:51:20.4481022Z 
2024-11-01T17:51:20.4481027Z 
2024-11-01T17:51:20.4481134Z Examples:
2024-11-01T17:51:20.4481674Z     Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2024-11-01T17:51:20.4482496Z     each shard have a (5, 5) local tensor, we can do it like below:
2024-11-01T17:51:20.4482958Z 
2024-11-01T17:51:20.4483067Z     on rank 0:
2024-11-01T17:51:20.4483420Z     >>> # xdoctest: +SKIP("not distributed")
2024-11-01T17:51:20.4483935Z     >>> local_shard_metadata = ShardMetadata(
2024-11-01T17:51:20.4484392Z     >>>     shard_offsets=[0, 0],
2024-11-01T17:51:20.4484790Z     >>>     shard_lengths=[5, 5],
2024-11-01T17:51:20.4485207Z     >>>     placement="rank:0/cuda:0"
2024-11-01T17:51:20.4485599Z     >>> )
2024-11-01T17:51:20.4486039Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2024-11-01T17:51:20.4486919Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2024-11-01T17:51:20.4487383Z 
2024-11-01T17:51:20.4487489Z     on rank 1:
2024-11-01T17:51:20.4487839Z     >>> # xdoctest: +SKIP("not distributed")
2024-11-01T17:51:20.4488347Z     >>> local_shard_metadata = ShardMetadata(
2024-11-01T17:51:20.4488810Z     >>>     shard_offsets=[5, 0],
2024-11-01T17:51:20.4489212Z     >>>     shard_lengths=[5, 5],
2024-11-01T17:51:20.4489605Z     >>>     placement="rank:1/cuda:1"
2024-11-01T17:51:20.4490024Z     >>> )
2024-11-01T17:51:20.4490467Z     >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2024-11-01T17:51:20.4491189Z     >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2024-11-01T17:51:20.4491624Z 
2024-11-01T17:51:20.4492089Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.4492606Z 
2024-11-01T17:51:20.4594178Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784.
2024-11-01T17:51:20.4595859Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.4596400Z 
2024-11-01T17:51:20.4596816Z Initialize a ShardedTensor given only one local tensor, global sharded tensor
2024-11-01T17:51:20.4597495Z size and sharding spec on each rank.
2024-11-01T17:51:20.4597820Z 
2024-11-01T17:51:20.4597922Z Args:
2024-11-01T17:51:20.4598680Z     local_tensor (Tensor): Single tensor of local shard stored in each rank.
2024-11-01T17:51:20.4599557Z     sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2024-11-01T17:51:20.4600343Z         The specification describing how to shard the Tensor.
2024-11-01T17:51:20.4601255Z     global_size (Sequence[int]): Size of the sharded tensor.
2024-11-01T17:51:20.4602327Z     process_group (ProcessGroup, optional): The process group to aggregate on.
2024-11-01T17:51:20.4603503Z         Default: None
2024-11-01T17:51:20.4604019Z     init_rrefs (bool, optional): Whether or not to initialize
2024-11-01T17:51:20.4604733Z         :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2024-11-01T17:51:20.4605500Z         Need to initialize the RPC Framework if specified as ``True``.
2024-11-01T17:51:20.4606090Z         Default: ``False``.
2024-11-01T17:51:20.4606335Z 
2024-11-01T17:51:20.4606439Z Returns:
2024-11-01T17:51:20.4607247Z     A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2024-11-01T17:51:20.4607976Z         tensor stored in the current rank.
2024-11-01T17:51:20.4608299Z 
2024-11-01T17:51:20.4608428Z Examples:
2024-11-01T17:51:20.4608743Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.4609172Z     >>> # All tensors below are of torch.int64 type.
2024-11-01T17:51:20.4609719Z     >>> # We have 2 process groups, 2 ranks.
2024-11-01T17:51:20.4610319Z     >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2024-11-01T17:51:20.4611049Z     >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2024-11-01T17:51:20.4611623Z     >>> local_tensor
2024-11-01T17:51:20.4611962Z     tensor([[1, 2, 3, 4]]) # Rank 0
2024-11-01T17:51:20.4612368Z     tensor([[3, 4, 5, 6]]) # Rank 1
2024-11-01T17:51:20.4612775Z     >>> sharding_dim = 0
2024-11-01T17:51:20.4613176Z     >>> sharding_spec = ChunkShardingSpec(
2024-11-01T17:51:20.4613638Z             dim=sharding_dim,
2024-11-01T17:51:20.4614014Z             placements=[
2024-11-01T17:51:20.4614350Z                 "rank:0/cuda:0",
2024-11-01T17:51:20.4614743Z                 "rank:1/cuda:1",
2024-11-01T17:51:20.4615116Z             ],
2024-11-01T17:51:20.4615403Z         )
2024-11-01T17:51:20.4615925Z     >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4])
2024-11-01T17:51:20.4616560Z     >>> st
2024-11-01T17:51:20.4616847Z     ShardedTensor(
2024-11-01T17:51:20.4617192Z         ShardedTensorMetadata(
2024-11-01T17:51:20.4617825Z             shards_metadata=[
2024-11-01T17:51:20.4618466Z                 ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2024-11-01T17:51:20.4619383Z                 ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2024-11-01T17:51:20.4620014Z             ],
2024-11-01T17:51:20.4620335Z             size=torch.Size([2, 4])
2024-11-01T17:51:20.4620727Z     )
2024-11-01T17:51:20.4621000Z     >>> st.local_tensor()
2024-11-01T17:51:20.4621362Z     tensor([1, 2, 3, 4]) # Rank 0
2024-11-01T17:51:20.4621748Z     tensor([3, 4, 5, 6]) # Rank 1
2024-11-01T17:51:20.4622043Z 
2024-11-01T17:51:20.4622438Z Warning: This API is experimental and subject to change. It lacks of a fully across
2024-11-01T17:51:20.4623374Z          rank validations, and we only validate the local shard on the current rank.
2024-11-01T17:51:20.4624257Z          We fully rely on the user to ensure local tensor is sharded based on the
2024-11-01T17:51:20.4624895Z          sharding spec.
2024-11-01T17:51:20.4625113Z 
2024-11-01T17:51:20.4625689Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.4626201Z 
2024-11-01T17:51:20.4627395Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023.
2024-11-01T17:51:20.4628959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.4629491Z 
2024-11-01T17:51:20.4630017Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2024-11-01T17:51:20.4630664Z single local shard.
2024-11-01T17:51:20.4630882Z 
2024-11-01T17:51:20.4631279Z If ``resharding_spec`` is same as the original one, this becomes a no-op.
2024-11-01T17:51:20.4632123Z If only ``resharding_spec`` shares the same sharding dim with the original one,
2024-11-01T17:51:20.4632795Z we swap local shards directly.
2024-11-01T17:51:20.4633447Z For more generic cases, we merge different shards across different ranks and split
2024-11-01T17:51:20.4634512Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2024-11-01T17:51:20.4635053Z 
2024-11-01T17:51:20.4635152Z Args:
2024-11-01T17:51:20.4635710Z     resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2024-11-01T17:51:20.4636517Z         specification describing how the tensor is sharded.
2024-11-01T17:51:20.4636922Z 
2024-11-01T17:51:20.4637026Z Returns:
2024-11-01T17:51:20.4637481Z     A :class:`ShardedTensor` object whose local shards are resharded.
2024-11-01T17:51:20.4637940Z 
2024-11-01T17:51:20.4638060Z Examples:
2024-11-01T17:51:20.4638338Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.4638742Z     >>> # We have 2 process groups, 2 ranks.
2024-11-01T17:51:20.4639341Z     >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2024-11-01T17:51:20.4639938Z     >>> tensor = torch.stack([tensor, tensor])
2024-11-01T17:51:20.4640389Z     >>> tensor
2024-11-01T17:51:20.4640723Z     tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2024-11-01T17:51:20.4641227Z     tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2024-11-01T17:51:20.4641722Z     tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2024-11-01T17:51:20.4642228Z     tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2024-11-01T17:51:20.4642700Z     >>> sharding_dim = 0
2024-11-01T17:51:20.4643077Z     >>> spec = ChunkShardingSpec(
2024-11-01T17:51:20.4643494Z             dim=sharding_dim,
2024-11-01T17:51:20.4643865Z             placements=[
2024-11-01T17:51:20.4644215Z                 "rank:0/cuda:0",
2024-11-01T17:51:20.4644725Z                 "rank:1/cuda:1",
2024-11-01T17:51:20.4645104Z                 "rank:2/cuda:2",
2024-11-01T17:51:20.4645464Z                 "rank:3/cuda:3",
2024-11-01T17:51:20.4645832Z             ],
2024-11-01T17:51:20.4646118Z         )
2024-11-01T17:51:20.4646409Z     >>> current_offsets = [0] * 2
2024-11-01T17:51:20.4646827Z     >>> current_offsets[0] = rank * 2
2024-11-01T17:51:20.4647383Z     >>> shard_metadata = ShardMetadata(
2024-11-01T17:51:20.4647906Z             shard_offsets=copy.deepcopy(current_offsets),
2024-11-01T17:51:20.4648422Z             shard_sizes=tensor.size(),
2024-11-01T17:51:20.4648894Z             placement=spec.placements[rank],
2024-11-01T17:51:20.4649332Z         )
2024-11-01T17:51:20.4649602Z     >>> local_shards = [
2024-11-01T17:51:20.4649938Z             Shard(
2024-11-01T17:51:20.4650251Z                 tensor=tensor,
2024-11-01T17:51:20.4650649Z                 metadata=shard_metadata,
2024-11-01T17:51:20.4651065Z             )
2024-11-01T17:51:20.4651338Z         ]
2024-11-01T17:51:20.4651866Z     >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2024-11-01T17:51:20.4652490Z     >>> sharding_dim = 1
2024-11-01T17:51:20.4652900Z     >>> resharding_spec = ChunkShardingSpec(
2024-11-01T17:51:20.4653370Z             dim=sharding_dim,
2024-11-01T17:51:20.4653743Z             placements=[
2024-11-01T17:51:20.4654086Z                 "rank:0/cuda:0",
2024-11-01T17:51:20.4654469Z                 "rank:1/cuda:1",
2024-11-01T17:51:20.4654853Z                 "rank:2/cuda:2",
2024-11-01T17:51:20.4655230Z                 "rank:3/cuda:3",
2024-11-01T17:51:20.4655593Z             ],
2024-11-01T17:51:20.4655867Z         )
2024-11-01T17:51:20.4656170Z     >>> st.reshard(resharding_spec)
2024-11-01T17:51:20.4656657Z     >>> tensor = st.local_shards()[0].tensor
2024-11-01T17:51:20.4657094Z     >>> tensor
2024-11-01T17:51:20.4657483Z     tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2024-11-01T17:51:20.4658174Z     tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2024-11-01T17:51:20.4658755Z     tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2024-11-01T17:51:20.4659343Z     tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2024-11-01T17:51:20.4659718Z 
2024-11-01T17:51:20.4660210Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.4660727Z 
2024-11-01T17:51:20.4758531Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12.
2024-11-01T17:51:20.4760017Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.4760588Z 
2024-11-01T17:51:20.4760909Z Representation of a sharding plan, describes how to shard a module
2024-11-01T17:51:20.4761786Z across hosts. `plan` is used to shard module parameters according to the spec provided,
2024-11-01T17:51:20.4762769Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2024-11-01T17:51:20.4763737Z layout of a module with a spec, and when to convert back to data parallel fashion.
2024-11-01T17:51:20.4764298Z 
2024-11-01T17:51:20.4764398Z Args:
2024-11-01T17:51:20.4764948Z     plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2024-11-01T17:51:20.4765761Z           :class:`torch.distributed._shard.sharder.Sharder`]):
2024-11-01T17:51:20.4766694Z         a dict describes how to shard a module, there're currently two ways to shard a module:
2024-11-01T17:51:20.4767661Z             1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2024-11-01T17:51:20.4768377Z                a parameter to a `ShardingSpec`.
2024-11-01T17:51:20.4769107Z             2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2024-11-01T17:51:20.4769809Z                to a `Sharder` object.
2024-11-01T17:51:20.4770584Z     output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2024-11-01T17:51:20.4771704Z         a dict specifies the layout of a module's output which produces a ShardedTensor,
2024-11-01T17:51:20.4772633Z         keyed by the name of module to ShardingSpec("" in key means the root module).
2024-11-01T17:51:20.4773279Z         Default: `None`
2024-11-01T17:51:20.4773875Z     return_local_tensor (List[str], optional): a list of string, each element enables
2024-11-01T17:51:20.4775102Z         a module's sharded output to be returned as a Tensor from its local shards to
2024-11-01T17:51:20.4775998Z         ensure further processing in a data parallel fashion. ("" in list means the
2024-11-01T17:51:20.4776640Z         root module).
2024-11-01T17:51:20.4776970Z         Default: None
2024-11-01T17:51:20.4777278Z Example:
2024-11-01T17:51:20.4777871Z   Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2024-11-01T17:51:20.4778911Z   want to convert the output of the second linear layer back to DDP, we can do it as follows:
2024-11-01T17:51:20.4779512Z 
2024-11-01T17:51:20.4779766Z     >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2024-11-01T17:51:20.4780324Z     >>> class MyModule(nn.Module):
2024-11-01T17:51:20.4780795Z     >>>     def __init__(self) -> None:
2024-11-01T17:51:20.4781234Z     >>>        super().__init__()
2024-11-01T17:51:20.4781646Z     >>>        self.fc1 = nn.Linear()
2024-11-01T17:51:20.4782075Z     >>>        self.gelu = nn.GELU()
2024-11-01T17:51:20.4782501Z     >>>        self.fc2 = nn.Linear()
2024-11-01T17:51:20.4782930Z     >>>        self.relu = nn.Linear()
2024-11-01T17:51:20.4783323Z     >>>
2024-11-01T17:51:20.4783622Z     >>>     def forward(self, input):
2024-11-01T17:51:20.4784162Z     >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2024-11-01T17:51:20.4784573Z 
2024-11-01T17:51:20.4784578Z 
2024-11-01T17:51:20.4784775Z     >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2024-11-01T17:51:20.4785285Z     >>> sharding_plan = ShardingPlan(
2024-11-01T17:51:20.4785788Z     >>>    plan={
2024-11-01T17:51:20.4786117Z     >>>        "fc1.weight": spec1,
2024-11-01T17:51:20.4786537Z     >>>        "fc2.weight": spec2
2024-11-01T17:51:20.4786920Z     >>>    },
2024-11-01T17:51:20.4787218Z     >>>    output_plan={
2024-11-01T17:51:20.4787585Z     >>>        "fc2": output_spec
2024-11-01T17:51:20.4787947Z     >>>    },
2024-11-01T17:51:20.4788274Z     >>>    return_local_tensor=["fc2"]
2024-11-01T17:51:20.4788681Z     >>> )
2024-11-01T17:51:20.4788835Z 
2024-11-01T17:51:20.4789287Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.4789799Z 
2024-11-01T17:51:20.5541450Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72.
2024-11-01T17:51:20.5543284Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5543935Z 
2024-11-01T17:51:20.5544166Z Run post-localSGD algorithm.
2024-11-01T17:51:20.5544506Z 
2024-11-01T17:51:20.5544922Z This DDP communication hook is used for running post-localSGD algorithm,
2024-11-01T17:51:20.5545746Z by combining with a model averaging component (e.g.,
2024-11-01T17:51:20.5546740Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2024-11-01T17:51:20.5547639Z that runs after the optimizer step.
2024-11-01T17:51:20.5547934Z 
2024-11-01T17:51:20.5548048Z Args:
2024-11-01T17:51:20.5548670Z     state (PostLocalSGDState): State information to run post-localSGD.
2024-11-01T17:51:20.5549628Z         Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2024-11-01T17:51:20.5551102Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2024-11-01T17:51:20.5552425Z         Note that since DDP comm hook only supports single process single device mode,
2024-11-01T17:51:20.5553278Z         only exactly one tensor is stored in this bucket.
2024-11-01T17:51:20.5553663Z 
2024-11-01T17:51:20.5553935Z Returns:
2024-11-01T17:51:20.5554484Z     Future handler of the communication, which updates the gradients in place.
2024-11-01T17:51:20.5555059Z 
2024-11-01T17:51:20.5555275Z Example::
2024-11-01T17:51:20.5555599Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.5556532Z     >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2024-11-01T17:51:20.5557313Z                               start_localSGD_iter=10)
2024-11-01T17:51:20.5557979Z     >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:20.5558978Z     >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2024-11-01T17:51:20.5560334Z     >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2024-11-01T17:51:20.5561093Z 
2024-11-01T17:51:20.5561619Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.5562146Z 
2024-11-01T17:51:20.5597225Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342.
2024-11-01T17:51:20.5598857Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5599412Z 
2024-11-01T17:51:20.5599555Z Implement PowerSGD algorithm.
2024-11-01T17:51:20.5599835Z 
2024-11-01T17:51:20.5600149Z This DDP communication hook implements PowerSGD gradient compression
2024-11-01T17:51:20.5600976Z algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2024-11-01T17:51:20.5601815Z Once gradient tensors are aggregated across all workers, this hook applies
2024-11-01T17:51:20.5602462Z compression as follows:
2024-11-01T17:51:20.5602695Z 
2024-11-01T17:51:20.5603645Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2024-11-01T17:51:20.5604471Z 
2024-11-01T17:51:20.5605187Z     1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2024-11-01T17:51:20.5605973Z 
2024-11-01T17:51:20.5606793Z     1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2024-11-01T17:51:20.5607585Z 
2024-11-01T17:51:20.5607726Z 2. Handles uncompressed tensors:
2024-11-01T17:51:20.5608021Z 
2024-11-01T17:51:20.5608745Z     2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2024-11-01T17:51:20.5609641Z 
2024-11-01T17:51:20.5610132Z     2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2024-11-01T17:51:20.5610783Z 
2024-11-01T17:51:20.5611134Z 3. Handles the tensors that should be compressed by PowerSGD compression:
2024-11-01T17:51:20.5611637Z 
2024-11-01T17:51:20.5612124Z     3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2024-11-01T17:51:20.5613154Z     such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2024-11-01T17:51:20.5613798Z 
2024-11-01T17:51:20.5614024Z     3.2. Computes each P in Ps, which is equal to MQ;
2024-11-01T17:51:20.5614404Z 
2024-11-01T17:51:20.5614544Z     3.3. Allreduces Ps as a batch;
2024-11-01T17:51:20.5614841Z 
2024-11-01T17:51:20.5614992Z     3.4. Orthogonalizes each P in Ps;
2024-11-01T17:51:20.5615308Z 
2024-11-01T17:51:20.5615601Z     3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2024-11-01T17:51:20.5616075Z 
2024-11-01T17:51:20.5616213Z     3.6. Allreduces Qs as a batch;
2024-11-01T17:51:20.5616491Z 
2024-11-01T17:51:20.5616958Z     3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2024-11-01T17:51:20.5617572Z 
2024-11-01T17:51:20.5618175Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2024-11-01T17:51:20.5619345Z This not only gives the user more control over the tradeoff between speedup and accuracy,
2024-11-01T17:51:20.5620551Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2024-11-01T17:51:20.5621350Z 
2024-11-01T17:51:20.5621573Z Args:
2024-11-01T17:51:20.5622334Z     state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2024-11-01T17:51:20.5623628Z         To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2024-11-01T17:51:20.5624466Z         and ``min_compression_rate``.
2024-11-01T17:51:20.5625495Z     bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2024-11-01T17:51:20.5626643Z         Note that since DDP comm hook only supports single process single device mode,
2024-11-01T17:51:20.5627398Z         only exactly one tensor is stored in this bucket.
2024-11-01T17:51:20.5627792Z 
2024-11-01T17:51:20.5627898Z Returns:
2024-11-01T17:51:20.5628414Z     Future handler of the communication, which updates the gradients in place.
2024-11-01T17:51:20.5628945Z 
2024-11-01T17:51:20.5629066Z Example::
2024-11-01T17:51:20.5629362Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.5629975Z     >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2024-11-01T17:51:20.5630755Z                               start_powerSGD_iter=10, min_compression_rate=0.5)
2024-11-01T17:51:20.5631508Z     >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2024-11-01T17:51:20.5631909Z 
2024-11-01T17:51:20.5632340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.5632866Z 
2024-11-01T17:51:20.5639730Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36.
2024-11-01T17:51:20.5641381Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5641957Z 
2024-11-01T17:51:20.5642274Z Averages parameters periodically after the warm-up stage.
2024-11-01T17:51:20.5642686Z 
2024-11-01T17:51:20.5643166Z This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2024-11-01T17:51:20.5643998Z by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2024-11-01T17:51:20.5644769Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2024-11-01T17:51:20.5645265Z 
2024-11-01T17:51:20.5645365Z Args:
2024-11-01T17:51:20.5645755Z     period (int): The number of steps per model averaging.
2024-11-01T17:51:20.5646560Z                   Usually the period should be greater than ``1`` to reduce the communication cost.
2024-11-01T17:51:20.5647322Z                   Otherwise, only DDP needs to be used.
2024-11-01T17:51:20.5648064Z     warmup_steps (int): The number of warm-up steps. During this stage,
2024-11-01T17:51:20.5648705Z                         model averaging is skipped.
2024-11-01T17:51:20.5649373Z     process_group: The process group to be used for all-reduce.
2024-11-01T17:51:20.5650016Z                    If ``None``, the default process group, which
2024-11-01T17:51:20.5650682Z                    is created by :func:`torch.distributed.init_process_group`,
2024-11-01T17:51:20.5651314Z                    will be used. (default: ``None``)
2024-11-01T17:51:20.5651650Z 
2024-11-01T17:51:20.5651785Z Example::
2024-11-01T17:51:20.5651943Z 
2024-11-01T17:51:20.5652135Z     >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.5652585Z     >>> import torch
2024-11-01T17:51:20.5652952Z     >>> import torch.distributed as dist
2024-11-01T17:51:20.5653718Z     >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2024-11-01T17:51:20.5654735Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2024-11-01T17:51:20.5655546Z     >>> import torch.nn as nn
2024-11-01T17:51:20.5656058Z     >>>
2024-11-01T17:51:20.5656771Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2024-11-01T17:51:20.5657457Z     >>> torch.cuda.set_device(rank)
2024-11-01T17:51:20.5657935Z     >>> module = nn.Linear(1, 1, bias=False).cuda()
2024-11-01T17:51:20.5658641Z     >>> model = nn.parallel.DistributedDataParallel(
2024-11-01T17:51:20.5659235Z     >>>    module, device_ids=[rank], output_device=rank
2024-11-01T17:51:20.5659701Z     >>> )
2024-11-01T17:51:20.5660153Z     >>> # Register a post-localSGD communication hook.
2024-11-01T17:51:20.5660943Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2024-11-01T17:51:20.5661755Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:20.5662254Z     >>>
2024-11-01T17:51:20.5662802Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2024-11-01T17:51:20.5663604Z     >>> # After 100 steps, run model averaging every 4 steps.
2024-11-01T17:51:20.5664485Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2024-11-01T17:51:20.5665472Z     >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2024-11-01T17:51:20.5666130Z     >>> for step in range(0, 200):
2024-11-01T17:51:20.5666550Z     >>>    optimizer.zero_grad()
2024-11-01T17:51:20.5666975Z     >>>    loss = loss_fn(output, labels)
2024-11-01T17:51:20.5667402Z     >>>    loss.backward()
2024-11-01T17:51:20.5667750Z     >>>    optimizer.step()
2024-11-01T17:51:20.5668272Z     >>>    # Will average model parameters globally every 4 steps. Thus,
2024-11-01T17:51:20.5669092Z     >>>    # inter-node communication only occurs every 4 iterations after
2024-11-01T17:51:20.5669723Z     >>>    # the initial ``warmup_steps`` period.
2024-11-01T17:51:20.5670354Z     >>>    averager.average_parameters(model.parameters())
2024-11-01T17:51:20.5670745Z 
2024-11-01T17:51:20.5671183Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.5671696Z 
2024-11-01T17:51:20.5673098Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18.
2024-11-01T17:51:20.5674982Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5675512Z 
2024-11-01T17:51:20.5675985Z Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2024-11-01T17:51:20.5676607Z 
2024-11-01T17:51:20.5677057Z Process groups of different sizes are organized in a hierarchy, and they average parameters
2024-11-01T17:51:20.5678024Z by using different periods concurrently after the warm-up stage.
2024-11-01T17:51:20.5679089Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2024-11-01T17:51:20.5680447Z that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2024-11-01T17:51:20.5681642Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2024-11-01T17:51:20.5682832Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2024-11-01T17:51:20.5684054Z Similarly, the process groups within this class do not have such an intra-machine process
2024-11-01T17:51:20.5685115Z subgroup, which should be embedded by the post-local SGD communication hook instead.
2024-11-01T17:51:20.5685697Z 
2024-11-01T17:51:20.5685798Z Args:
2024-11-01T17:51:20.5686325Z     period_group_size_dict: An ordered dict mapping keys of model averaging period to
2024-11-01T17:51:20.5687170Z                             process group size, used for initializing process groups of
2024-11-01T17:51:20.5687984Z                             different sizes in a hierarchy to average parameters concurrently.
2024-11-01T17:51:20.5688799Z                             Particularly, at each iteration, there will be at most a single
2024-11-01T17:51:20.5689719Z                             process group that runs averaging -- the period of such group should
2024-11-01T17:51:20.5690541Z                             have the largest period which the current step can be divided by.
2024-11-01T17:51:20.5691373Z                             For example, if the dict has three keys: 2, 4, and 8,
2024-11-01T17:51:20.5692106Z                             then this means totally three process groups will be created to
2024-11-01T17:51:20.5692890Z                             average parameters every 2, 4, and 8 iterations, respectively.
2024-11-01T17:51:20.5693673Z                             At the 4th iteration, only the second process group will run
2024-11-01T17:51:20.5694399Z                             averaging, because the first process group should be a
2024-11-01T17:51:20.5695172Z                             subset of the second process group, and no need to execute the first
2024-11-01T17:51:20.5695841Z                             process group redundantly.
2024-11-01T17:51:20.5705198Z                             On the other hand, the third process group can only be triggered
2024-11-01T17:51:20.5706059Z                             every 8 iterations, so it will not be triggered at the 4th iteration.
2024-11-01T17:51:20.5707492Z     warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2024-11-01T17:51:20.5708746Z     process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2024-11-01T17:51:20.5709801Z                                             If ``None``, the default process group, which is created
2024-11-01T17:51:20.5710573Z                                             by :func:`torch.distributed.init_process_group`, will be used.
2024-11-01T17:51:20.5711424Z                                             (default: ``None``)
2024-11-01T17:51:20.5711780Z 
2024-11-01T17:51:20.5711899Z Example::
2024-11-01T17:51:20.5712291Z     >>> # xdoctest: +SKIP('undefined rank')
2024-11-01T17:51:20.5712790Z     >>> from collections import OrderedDict
2024-11-01T17:51:20.5713242Z     >>> import torch
2024-11-01T17:51:20.5713611Z     >>> import torch.distributed as dist
2024-11-01T17:51:20.5714399Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2024-11-01T17:51:20.5715101Z     >>>     PostLocalSGDState,
2024-11-01T17:51:20.5715499Z     >>>     post_localSGD_hook,
2024-11-01T17:51:20.5715872Z     >>> )
2024-11-01T17:51:20.5716561Z     >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2024-11-01T17:51:20.5717389Z     >>> import torch.nn as nn
2024-11-01T17:51:20.5717741Z     >>>
2024-11-01T17:51:20.5718142Z     >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2024-11-01T17:51:20.5718702Z     >>> torch.cuda.set_device(rank)
2024-11-01T17:51:20.5719187Z     >>> module = nn.Linear(1, 1, bias=False).to(rank)
2024-11-01T17:51:20.5719769Z     >>> model = nn.parallel.DistributedDataParallel(
2024-11-01T17:51:20.5720346Z     >>>    module, device_ids=[rank], output_device=rank
2024-11-01T17:51:20.5720823Z     >>> )
2024-11-01T17:51:20.5721263Z     >>> # Register a post-localSGD communication hook.
2024-11-01T17:51:20.5722165Z     >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2024-11-01T17:51:20.5722922Z     >>> subgroup, _ = dist.new_subgroups()
2024-11-01T17:51:20.5723686Z     >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2024-11-01T17:51:20.5724501Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:20.5724999Z     >>>
2024-11-01T17:51:20.5725557Z     >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2024-11-01T17:51:20.5726322Z     >>> # the 16 processes every 16 iterations.
2024-11-01T17:51:20.5726942Z     >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2024-11-01T17:51:20.5727711Z     >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2024-11-01T17:51:20.5728677Z     >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2024-11-01T17:51:20.5729838Z     >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2024-11-01T17:51:20.5730637Z     >>> # After 100 steps, run model averaging at two levels.
2024-11-01T17:51:20.5731169Z     >>> for step in range(0, 200):
2024-11-01T17:51:20.5731589Z     >>>    optimizer.zero_grad()
2024-11-01T17:51:20.5732021Z     >>>    loss = loss_fn(output, labels)
2024-11-01T17:51:20.5732445Z     >>>    loss.backward()
2024-11-01T17:51:20.5732815Z     >>>    optimizer.step()
2024-11-01T17:51:20.5733278Z     >>>    # Average parameters after ``optimizer.step()``.
2024-11-01T17:51:20.5734186Z     >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2024-11-01T17:51:20.5734996Z     >>>    averager.average_parameters(model.parameters())
2024-11-01T17:51:20.5735383Z 
2024-11-01T17:51:20.5735500Z .. warning ::
2024-11-01T17:51:20.5736079Z     The last group size in the dict must be the size of the provided ``process_group``,
2024-11-01T17:51:20.5736986Z     which indicates model averaging at the highest level of the hierarchy.
2024-11-01T17:51:20.5737939Z     If ``process_group`` is not provided, then the last group size should be equal to the world size.
2024-11-01T17:51:20.5738560Z 
2024-11-01T17:51:20.5738686Z .. warning ::
2024-11-01T17:51:20.5739185Z     `HierarchicalModelAverager` is experimental and subject to change.
2024-11-01T17:51:20.5739670Z 
2024-11-01T17:51:20.5740110Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.5740627Z 
2024-11-01T17:51:20.5870185Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40.
2024-11-01T17:51:20.5871866Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5872474Z 
2024-11-01T17:51:20.5873199Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2024-11-01T17:51:20.5874476Z on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2024-11-01T17:51:20.5875028Z 
2024-11-01T17:51:20.5875257Z . N.B. Intended to be used with DynamicMetaLoadPlanner
2024-11-01T17:51:20.5875669Z 
2024-11-01T17:51:20.5875788Z .. warning::
2024-11-01T17:51:20.5876204Z     Current implementation only supports loading Tensors.
2024-11-01T17:51:20.5876600Z 
2024-11-01T17:51:20.5876763Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.5877186Z >>> sd = {"mode": model}
2024-11-01T17:51:20.5877531Z >>> dcp.load(
2024-11-01T17:51:20.5877818Z >>>    sd,
2024-11-01T17:51:20.5878205Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2024-11-01T17:51:20.5878751Z >>>    planner=DynamicMetaLoadPlanner(),
2024-11-01T17:51:20.5879229Z >>>    checkpoint_id="path_to_model.pt"
2024-11-01T17:51:20.5879626Z >>> )
2024-11-01T17:51:20.5879782Z 
2024-11-01T17:51:20.5880234Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.5880760Z 
2024-11-01T17:51:20.5881866Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151.
2024-11-01T17:51:20.5883421Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5883951Z 
2024-11-01T17:51:20.5884476Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2024-11-01T17:51:20.5885741Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2024-11-01T17:51:20.5886554Z metadata file, like Torch Save files.
2024-11-01T17:51:20.5886858Z 
2024-11-01T17:51:20.5887120Z . N.B. Intended to be used with BroadcastingTorchSaveReader
2024-11-01T17:51:20.5887566Z 
2024-11-01T17:51:20.5887680Z .. warning::
2024-11-01T17:51:20.5888094Z     Current implementation only supports loading Tensors.
2024-11-01T17:51:20.5888511Z 
2024-11-01T17:51:20.5888793Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.5889230Z >>> sd = {"mode": model}
2024-11-01T17:51:20.5889571Z >>> dcp.load(
2024-11-01T17:51:20.5889840Z >>>    sd,
2024-11-01T17:51:20.5890220Z >>>    storage_reader=BroadcastingTorchSaveReader(),
2024-11-01T17:51:20.5890772Z >>>    planner=DynamicMetaLoadPlanner(),
2024-11-01T17:51:20.5891246Z >>>    checkpoint_id="path_to_model.pt"
2024-11-01T17:51:20.5891661Z >>> )
2024-11-01T17:51:20.5891808Z 
2024-11-01T17:51:20.5892236Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.5892762Z 
2024-11-01T17:51:20.5929261Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220.
2024-11-01T17:51:20.5930834Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5931386Z 
2024-11-01T17:51:20.5931710Z Load a state_dict in conjunction with FSDP sharded optimizer state.
2024-11-01T17:51:20.5932197Z 
2024-11-01T17:51:20.5932429Z This is the current recommended way to checkpoint FSDP.
2024-11-01T17:51:20.5932957Z >>> # xdoctest: +SKIP
2024-11-01T17:51:20.5933386Z >>> import torch.distributed.checkpoint as dist_cp
2024-11-01T17:51:20.5933873Z >>> # Save
2024-11-01T17:51:20.5934153Z >>> model: torch.nn.Model
2024-11-01T17:51:20.5934550Z >>> optim_params = model.parameters()
2024-11-01T17:51:20.5935052Z >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2024-11-01T17:51:20.5935524Z >>> # Save
2024-11-01T17:51:20.5936003Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2024-11-01T17:51:20.5936728Z >>>     state_dict = {
2024-11-01T17:51:20.5937170Z >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2024-11-01T17:51:20.5937701Z >>>         "model": model.state_dict()
2024-11-01T17:51:20.5938112Z >>>     }
2024-11-01T17:51:20.5938410Z >>>     dist_cp.save_state_dict(
2024-11-01T17:51:20.5938805Z >>>         state_dict=optim_state,
2024-11-01T17:51:20.5939350Z >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2024-11-01T17:51:20.5939954Z >>>         planner=dist_cp.DefaultSavePlanner(),
2024-11-01T17:51:20.5940414Z >>>     )
2024-11-01T17:51:20.5940673Z >>>
2024-11-01T17:51:20.5940925Z >>> # Load
2024-11-01T17:51:20.5941402Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2024-11-01T17:51:20.5942079Z >>>     model_state_dict = model_tp.state_dict()
2024-11-01T17:51:20.5942546Z >>>     checkpoint = {
2024-11-01T17:51:20.5942901Z >>>         "model": model_state_dict
2024-11-01T17:51:20.5943302Z >>>     }
2024-11-01T17:51:20.5943584Z >>>     dist_cp.load_state_dict(
2024-11-01T17:51:20.5943990Z >>>         state_dict=checkpoint,
2024-11-01T17:51:20.5944539Z >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2024-11-01T17:51:20.5945155Z >>>         planner=dist_cp.DefaultLoadPlanner(),
2024-11-01T17:51:20.5945610Z >>>     )
2024-11-01T17:51:20.5945967Z >>>     model.load_state_dict(checkpoint["model_state"])
2024-11-01T17:51:20.5946454Z >>>
2024-11-01T17:51:20.5946853Z >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2024-11-01T17:51:20.5947392Z >>>         model_state_dict,
2024-11-01T17:51:20.5947785Z >>>         optimizer_key="optimizer",
2024-11-01T17:51:20.5948324Z >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2024-11-01T17:51:20.5948846Z >>>     )
2024-11-01T17:51:20.5949108Z >>>
2024-11-01T17:51:20.5949455Z >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2024-11-01T17:51:20.5950000Z >>>        model, optim, optim_state["optimizer"]
2024-11-01T17:51:20.5950453Z >>>     )
2024-11-01T17:51:20.5950700Z >>>
2024-11-01T17:51:20.5951007Z >>>     optim.load_state_dict(flattened_osd)
2024-11-01T17:51:20.5951348Z 
2024-11-01T17:51:20.5951795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.5952394Z 
2024-11-01T17:51:20.5955508Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110.
2024-11-01T17:51:20.5957173Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.5957764Z 
2024-11-01T17:51:20.5958169Z Abstract class defining the protocol used by save_state_dict to plan the save process.
2024-11-01T17:51:20.5958747Z 
2024-11-01T17:51:20.5959173Z SavePlanners are stateful objects that can be used to customize the whole save process.
2024-11-01T17:51:20.5959765Z 
2024-11-01T17:51:20.5960188Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2024-11-01T17:51:20.5960943Z will be visible to the whole process.
2024-11-01T17:51:20.5961249Z 
2024-11-01T17:51:20.5961703Z A planner subclass can expect the following sequence of calls during save_state_dict:
2024-11-01T17:51:20.5962402Z 
2024-11-01T17:51:20.5962755Z 1) set_up_planner - called on all ranks.
2024-11-01T17:51:20.5963247Z     Signals the start of a checkpoint save.
2024-11-01T17:51:20.5963779Z 
2024-11-01T17:51:20.5964190Z 2) create_local_plan - called on all ranks.
2024-11-01T17:51:20.5965120Z     Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2024-11-01T17:51:20.5965707Z 
2024-11-01T17:51:20.5966043Z 3) create_global_plan - called on the coordinator rank only.
2024-11-01T17:51:20.5966754Z     Takes the SavePlan from all ranks and make any global decision.
2024-11-01T17:51:20.5967209Z 
2024-11-01T17:51:20.5967411Z 4) finish_plan - called on all ranks.
2024-11-01T17:51:20.5968039Z     This gives each rank a chance to adjust to global planning decisions.
2024-11-01T17:51:20.5968634Z 
2024-11-01T17:51:20.5968906Z 5) resolve_data - called multiple times on each rank
2024-11-01T17:51:20.5969592Z     Lookups a value on the `state_dict` for the storage layer to write.
2024-11-01T17:51:20.5970063Z 
2024-11-01T17:51:20.5970506Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2024-11-01T17:51:20.5971385Z most changes can be expressed by changes in a single method.
2024-11-01T17:51:20.5971811Z 
2024-11-01T17:51:20.5971991Z There are 3 usual patterns of extension:
2024-11-01T17:51:20.5972309Z 
2024-11-01T17:51:20.5972672Z Rewriting state_dict. This is the simplest way to extend the save process as it
2024-11-01T17:51:20.5973603Z doesn't requite understanding the intrincacies of how SavePlan works:
2024-11-01T17:51:20.5974093Z 
2024-11-01T17:51:20.5974244Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.5974745Z >>> class RenamePlanner(DefaultSavePlanner):
2024-11-01T17:51:20.5975224Z >>>     def set_up_planner(
2024-11-01T17:51:20.5975588Z >>>         self,
2024-11-01T17:51:20.5975913Z >>>         state_dict: STATE_DICT_TYPE,
2024-11-01T17:51:20.5976396Z >>>         storage_meta: Optional[StorageMeta],
2024-11-01T17:51:20.5976875Z >>>         is_coordinator: bool,
2024-11-01T17:51:20.5977302Z >>>     ) -> None:
2024-11-01T17:51:20.5977654Z >>>         # prefix all keys with `foo_``
2024-11-01T17:51:20.5978414Z >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2024-11-01T17:51:20.5979038Z 
2024-11-01T17:51:20.5979524Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2024-11-01T17:51:20.5980196Z 
2024-11-01T17:51:20.5980347Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.5980830Z >>> class FP16Planner(DefaultSavePlanner):
2024-11-01T17:51:20.5981310Z >>>     def create_local_plan(self):
2024-11-01T17:51:20.5981778Z >>>         plan = super().create_local_plan()
2024-11-01T17:51:20.5982230Z >>>         for p in plan:
2024-11-01T17:51:20.5982631Z >>>             if p.tensor_data is not None:
2024-11-01T17:51:20.5983195Z >>>                 p.tensor_data.properties.dtype = torch.float16
2024-11-01T17:51:20.5983722Z >>>         return plan
2024-11-01T17:51:20.5984053Z >>>
2024-11-01T17:51:20.5984343Z >>>     def resolve_data(self, write_item):
2024-11-01T17:51:20.5984918Z >>>         item = super().resolve_data(write_item)
2024-11-01T17:51:20.5985663Z >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2024-11-01T17:51:20.5986248Z 
2024-11-01T17:51:20.5986847Z Using the global planning step to make central decisions that can't be made individually by each rank
2024-11-01T17:51:20.5987516Z 
2024-11-01T17:51:20.5987680Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.5988145Z >>> from itertools import zip_longest
2024-11-01T17:51:20.5988591Z >>> from dataclasses import replace
2024-11-01T17:51:20.5989116Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2024-11-01T17:51:20.5990037Z >>>     # This uses the default local plan behavior of having all non-sharded writes in rank 0
2024-11-01T17:51:20.5990858Z >>>     # This sample doesn't handle ShardedTensors
2024-11-01T17:51:20.5991403Z >>>     def create_global_plan(self, all_plans):
2024-11-01T17:51:20.5991970Z >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2024-11-01T17:51:20.5992479Z >>>         items_per_rank = [
2024-11-01T17:51:20.5992947Z >>>             [item for item in items if item is not None]
2024-11-01T17:51:20.5993551Z >>>             for items in zip(*zip_longest(*iters), strict=True)
2024-11-01T17:51:20.5994199Z >>>         ]
2024-11-01T17:51:20.5994496Z >>>         all_plans = [
2024-11-01T17:51:20.5994880Z >>>             replace(plan, items=items)
2024-11-01T17:51:20.5995470Z >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2024-11-01T17:51:20.5996042Z >>>         ]
2024-11-01T17:51:20.5996492Z >>>         return super().create_global_plan(all_plans)
2024-11-01T17:51:20.5996851Z 
2024-11-01T17:51:20.5997250Z Finally, some planners need to save additional metadata in the checkpoint, this is
2024-11-01T17:51:20.5998192Z accomplished by having each rank contribute their data items in the local plan and
2024-11-01T17:51:20.5998911Z the global planner aggregate them:
2024-11-01T17:51:20.5999194Z 
2024-11-01T17:51:20.5999345Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.5999874Z >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2024-11-01T17:51:20.6000503Z >>>     def create_local_plan(self) -> SavePlan:
2024-11-01T17:51:20.6001025Z >>>         plan = super().create_local_plan()
2024-11-01T17:51:20.6001658Z >>>         return replace(plan, planner_data="per-rank-data")
2024-11-01T17:51:20.6002145Z >>>
2024-11-01T17:51:20.6002819Z >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2024-11-01T17:51:20.6003694Z >>>         global_plan, metadata = super().create_global_plan(all_plans)
2024-11-01T17:51:20.6004378Z >>>         merged_data = [p.planner_data for p in global_plan]
2024-11-01T17:51:20.6005019Z >>>         metadata = replace(metadata, planner_data=merged_data)
2024-11-01T17:51:20.6005582Z >>>         return global_plan, metadata
2024-11-01T17:51:20.6005889Z 
2024-11-01T17:51:20.6006310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.6007126Z 
2024-11-01T17:51:20.6008177Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=272.
2024-11-01T17:51:20.6009619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.6010147Z 
2024-11-01T17:51:20.6010547Z Abstract class defining the protocol used by load_state_dict to plan the load process.
2024-11-01T17:51:20.6011135Z 
2024-11-01T17:51:20.6011539Z LoadPlanner are stateful objects that can be used to customize the whole load process.
2024-11-01T17:51:20.6012138Z 
2024-11-01T17:51:20.6012550Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2024-11-01T17:51:20.6013297Z will be visible to the whole process.
2024-11-01T17:51:20.6013609Z 
2024-11-01T17:51:20.6014008Z A planner subclass can expect the following sequence of calls during load_state_dict:
2024-11-01T17:51:20.6014583Z 
2024-11-01T17:51:20.6014942Z 1) set_up_planner - called on all ranks.
2024-11-01T17:51:20.6015449Z     Signals the start of loading a checkpoint.
2024-11-01T17:51:20.6015791Z 
2024-11-01T17:51:20.6016005Z 2) create_local_plan - called on all ranks.
2024-11-01T17:51:20.6016756Z     Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2024-11-01T17:51:20.6017351Z 
2024-11-01T17:51:20.6017671Z 3) create_global_plan - called on the coordinator rank only.
2024-11-01T17:51:20.6018382Z     Takes the LoadPlan from all ranks and make any global decision.
2024-11-01T17:51:20.6018827Z 
2024-11-01T17:51:20.6019101Z 4) load_bytes - called multiple times on each rank
2024-11-01T17:51:20.6019774Z     This is called once per non-tensor value in state_dict.
2024-11-01T17:51:20.6020175Z 
2024-11-01T17:51:20.6020577Z 5) resolve_tensor and commit_tensor - called multiple times on each rank
2024-11-01T17:51:20.6021321Z     They are called in pair for each Tensor value in state_dict.
2024-11-01T17:51:20.6021775Z 
2024-11-01T17:51:20.6022201Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2024-11-01T17:51:20.6023067Z most changes can be expressed by changes in a single method.
2024-11-01T17:51:20.6023506Z 
2024-11-01T17:51:20.6023674Z There are two usual patterns of extension:
2024-11-01T17:51:20.6024007Z 
2024-11-01T17:51:20.6024385Z Rewriting state_dict. This is the simplest way to extend the load process as it
2024-11-01T17:51:20.6025357Z doesn't requite understanding the intrincacies of how LoadPlan works. We need
2024-11-01T17:51:20.6026302Z to keep a reference to the original state_dict as load happens in place so
2024-11-01T17:51:20.6026973Z we need to be able to perform it in place
2024-11-01T17:51:20.6027311Z 
2024-11-01T17:51:20.6027459Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.6027963Z >>> class RenamePlanner(DefaultLoadPlanner):
2024-11-01T17:51:20.6028443Z >>>     def set_up_planner(
2024-11-01T17:51:20.6028801Z >>>         self,
2024-11-01T17:51:20.6029132Z >>>         state_dict: STATE_DICT_TYPE,
2024-11-01T17:51:20.6029565Z >>>         metadata: Metadata,
2024-11-01T17:51:20.6029965Z >>>         is_coordinator: bool,
2024-11-01T17:51:20.6030387Z >>>     ) -> None:
2024-11-01T17:51:20.6030755Z >>>         self.original_state_dict = state_dict
2024-11-01T17:51:20.6031347Z >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2024-11-01T17:51:20.6031859Z >>>
2024-11-01T17:51:20.6032167Z >>>         if self.flatten_sharded_tensors:
2024-11-01T17:51:20.6032723Z >>>             state_dict = _flatten_sharded_tensors(state_dict)
2024-11-01T17:51:20.6033213Z >>>
2024-11-01T17:51:20.6033504Z >>>         if self.flatten_state_dict:
2024-11-01T17:51:20.6034158Z >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2024-11-01T17:51:20.6034702Z >>>
2024-11-01T17:51:20.6034997Z >>>         self.state_dict = state_dict
2024-11-01T17:51:20.6035445Z >>>         self.metadata = metadata
2024-11-01T17:51:20.6035917Z >>>         self.is_coordinator = is_coordinator
2024-11-01T17:51:20.6036346Z >>>
2024-11-01T17:51:20.6036668Z >>>     def load_bytes(self, read_item, value):
2024-11-01T17:51:20.6037149Z >>>         # Remove the "foo_" prefix
2024-11-01T17:51:20.6037904Z >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2024-11-01T17:51:20.6038520Z 
2024-11-01T17:51:20.6038526Z 
2024-11-01T17:51:20.6038896Z Modifying resolve_tensor and commit_tensor to handle load time transformation.
2024-11-01T17:51:20.6039423Z 
2024-11-01T17:51:20.6039585Z >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:20.6040102Z >>> class MetaModelMaterialize(DefaultSavePlanner):
2024-11-01T17:51:20.6040662Z >>>     def resolve_tensor(self, read_item):
2024-11-01T17:51:20.6041181Z >>>         tensor = super().resolve_tensor(read_item)
2024-11-01T17:51:20.6041748Z >>>         return torch.empty_like(tensor, device="cpu")
2024-11-01T17:51:20.6042226Z >>>
2024-11-01T17:51:20.6042552Z >>>     def commit_tensor(self, read_item, tensor):
2024-11-01T17:51:20.6043189Z >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2024-11-01T17:51:20.6043588Z 
2024-11-01T17:51:20.6044021Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.6044547Z 
2024-11-01T17:51:20.6160406Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61.
2024-11-01T17:51:20.6162536Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.6163083Z 
2024-11-01T17:51:20.6163288Z Load a distributed ``state_dict`` in SPMD style.
2024-11-01T17:51:20.6163665Z 
2024-11-01T17:51:20.6163929Z Each rank will try to read the least amount of data necessary
2024-11-01T17:51:20.6164705Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor`
2024-11-01T17:51:20.6165576Z or :class:`DTensor` instances, each rank only reads data for their local shards.
2024-11-01T17:51:20.6166107Z 
2024-11-01T17:51:20.6166498Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2024-11-01T17:51:20.6167428Z load will first call ``state_dict`` before attempting deserialization, followed by
2024-11-01T17:51:20.6168201Z ``load_state_dict`` once the deserialization is complete.
2024-11-01T17:51:20.6169073Z For each non-``Stateful`` object, load will deserailize the object, and then replace
2024-11-01T17:51:20.6169852Z it in the ``state_dict`` with the deserialized object.
2024-11-01T17:51:20.6170236Z 
2024-11-01T17:51:20.6170376Z .. warning::
2024-11-01T17:51:20.6171012Z     All tensors in ``state_dict`` must be allocated on their
2024-11-01T17:51:20.6171633Z     destination device *prior to* calling this function.
2024-11-01T17:51:20.6172032Z 
2024-11-01T17:51:20.6172451Z     All non-tensor data is loaded using `torch.load()` and modified in place
2024-11-01T17:51:20.6173078Z     on state_dict.
2024-11-01T17:51:20.6173285Z 
2024-11-01T17:51:20.6173401Z .. warning::
2024-11-01T17:51:20.6173882Z     Users must call `load_state_dict` on the root module to ensure load
2024-11-01T17:51:20.6174675Z     pos-processing and non-tensor data properly propagates.
2024-11-01T17:51:20.6175092Z 
2024-11-01T17:51:20.6175208Z .. note:
2024-11-01T17:51:20.6175689Z     If no process group is initialized, this function will assume the intent
2024-11-01T17:51:20.6176531Z     is to load a checkpoint into the local process. This can be useful in the
2024-11-01T17:51:20.6177406Z     case of local inference, and when using regular Tensors (as opposed to DTensor
2024-11-01T17:51:20.6178073Z      or ShardedTensor)
2024-11-01T17:51:20.6178292Z 
2024-11-01T17:51:20.6178406Z .. note:
2024-11-01T17:51:20.6178745Z     Rank 0 is assumed to be the coordinator rank.
2024-11-01T17:51:20.6179108Z 
2024-11-01T17:51:20.6179207Z Args:
2024-11-01T17:51:20.6179576Z     state_dict (Dict[str, Any]): The state_dict to save.
2024-11-01T17:51:20.6180155Z     checkpoint_id (Union[str, os.PathLike, None]):
2024-11-01T17:51:20.6180852Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2024-11-01T17:51:20.6181657Z         depends on the storage. It can be a path to a folder or to a file.
2024-11-01T17:51:20.6182455Z         It can also be a key if the storage is a key-value store.
2024-11-01T17:51:20.6183003Z         (Default: ``None``)
2024-11-01T17:51:20.6183436Z     storage_reader (Optional[StorageReader]):
2024-11-01T17:51:20.6184075Z         Instance of StorageWriter used to perform reads. If this is not
2024-11-01T17:51:20.6184842Z         specified, DCP will automatically infer the reader based on the
2024-11-01T17:51:20.6185590Z         checkpoint_id. If checkpoint_id is also None, an exception will
2024-11-01T17:51:20.6186174Z         be raised. (Default: ``None``)
2024-11-01T17:51:20.6186623Z     planner (Optional[LoadPlanner]):
2024-11-01T17:51:20.6187229Z         Instance of LoadPlanner. If this is not specificed, the default
2024-11-01T17:51:20.6188146Z         planner will be used. (Default: ``None``)
2024-11-01T17:51:20.6188873Z     process_group (Optional[ProcessGroup]):
2024-11-01T17:51:20.6189948Z         ProcessGroup to be used for cross-rank synchronization.
2024-11-01T17:51:20.6190928Z         (Default: ``None``)
2024-11-01T17:51:20.6191291Z 
2024-11-01T17:51:20.6191397Z Returns:
2024-11-01T17:51:20.6191665Z     None.
2024-11-01T17:51:20.6191819Z 
2024-11-01T17:51:20.6191940Z Examples
2024-11-01T17:51:20.6192229Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.6192580Z     >>> my_model = MyModule()
2024-11-01T17:51:20.6193031Z     >>> optimizer = Adagrad(my_model.parameters())
2024-11-01T17:51:20.6193578Z     >>> model_state_dict = my_model.state_dict()
2024-11-01T17:51:20.6194459Z     >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1")
2024-11-01T17:51:20.6195059Z 
2024-11-01T17:51:20.6195291Z     >>> torch.distributed.checkpoint.load_state_dict(
2024-11-01T17:51:20.6195831Z     >>>     state_dict=model_state_dict,
2024-11-01T17:51:20.6196599Z     >>>     storage_reader=fs_storage_reader,
2024-11-01T17:51:20.6197037Z     >>> )
2024-11-01T17:51:20.6197280Z 
2024-11-01T17:51:20.6197793Z     >>> # module.load_state_dict() function might have customized steps
2024-11-01T17:51:20.6198605Z     >>> # to flush the state_dict, must call it to
2024-11-01T17:51:20.6199095Z     >>> # ensure correct behavior.
2024-11-01T17:51:20.6199543Z     >>> my_model.load_state_dict(model_state_dict)
2024-11-01T17:51:20.6199904Z 
2024-11-01T17:51:20.6200020Z .. note::
2024-11-01T17:51:20.6200480Z     load_state_dict uses collectives to coordinate reads across ranks.
2024-11-01T17:51:20.6201480Z     For NCCL-based process groups, internal tensor representations of
2024-11-01T17:51:20.6202303Z     objects must be moved to the GPU device before communication takes place.
2024-11-01T17:51:20.6203157Z     In this case, the device used is given by ``torch.cuda.current_device()``
2024-11-01T17:51:20.6204091Z     and it is the user's responsibility to ensure that this is set so that each
2024-11-01T17:51:20.6204899Z     rank has an individual GPU, via ``torch.cuda.set_device()``.
2024-11-01T17:51:20.6205348Z 
2024-11-01T17:51:20.6205769Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.6206296Z 
2024-11-01T17:51:20.6207585Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67.
2024-11-01T17:51:20.6209042Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.6209591Z 
2024-11-01T17:51:20.6209755Z Save a distributed model in SPMD style.
2024-11-01T17:51:20.6210088Z 
2024-11-01T17:51:20.6210353Z This function is different from ``torch.save()`` as it handles
2024-11-01T17:51:20.6211170Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2024-11-01T17:51:20.6211709Z 
2024-11-01T17:51:20.6212101Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2024-11-01T17:51:20.6212872Z save will call ``state_dict`` before serialization.
2024-11-01T17:51:20.6213248Z 
2024-11-01T17:51:20.6213359Z .. warning::
2024-11-01T17:51:20.6213886Z     There is no guarantees of Backwards Compatibility across PyTorch versions
2024-11-01T17:51:20.6214533Z     for saved state_dicts.
2024-11-01T17:51:20.6214772Z 
2024-11-01T17:51:20.6214897Z .. warning::
2024-11-01T17:51:20.6215388Z     If using the `process_group` argument, make sure that only its ranks
2024-11-01T17:51:20.6216173Z     call `save_state_dict` and that all data in state_dict belong to it.
2024-11-01T17:51:20.6216646Z 
2024-11-01T17:51:20.6216752Z .. note::
2024-11-01T17:51:20.6217388Z     When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2024-11-01T17:51:20.6218332Z     the shard_group should be calling `save_state_dict` and the corresponding process
2024-11-01T17:51:20.6219034Z     group needs to be passed in.
2024-11-01T17:51:20.6219459Z 
2024-11-01T17:51:20.6219577Z .. note::
2024-11-01T17:51:20.6220144Z     If no process group is available, this function assumes the intention is to save the
2024-11-01T17:51:20.6220851Z      state_dict in the local process.
2024-11-01T17:51:20.6221164Z 
2024-11-01T17:51:20.6221266Z .. note:
2024-11-01T17:51:20.6221618Z     Rank 0 is assumed to be the coordinator rank.
2024-11-01T17:51:20.6221972Z 
2024-11-01T17:51:20.6221991Z 
2024-11-01T17:51:20.6222094Z Args:
2024-11-01T17:51:20.6222461Z     state_dict (Dict[str, Any]): The state_dict to save.
2024-11-01T17:51:20.6223043Z     checkpoint_id (Union[str, os.PathLike, None]):
2024-11-01T17:51:20.6223724Z         The ID of this checkpoint instance. The meaning of the checkpoint_id
2024-11-01T17:51:20.6224529Z         depends on the storage. It can be a path to a folder or to a file.
2024-11-01T17:51:20.6225346Z         It can also be a key if the storage is a key-value store.
2024-11-01T17:51:20.6225894Z         (Default: ``None``)
2024-11-01T17:51:20.6226324Z     storage_writer (Optional[StorageWriter]):
2024-11-01T17:51:20.6226958Z         Instance of StorageWriter used to perform writes. If this is not
2024-11-01T17:51:20.6227730Z         specified, DCP will automatically infer the writer based on the
2024-11-01T17:51:20.6228485Z         checkpoint_id. If checkpoint_id is also None, an exception will
2024-11-01T17:51:20.6229085Z         be raised. (Default: ``None``)
2024-11-01T17:51:20.6229543Z     planner (Optional[SavePlanner]):
2024-11-01T17:51:20.6230141Z         Instance of SavePlanner. If this is not specificed, the default
2024-11-01T17:51:20.6230874Z         planner will be used. (Default: ``None``)
2024-11-01T17:51:20.6231399Z     process_group (Optional[ProcessGroup]):
2024-11-01T17:51:20.6232063Z         ProcessGroup to be used for cross-rank synchronization.
2024-11-01T17:51:20.6232609Z         (Default: ``None``)
2024-11-01T17:51:20.6232846Z 
2024-11-01T17:51:20.6232964Z Returns:
2024-11-01T17:51:20.6233348Z     Metadata: Metadata object for the saved checkpoint.
2024-11-01T17:51:20.6233736Z 
2024-11-01T17:51:20.6233913Z Example:
2024-11-01T17:51:20.6234218Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.6234587Z     >>> my_model = MyModule()
2024-11-01T17:51:20.6234838Z 
2024-11-01T17:51:20.6234998Z     >>> state_dict = {"model": my_model}
2024-11-01T17:51:20.6235301Z 
2024-11-01T17:51:20.6235748Z     >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1")
2024-11-01T17:51:20.6236533Z     >>> torch.distributed.checkpoint.save(
2024-11-01T17:51:20.6237009Z     >>>     state_dict=state_dict,
2024-11-01T17:51:20.6237459Z     >>>     storage_writer=fs_storage_writer,
2024-11-01T17:51:20.6237898Z     >>> )
2024-11-01T17:51:20.6238054Z 
2024-11-01T17:51:20.6238175Z .. note::
2024-11-01T17:51:20.6238645Z     save_state_dict uses collectives to coordinate writes across ranks.
2024-11-01T17:51:20.6239499Z     For NCCL-based process groups, internal tensor representations of
2024-11-01T17:51:20.6240338Z     objects must be moved to the GPU device before communication takes place.
2024-11-01T17:51:20.6241195Z     In this case, the device used is given by ``torch.cuda.current_device()``
2024-11-01T17:51:20.6242112Z     and it is the user's responsibility to ensure that this is set so that
2024-11-01T17:51:20.6242912Z     each rank has an individual GPU, via ``torch.cuda.set_device()``.
2024-11-01T17:51:20.6243377Z 
2024-11-01T17:51:20.6243824Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.6244337Z 
2024-11-01T17:51:20.6245398Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170.
2024-11-01T17:51:20.6246878Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.6247881Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2024-11-01T17:51:20.6248863Z     staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2024-11-01T17:51:20.6249568Z 
2024-11-01T17:51:20.6249685Z     .. warning::
2024-11-01T17:51:20.6250104Z         This feature is experimental and subject to change.
2024-11-01T17:51:20.6250494Z 
2024-11-01T17:51:20.6250612Z     Args:
2024-11-01T17:51:20.6251000Z         state_dict (Dict[str, Any]): The state_dict to save.
2024-11-01T17:51:20.6251581Z         checkpoint_id (Union[str, os.PathLike, None]):
2024-11-01T17:51:20.6252280Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2024-11-01T17:51:20.6253099Z             depends on the storage. It can be a path to a folder or to a file.
2024-11-01T17:51:20.6253923Z             It can also be a key if the storage is a key-value store.
2024-11-01T17:51:20.6254484Z             (Default: ``None``)
2024-11-01T17:51:20.6254925Z         storage_writer (Optional[StorageWriter]):
2024-11-01T17:51:20.6255670Z             Instance of StorageWriter used to perform 'stage' and  'save'. If
2024-11-01T17:51:20.6256508Z             this is not specified, DCP will automatically infer the writer based on the
2024-11-01T17:51:20.6257343Z             checkpoint_id. If checkpoint_id is also None, an exception will
2024-11-01T17:51:20.6257966Z             be raised. (Default: ``None``)
2024-11-01T17:51:20.6258438Z         planner (Optional[SavePlanner]):
2024-11-01T17:51:20.6259036Z             Instance of SavePlanner. If this is not specificed, the default
2024-11-01T17:51:20.6259698Z             planner will be used. (Default: ``None``)
2024-11-01T17:51:20.6260240Z         process_group (Optional[ProcessGroup]):
2024-11-01T17:51:20.6261021Z             ProcessGroup to be used for cross-rank synchronization.
2024-11-01T17:51:20.6261573Z             (Default: ``None``)
2024-11-01T17:51:20.6261835Z 
2024-11-01T17:51:20.6261955Z     Returns:
2024-11-01T17:51:20.6262421Z         Future: A future holding the resultant Metadata object from `save`.
2024-11-01T17:51:20.6262911Z 
2024-11-01T17:51:20.6263020Z     Example:
2024-11-01T17:51:20.6263332Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:20.6263721Z         >>> my_model = MyModule()
2024-11-01T17:51:20.6263992Z 
2024-11-01T17:51:20.6264162Z         >>> state_dict = {"model": my_model}
2024-11-01T17:51:20.6264487Z 
2024-11-01T17:51:20.6264931Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1")
2024-11-01T17:51:20.6265821Z         >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2024-11-01T17:51:20.6266432Z         >>>     state_dict=state_dict,
2024-11-01T17:51:20.6266923Z         >>>     storage_writer=fs_storage_writer,
2024-11-01T17:51:20.6267434Z         >>> )
2024-11-01T17:51:20.6267718Z         >>>
2024-11-01T17:51:20.6267998Z         >>> # ... do some work ...
2024-11-01T17:51:20.6268384Z         >>>
2024-11-01T17:51:20.6268697Z         >>> checkpoint_future.result()
2024-11-01T17:51:20.6269016Z 
2024-11-01T17:51:20.6269114Z     
2024-11-01T17:51:20.6269689Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.6270207Z 
2024-11-01T17:51:20.6303488Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91.
2024-11-01T17:51:20.6305078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.6305634Z 
2024-11-01T17:51:20.6305932Z Initialize rendezvous event object and record its operations.
2024-11-01T17:51:20.6306368Z 
2024-11-01T17:51:20.6306468Z Args:
2024-11-01T17:51:20.6307075Z     run_id (str): The run id of the rendezvous.
2024-11-01T17:51:20.6307646Z     message (str): The message describing the event.
2024-11-01T17:51:20.6308392Z     node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2024-11-01T17:51:20.6309211Z     name (str): Event name. (E.g. Current action being performed).
2024-11-01T17:51:20.6309811Z     hostname (str): Hostname of the node.
2024-11-01T17:51:20.6310532Z     pid (Optional[int]): The process id of the node.
2024-11-01T17:51:20.6311267Z     master_endpoint (str): The master endpoint for the rendezvous store, if known.
2024-11-01T17:51:20.6312213Z     local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2024-11-01T17:51:20.6313018Z     rank (Optional[int]): The rank of the node, if known.
2024-11-01T17:51:20.6313516Z Returns:
2024-11-01T17:51:20.6313787Z     None
2024-11-01T17:51:20.6314129Z Example:
2024-11-01T17:51:20.6314472Z     >>> # See DynamicRendezvousHandler class
2024-11-01T17:51:20.6314940Z     >>> def _record(
2024-11-01T17:51:20.6315264Z     ...     self,
2024-11-01T17:51:20.6315580Z     ...     message: str,
2024-11-01T17:51:20.6316003Z     ...     node_state: NodeState = NodeState.RUNNING,
2024-11-01T17:51:20.6316518Z     ...     rank: Optional[int] = None,
2024-11-01T17:51:20.6317002Z     ... ) -> None:
2024-11-01T17:51:20.6317362Z     ...     construct_and_record_rdzv_event(
2024-11-01T17:51:20.6317942Z     ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2024-11-01T17:51:20.6318499Z     ...         run_id=self._settings.run_id,
2024-11-01T17:51:20.6318960Z     ...         message=message,
2024-11-01T17:51:20.6319359Z     ...         node_state=node_state,
2024-11-01T17:51:20.6319814Z     ...         hostname=self._this_node.addr,
2024-11-01T17:51:20.6320287Z     ...         pid=self._this_node.pid,
2024-11-01T17:51:20.6320748Z     ...         local_id=self._this_node.local_id,
2024-11-01T17:51:20.6321217Z     ...         rank=rank,
2024-11-01T17:51:20.6321552Z     ...     )
2024-11-01T17:51:20.6321738Z 
2024-11-01T17:51:20.6322307Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.6322827Z 
2024-11-01T17:51:20.8221044Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113.
2024-11-01T17:51:20.8222478Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8223034Z 
2024-11-01T17:51:20.8223394Z This configures FSDP-native mixed precision training.
2024-11-01T17:51:20.8223786Z 
2024-11-01T17:51:20.8223911Z Attributes:
2024-11-01T17:51:20.8224424Z     param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2024-11-01T17:51:20.8225203Z         parameters during forward and backward and thus the dtype for
2024-11-01T17:51:20.8225989Z         forward and backward computation. Outside forward and backward, the
2024-11-01T17:51:20.8226764Z         *sharded* parameters are kept in full precision (e.g. for the
2024-11-01T17:51:20.8227533Z         optimizer step), and for model checkpointing, the parameters are
2024-11-01T17:51:20.8228238Z         always saved in full precision. (Default: ``None``)
2024-11-01T17:51:20.8228942Z     reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2024-11-01T17:51:20.8229789Z         gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2024-11-01T17:51:20.8230551Z         ``None`` but ``param_dtype`` is not ``None``, then this takes on
2024-11-01T17:51:20.8231292Z         the ``param_dtype`` value, still running gradient reduction in low
2024-11-01T17:51:20.8232063Z         precision. This is permitted to differ from ``param_dtype``, e.g.
2024-11-01T17:51:20.8232838Z         to force gradient reduction to run in full precision. (Default:
2024-11-01T17:51:20.8233416Z         ``None``)
2024-11-01T17:51:20.8233996Z     buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2024-11-01T17:51:20.8234775Z         buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2024-11-01T17:51:20.8235522Z         ``buffer_dtype`` in the first forward pass and keeps them in that
2024-11-01T17:51:20.8236286Z         dtype thereafter. For model checkpointing, the buffers are saved
2024-11-01T17:51:20.8237025Z         in full precision except for ``LOCAL_STATE_DICT``. (Default:
2024-11-01T17:51:20.8237572Z         ``None``)
2024-11-01T17:51:20.8238301Z     keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2024-11-01T17:51:20.8239060Z         gradients to full precision after the backward pass in preparation
2024-11-01T17:51:20.8239852Z         for the optimizer step. If ``True``, then FSDP keeps the gradients
2024-11-01T17:51:20.8240631Z         in the dtype used for gradient reduction, which can save memory if
2024-11-01T17:51:20.8241405Z         using a custom optimizer that supports running in low precision.
2024-11-01T17:51:20.8241997Z         (Default: ``False``)
2024-11-01T17:51:20.8242539Z     cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2024-11-01T17:51:20.8243303Z         its forward args and kwargs to ``param_dtype``. This is to ensure
2024-11-01T17:51:20.8244083Z         that parameter and input dtypes match for forward computation, as
2024-11-01T17:51:20.8244871Z         required by many ops. This may need to be set to ``True`` when only
2024-11-01T17:51:20.8245671Z         applying mixed precision to some but not all FSDP modules, in which
2024-11-01T17:51:20.8246569Z         case a mixed-precision FSDP submodule needs to recast its inputs.
2024-11-01T17:51:20.8247160Z         (Default: ``False``)
2024-11-01T17:51:20.8247726Z     cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2024-11-01T17:51:20.8248505Z         casts its forward args and kwargs to ``param_dtype``, overriding
2024-11-01T17:51:20.8249332Z         the value of ``cast_forward_inputs``. For non-root FSDP modules,
2024-11-01T17:51:20.8249985Z         this does not do anything. (Default: ``True``)
2024-11-01T17:51:20.8250781Z     _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2024-11-01T17:51:20.8251524Z         module classes to ignore for mixed precision when using an
2024-11-01T17:51:20.8252214Z         ``auto_wrap_policy``: Modules of these classes will have FSDP
2024-11-01T17:51:20.8252946Z         applied to them separately with mixed precision disabled (meaning
2024-11-01T17:51:20.8253727Z         that the final FSDP construction would deviate from the specified
2024-11-01T17:51:20.8254475Z         policy). If ``auto_wrap_policy`` is not specified, then this does
2024-11-01T17:51:20.8255205Z         not do anything. This API is experimental and subject to change.
2024-11-01T17:51:20.8255805Z         (Default: ``(_BatchNorm,)``)
2024-11-01T17:51:20.8256109Z 
2024-11-01T17:51:20.8256366Z .. note:: This API is experimental and subject to change.
2024-11-01T17:51:20.8256768Z 
2024-11-01T17:51:20.8257080Z .. note:: Only floating point tensors are cast to their specified dtypes.
2024-11-01T17:51:20.8257555Z 
2024-11-01T17:51:20.8257837Z .. note:: In ``summon_full_params``, parameters are forced to full
2024-11-01T17:51:20.8258418Z     precision, but buffers are not.
2024-11-01T17:51:20.8258706Z 
2024-11-01T17:51:20.8259013Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2024-11-01T17:51:20.8259780Z     their inputs are in a low precision like ``float16`` or ``bfloat16``.
2024-11-01T17:51:20.8260678Z     Disabling FSDP's mixed precision for those norm modules only means that
2024-11-01T17:51:20.8261494Z     the affine parameters are kept in ``float32``. However, this incurs
2024-11-01T17:51:20.8262389Z     separate all-gathers and reduce-scatters for those norm modules, which
2024-11-01T17:51:20.8263221Z     may be inefficient, so if the workload permits, the user should prefer
2024-11-01T17:51:20.8263920Z     to still apply mixed precision to those modules.
2024-11-01T17:51:20.8264289Z 
2024-11-01T17:51:20.8264599Z .. note:: By default, if the user passes a model with any ``_BatchNorm``
2024-11-01T17:51:20.8265387Z     modules and specifies an ``auto_wrap_policy``, then the batch norm
2024-11-01T17:51:20.8266180Z     modules will have FSDP applied to them separately with mixed precision
2024-11-01T17:51:20.8266926Z     disabled. See the ``_module_classes_to_ignore`` argument.
2024-11-01T17:51:20.8267331Z 
2024-11-01T17:51:20.8267638Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2024-11-01T17:51:20.8268522Z     ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2024-11-01T17:51:20.8269243Z     its ``cast_root_forward_inputs`` takes precedence over its
2024-11-01T17:51:20.8269973Z     ``cast_forward_inputs``. For non-root FSDP instances, their
2024-11-01T17:51:20.8270701Z     ``cast_root_forward_inputs`` values are ignored. The default setting is
2024-11-01T17:51:20.8271505Z     sufficient for the typical case where each FSDP instance has the same
2024-11-01T17:51:20.8272306Z     ``MixedPrecision`` configuration and only needs to cast inputs to the
2024-11-01T17:51:20.8273121Z     ``param_dtype`` at the beginning of the model's forward pass.
2024-11-01T17:51:20.8273542Z 
2024-11-01T17:51:20.8273978Z .. note:: For nested FSDP instances with different ``MixedPrecision``
2024-11-01T17:51:20.8274775Z     configurations, we recommend setting individual ``cast_forward_inputs``
2024-11-01T17:51:20.8275609Z     values to configure casting inputs or not before each instance's
2024-11-01T17:51:20.8276365Z     forward. In such a case, since the casts happen before each FSDP
2024-11-01T17:51:20.8277207Z     instance's forward, a parent FSDP instance should have its non-FSDP
2024-11-01T17:51:20.8278007Z     submodules run before its FSDP submodules to avoid the activation dtype
2024-11-01T17:51:20.8278816Z     being changed due to a different ``MixedPrecision`` configuration.
2024-11-01T17:51:20.8279285Z 
2024-11-01T17:51:20.8279396Z     Example::
2024-11-01T17:51:20.8279581Z 
2024-11-01T17:51:20.8279766Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.8280468Z         >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2024-11-01T17:51:20.8281004Z         >>> model[1] = FSDP(
2024-11-01T17:51:20.8291888Z         >>>     model[1],
2024-11-01T17:51:20.8292599Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2024-11-01T17:51:20.8293298Z         >>> )
2024-11-01T17:51:20.8293605Z         >>> model = FSDP(
2024-11-01T17:51:20.8293945Z         >>>     model,
2024-11-01T17:51:20.8294568Z         >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2024-11-01T17:51:20.8295254Z         >>> )
2024-11-01T17:51:20.8295426Z 
2024-11-01T17:51:20.8295757Z     The above shows a working example. On the other hand, if ``model[1]``
2024-11-01T17:51:20.8296524Z     were replaced with ``model[0]``, meaning that the submodule using
2024-11-01T17:51:20.8297291Z     different ``MixedPrecision`` ran its forward first, then ``model[1]``
2024-11-01T17:51:20.8298096Z     would incorrectly see ``float16`` activations instead of ``bfloat16``
2024-11-01T17:51:20.8298680Z     ones.
2024-11-01T17:51:20.8298835Z 
2024-11-01T17:51:20.8298841Z 
2024-11-01T17:51:20.8299421Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8299935Z 
2024-11-01T17:51:20.8394519Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=649.
2024-11-01T17:51:20.8396280Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8397194Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2024-11-01T17:51:20.8397795Z 
2024-11-01T17:51:20.8398407Z         Also takes (optional) configuration for the model's and optimizer's state dict.
2024-11-01T17:51:20.8399464Z         The target module does not have to be a FSDP module. If the target
2024-11-01T17:51:20.8400293Z         module is a FSDP module, its ``state_dict_type`` will also be changed.
2024-11-01T17:51:20.8400812Z 
2024-11-01T17:51:20.8401212Z         .. note:: This API should be called for only the top-level (root)
2024-11-01T17:51:20.8401781Z             module.
2024-11-01T17:51:20.8401972Z 
2024-11-01T17:51:20.8402301Z         .. note:: This API enables users to transparently use the conventional
2024-11-01T17:51:20.8403363Z             ``state_dict`` API to take model checkpoints in cases where the
2024-11-01T17:51:20.8404124Z             root FSDP module is wrapped by another ``nn.Module``. For example,
2024-11-01T17:51:20.8404966Z             the following will ensure ``state_dict`` is called on all non-FSDP
2024-11-01T17:51:20.8405769Z             instances, while dispatching into `sharded_state_dict` implementation
2024-11-01T17:51:20.8406387Z             for FSDP:
2024-11-01T17:51:20.8406896Z 
2024-11-01T17:51:20.8407026Z         Example::
2024-11-01T17:51:20.8407213Z 
2024-11-01T17:51:20.8407430Z             >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.8407941Z             >>> model = DDP(FSDP(...))
2024-11-01T17:51:20.8408372Z             >>> FSDP.set_state_dict_type(
2024-11-01T17:51:20.8408794Z             >>>     model,
2024-11-01T17:51:20.8409214Z             >>>     StateDictType.SHARDED_STATE_DICT,
2024-11-01T17:51:20.8409892Z             >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2024-11-01T17:51:20.8410706Z             >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2024-11-01T17:51:20.8411316Z             >>> )
2024-11-01T17:51:20.8411676Z             >>> param_state_dict = model.state_dict()
2024-11-01T17:51:20.8412279Z             >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2024-11-01T17:51:20.8412704Z 
2024-11-01T17:51:20.8412810Z         Args:
2024-11-01T17:51:20.8413163Z             module (torch.nn.Module): Root module.
2024-11-01T17:51:20.8413981Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2024-11-01T17:51:20.8414813Z             state_dict_config (Optional[StateDictConfig]): the configuration for the
2024-11-01T17:51:20.8415476Z                 target ``state_dict_type``.
2024-11-01T17:51:20.8416154Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2024-11-01T17:51:20.8416848Z                 for the optimizer state dict.
2024-11-01T17:51:20.8417179Z 
2024-11-01T17:51:20.8417304Z         Returns:
2024-11-01T17:51:20.8417806Z             A StateDictSettings that include the previous state_dict type and
2024-11-01T17:51:20.8418428Z             configuration for the module.
2024-11-01T17:51:20.8418845Z         
2024-11-01T17:51:20.8419452Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8419971Z 
2024-11-01T17:51:20.8421291Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=805.
2024-11-01T17:51:20.8422999Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8423961Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2024-11-01T17:51:20.8424507Z 
2024-11-01T17:51:20.8424990Z         This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2024-11-01T17:51:20.8425811Z         :meth:`set_state_dict_type` for the detail.
2024-11-01T17:51:20.8426156Z 
2024-11-01T17:51:20.8426268Z         Example::
2024-11-01T17:51:20.8426468Z 
2024-11-01T17:51:20.8426662Z             >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.8427164Z             >>> model = DDP(FSDP(...))
2024-11-01T17:51:20.8427612Z             >>> with FSDP.state_dict_type(
2024-11-01T17:51:20.8428041Z             >>>     model,
2024-11-01T17:51:20.8428467Z             >>>     StateDictType.SHARDED_STATE_DICT,
2024-11-01T17:51:20.8428925Z             >>> ):
2024-11-01T17:51:20.8429285Z             >>>     checkpoint = model.state_dict()
2024-11-01T17:51:20.8429636Z 
2024-11-01T17:51:20.8429740Z         Args:
2024-11-01T17:51:20.8430089Z             module (torch.nn.Module): Root module.
2024-11-01T17:51:20.8430771Z             state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2024-11-01T17:51:20.8431767Z             state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2024-11-01T17:51:20.8432509Z                 configuration for the target ``state_dict_type``.
2024-11-01T17:51:20.8433241Z             optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2024-11-01T17:51:20.8434157Z                ``state_dict`` configuration for the target ``state_dict_type``.
2024-11-01T17:51:20.8434728Z         
2024-11-01T17:51:20.8435332Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8435844Z 
2024-11-01T17:51:20.8465904Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1818.
2024-11-01T17:51:20.8467629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8468301Z 
2024-11-01T17:51:20.8469028Z Transform the state-dict of an optimizer corresponding to a sharded model.
2024-11-01T17:51:20.8469627Z 
2024-11-01T17:51:20.8470123Z The given state-dict can be transformed to one of three types:
2024-11-01T17:51:20.8471312Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2024-11-01T17:51:20.8471921Z 
2024-11-01T17:51:20.8472255Z For full optimizer state_dict, all states are unflattened and not sharded.
2024-11-01T17:51:20.8473090Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2024-11-01T17:51:20.8473690Z avoid OOM.
2024-11-01T17:51:20.8473948Z 
2024-11-01T17:51:20.8474472Z For sharded optimizer state_dict, all states are unflattened but sharded.
2024-11-01T17:51:20.8475308Z CPU only can be specified via :meth:`state_dict_type` to further save
2024-11-01T17:51:20.8475872Z memory.
2024-11-01T17:51:20.8476036Z 
2024-11-01T17:51:20.8476354Z For local state_dict, no transformation will be performed. But a state
2024-11-01T17:51:20.8477204Z will be converted from nn.Tensor to ShardedTensor to represent its sharding
2024-11-01T17:51:20.8477883Z nature (this is not supported yet).
2024-11-01T17:51:20.8478178Z 
2024-11-01T17:51:20.8478367Z Example::
2024-11-01T17:51:20.8478525Z 
2024-11-01T17:51:20.8478718Z     >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.8479399Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2024-11-01T17:51:20.8480149Z     >>> from torch.distributed.fsdp import StateDictType
2024-11-01T17:51:20.8480809Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2024-11-01T17:51:20.8481523Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2024-11-01T17:51:20.8482108Z     >>> # Save a checkpoint
2024-11-01T17:51:20.8482531Z     >>> model, optim = ...
2024-11-01T17:51:20.8482900Z     >>> FSDP.set_state_dict_type(
2024-11-01T17:51:20.8483289Z     >>>     model,
2024-11-01T17:51:20.8483643Z     >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:20.8484152Z     >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8484726Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8485203Z     >>> )
2024-11-01T17:51:20.8485509Z     >>> state_dict = model.state_dict()
2024-11-01T17:51:20.8486060Z     >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2024-11-01T17:51:20.8486666Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2024-11-01T17:51:20.8487166Z     >>> # Load a checkpoint
2024-11-01T17:51:20.8487521Z     >>> model, optim = ...
2024-11-01T17:51:20.8487977Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2024-11-01T17:51:20.8488498Z     >>> FSDP.set_state_dict_type(
2024-11-01T17:51:20.8488889Z     >>>     model,
2024-11-01T17:51:20.8489240Z     >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:20.8489750Z     >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8490299Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8490792Z     >>> )
2024-11-01T17:51:20.8491103Z     >>> model.load_state_dict(state_dict)
2024-11-01T17:51:20.8491741Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2024-11-01T17:51:20.8492279Z     >>>     model, optim, optim_state_dict
2024-11-01T17:51:20.8492692Z     >>> )
2024-11-01T17:51:20.8493028Z     >>> optim.load_state_dict(optim_state_dict)
2024-11-01T17:51:20.8493377Z 
2024-11-01T17:51:20.8493475Z Args:
2024-11-01T17:51:20.8493913Z     model (torch.nn.Module): Root module (which may or may not be a
2024-11-01T17:51:20.8494650Z         :class:`FullyShardedDataParallel` instance) whose parameters
2024-11-01T17:51:20.8495289Z         were passed into the optimizer ``optim``.
2024-11-01T17:51:20.8495976Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2024-11-01T17:51:20.8496521Z         parameters.
2024-11-01T17:51:20.8497031Z     optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2024-11-01T17:51:20.8497820Z         transform. If the value is None, optim.state_dict() will be used. (
2024-11-01T17:51:20.8498423Z         Default: ``None``)
2024-11-01T17:51:20.8499082Z     group (dist.ProcessGroup): Model's process group across which parameters
2024-11-01T17:51:20.8499866Z         are sharded or ``None`` if using the default process group. (
2024-11-01T17:51:20.8500429Z         Default: ``None``)
2024-11-01T17:51:20.8500678Z 
2024-11-01T17:51:20.8500784Z Returns:
2024-11-01T17:51:20.8501238Z     Dict[str, Any]: A :class:`dict` containing the optimizer state for
2024-11-01T17:51:20.8501944Z     ``model``. The sharding of the optimizer state is based on
2024-11-01T17:51:20.8502463Z     ``state_dict_type``.
2024-11-01T17:51:20.8502699Z 
2024-11-01T17:51:20.8503228Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8503758Z 
2024-11-01T17:51:20.8505176Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1916.
2024-11-01T17:51:20.8507279Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8507834Z 
2024-11-01T17:51:20.8508471Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2024-11-01T17:51:20.8509199Z 
2024-11-01T17:51:20.8509432Z Given a ``optim_state_dict`` that is transformed through
2024-11-01T17:51:20.8510140Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2024-11-01T17:51:20.8510930Z state_dict that can be loaded to ``optim`` which is the optimizer for
2024-11-01T17:51:20.8511685Z ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2024-11-01T17:51:20.8512127Z 
2024-11-01T17:51:20.8512319Z     >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.8512993Z     >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2024-11-01T17:51:20.8513728Z     >>> from torch.distributed.fsdp import StateDictType
2024-11-01T17:51:20.8514473Z     >>> from torch.distributed.fsdp import FullStateDictConfig
2024-11-01T17:51:20.8515251Z     >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2024-11-01T17:51:20.8515844Z     >>> # Save a checkpoint
2024-11-01T17:51:20.8516201Z     >>> model, optim = ...
2024-11-01T17:51:20.8516581Z     >>> FSDP.set_state_dict_type(
2024-11-01T17:51:20.8516970Z     >>>     model,
2024-11-01T17:51:20.8517320Z     >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:20.8517830Z     >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8518399Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8518870Z     >>> )
2024-11-01T17:51:20.8519178Z     >>> state_dict = model.state_dict()
2024-11-01T17:51:20.8519641Z     >>> original_osd = optim.state_dict()
2024-11-01T17:51:20.8520142Z     >>> optim_state_dict = FSDP.optim_state_dict(
2024-11-01T17:51:20.8520601Z     >>>     model,
2024-11-01T17:51:20.8520889Z     >>>     optim,
2024-11-01T17:51:20.8521231Z     >>>     optim_state_dict=original_osd
2024-11-01T17:51:20.8521881Z     >>> )
2024-11-01T17:51:20.8522245Z     >>> save_a_checkpoint(state_dict, optim_state_dict)
2024-11-01T17:51:20.8522746Z     >>> # Load a checkpoint
2024-11-01T17:51:20.8523103Z     >>> model, optim = ...
2024-11-01T17:51:20.8523563Z     >>> state_dict, optim_state_dict = load_a_checkpoint()
2024-11-01T17:51:20.8524087Z     >>> FSDP.set_state_dict_type(
2024-11-01T17:51:20.8524475Z     >>>     model,
2024-11-01T17:51:20.8524824Z     >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:20.8525004Z     >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8525228Z     >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:20.8525341Z     >>> )
2024-11-01T17:51:20.8525485Z     >>> model.load_state_dict(state_dict)
2024-11-01T17:51:20.8525706Z     >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2024-11-01T17:51:20.8525854Z     >>>     model, optim, optim_state_dict
2024-11-01T17:51:20.8525968Z     >>> )
2024-11-01T17:51:20.8526136Z     >>> optim.load_state_dict(optim_state_dict)
2024-11-01T17:51:20.8526146Z 
2024-11-01T17:51:20.8526244Z Args:
2024-11-01T17:51:20.8526545Z     model (torch.nn.Module): Root module (which may or may not be a
2024-11-01T17:51:20.8526828Z         :class:`FullyShardedDataParallel` instance) whose parameters
2024-11-01T17:51:20.8527019Z         were passed into the optimizer ``optim``.
2024-11-01T17:51:20.8527331Z     optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2024-11-01T17:51:20.8527457Z         parameters.
2024-11-01T17:51:20.8527756Z     optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2024-11-01T17:51:20.8528150Z     is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2024-11-01T17:51:20.8528471Z         KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2024-11-01T17:51:20.8528785Z         KeyedOptimizer or torch.distributed's NamedOptimizer.
2024-11-01T17:51:20.8529078Z     load_directly (bool): If this is set to True, this API will also
2024-11-01T17:51:20.8529365Z         call optim.load_state_dict(result) before returning the result.
2024-11-01T17:51:20.8529697Z         Otherwise, users are responsible to call ``optim.load_state_dict()``
2024-11-01T17:51:20.8529822Z         (Default: ``False``)
2024-11-01T17:51:20.8530254Z     group (dist.ProcessGroup): Model's process group across which parameters
2024-11-01T17:51:20.8530526Z         are sharded or ``None`` if using the default process group. (
2024-11-01T17:51:20.8530645Z         Default: ``None``)
2024-11-01T17:51:20.8530651Z 
2024-11-01T17:51:20.8531088Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8531094Z 
2024-11-01T17:51:20.8708542Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137.
2024-11-01T17:51:20.8709049Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8709059Z 
2024-11-01T17:51:20.8709440Z RemoteModule instance can only be created after RPC initialization.
2024-11-01T17:51:20.8709459Z 
2024-11-01T17:51:20.8709822Z It creates a user-specified module on a specified remote node.
2024-11-01T17:51:20.8710188Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2024-11-01T17:51:20.8710326Z executed on the remote node.
2024-11-01T17:51:20.8710679Z It takes care of autograd recording to ensure the backward pass propagates
2024-11-01T17:51:20.8710896Z gradients back to the corresponding remote module.
2024-11-01T17:51:20.8711429Z It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2024-11-01T17:51:20.8711726Z without incurring any overheads of copying the actual module,
2024-11-01T17:51:20.8712012Z which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2024-11-01T17:51:20.8712161Z pointing to the remote module.
2024-11-01T17:51:20.8712166Z 
2024-11-01T17:51:20.8712458Z The arguments of ``forward_async`` and ``forward`` are the same as
2024-11-01T17:51:20.8713142Z the ``forward`` method of the module returned by the ``module_cls``.
2024-11-01T17:51:20.8713162Z 
2024-11-01T17:51:20.8713632Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2024-11-01T17:51:20.8713638Z 
2024-11-01T17:51:20.8714439Z Particularly, to create a hybrid model, typically the local modules should be
2024-11-01T17:51:20.8715027Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2024-11-01T17:51:20.8715148Z Hybrid Example:
2024-11-01T17:51:20.8715318Z         >>> class HybridModel(nn.Module):
2024-11-01T17:51:20.8715621Z         >>>     def __init__(self) -> None:
2024-11-01T17:51:20.8715794Z         >>>         nn.Module.__init__(self)
2024-11-01T17:51:20.8716003Z         >>>         self.remote_embedding = RemoteModule(...)
2024-11-01T17:51:20.8716189Z         >>>         self.local_linear = nn.Linear(...)
2024-11-01T17:51:20.8716196Z 
2024-11-01T17:51:20.8716523Z For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2024-11-01T17:51:20.8717274Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2024-11-01T17:51:20.8717744Z the generated ``RemoteModule`` will have 2 methods in signature of
2024-11-01T17:51:20.8718087Z ``def forward(input: Tensor) -> Tensor:`` and
2024-11-01T17:51:20.8718404Z ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2024-11-01T17:51:20.8718416Z 
2024-11-01T17:51:20.8718544Z .. note::
2024-11-01T17:51:20.8718841Z     If the remote module is placed on a cuda device,
2024-11-01T17:51:20.8719751Z     any input CPU tensors will be automatically moved to the same cuda device,
2024-11-01T17:51:20.8720401Z     and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2024-11-01T17:51:20.8720408Z 
2024-11-01T17:51:20.8720526Z Args:
2024-11-01T17:51:20.8721084Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2024-11-01T17:51:20.8721569Z         The device can be a local device or a remote device specified by one of the following remote
2024-11-01T17:51:20.8721681Z         formats:
2024-11-01T17:51:20.8721687Z 
2024-11-01T17:51:20.8721904Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2024-11-01T17:51:20.8722118Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2024-11-01T17:51:20.8722123Z 
2024-11-01T17:51:20.8722498Z         In addition, the device field can be optional and the default value is "cpu".
2024-11-01T17:51:20.8722655Z     module_cls (nn.Module): For example,
2024-11-01T17:51:20.8722809Z         >>> class MyModule(nn.Module):
2024-11-01T17:51:20.8722950Z         >>>     def forward(input):
2024-11-01T17:51:20.8723084Z         >>>         return input + 1
2024-11-01T17:51:20.8723202Z         >>>
2024-11-01T17:51:20.8723336Z         >>> module_cls = MyModule
2024-11-01T17:51:20.8723632Z     args (Sequence, optional): args to be passed to ``module_cls``.
2024-11-01T17:51:20.8723912Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2024-11-01T17:51:20.8724302Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2024-11-01T17:51:20.8724682Z         to be created. The type object should be decorated by @torch.jit.interface.
2024-11-01T17:51:20.8725098Z         If not provided, the generated RemoteModule is not torchscript-able.
2024-11-01T17:51:20.8725453Z         Warning, this is an experimental API and susceptible to frequent changes.
2024-11-01T17:51:20.8725461Z 
2024-11-01T17:51:20.8725570Z Returns:
2024-11-01T17:51:20.8725943Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2024-11-01T17:51:20.8726359Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2024-11-01T17:51:20.8726734Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2024-11-01T17:51:20.8727101Z     on the user-provided module on the remote side.
2024-11-01T17:51:20.8727266Z 
2024-11-01T17:51:20.8727455Z Example::
2024-11-01T17:51:20.8727758Z     Run the following code in two different processes:
2024-11-01T17:51:20.8727764Z 
2024-11-01T17:51:20.8727973Z     >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:20.8728105Z     >>> # On worker 0:
2024-11-01T17:51:20.8728268Z     >>> import torch
2024-11-01T17:51:20.8728445Z     >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:20.8728671Z     >>> from torch import nn, Tensor
2024-11-01T17:51:20.8729214Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2024-11-01T17:51:20.8729434Z     >>>
2024-11-01T17:51:20.8729802Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2024-11-01T17:51:20.8729990Z     >>> remote_linear_module = RemoteModule(
2024-11-01T17:51:20.8730178Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2024-11-01T17:51:20.8730282Z     >>> )
2024-11-01T17:51:20.8730439Z     >>> input = torch.randn(128, 20)
2024-11-01T17:51:20.8730663Z     >>> ret_fut = remote_linear_module.forward_async(input)
2024-11-01T17:51:20.8730809Z     >>> ret = ret_fut.wait()
2024-11-01T17:51:20.8730929Z     >>> rpc.shutdown()
2024-11-01T17:51:20.8730934Z 
2024-11-01T17:51:20.8731064Z     >>> # On worker 1:
2024-11-01T17:51:20.8731174Z     >>> import torch
2024-11-01T17:51:20.8731344Z     >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:20.8731464Z     >>>
2024-11-01T17:51:20.8731654Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2024-11-01T17:51:20.8731786Z     >>> rpc.shutdown()
2024-11-01T17:51:20.8731791Z 
2024-11-01T17:51:20.8732352Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8732359Z 
2024-11-01T17:51:20.8733617Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514.
2024-11-01T17:51:20.8734077Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8734089Z 
2024-11-01T17:51:20.8734545Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2024-11-01T17:51:20.8734551Z 
2024-11-01T17:51:20.8735020Z This alternate initialization method can be particularly useful if we want to create multiple
2024-11-01T17:51:20.8735468Z RemoteModule instances that share the same underlying module and reduce memory consumption.
2024-11-01T17:51:20.8735473Z 
2024-11-01T17:51:20.8735878Z Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2024-11-01T17:51:20.8736140Z which is not supported. The recommended way is as follows:
2024-11-01T17:51:20.8736151Z 
2024-11-01T17:51:20.8736313Z     1. the sender creates a RemoteModule;
2024-11-01T17:51:20.8736530Z     2. the sender sends its ``module_rref`` over RPC;
2024-11-01T17:51:20.8737024Z     3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2024-11-01T17:51:20.8737031Z 
2024-11-01T17:51:20.8737163Z Example::
2024-11-01T17:51:20.8737376Z     Run the following code in two different processes:
2024-11-01T17:51:20.8737381Z 
2024-11-01T17:51:20.8737546Z     >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:20.8737664Z     >>> # On worker 0:
2024-11-01T17:51:20.8737798Z     >>> import torch
2024-11-01T17:51:20.8737963Z     >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:20.8738102Z     >>> from torch import nn, Tensor
2024-11-01T17:51:20.8738435Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2024-11-01T17:51:20.8738537Z     >>>
2024-11-01T17:51:20.8738749Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2024-11-01T17:51:20.8738896Z     >>> remote_module = RemoteModule(
2024-11-01T17:51:20.8739092Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2024-11-01T17:51:20.8739195Z     >>> )
2024-11-01T17:51:20.8739299Z     >>>
2024-11-01T17:51:20.8739460Z     >>> remote_module1 = rpc.rpc_sync(
2024-11-01T17:51:20.8739581Z     >>>     "worker1/cpu",
2024-11-01T17:51:20.8739860Z     >>>     RemoteModule.init_from_module_rref,
2024-11-01T17:51:20.8740084Z     >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2024-11-01T17:51:20.8740186Z     >>> )
2024-11-01T17:51:20.8740320Z     >>> rpc.shutdown()
2024-11-01T17:51:20.8740325Z 
2024-11-01T17:51:20.8740440Z     >>> # On worker 1:
2024-11-01T17:51:20.8740568Z     >>> import torch
2024-11-01T17:51:20.8740735Z     >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:20.8740852Z     >>>
2024-11-01T17:51:20.8741044Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2024-11-01T17:51:20.8741164Z     >>> rpc.shutdown()
2024-11-01T17:51:20.8741170Z 
2024-11-01T17:51:20.8741291Z Args:
2024-11-01T17:51:20.8741841Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2024-11-01T17:51:20.8742306Z         The device can be a local device or a remote device specified by one of the following remote
2024-11-01T17:51:20.8742414Z         formats:
2024-11-01T17:51:20.8742419Z 
2024-11-01T17:51:20.8742640Z             1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2024-11-01T17:51:20.8742854Z             2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2024-11-01T17:51:20.8742859Z 
2024-11-01T17:51:20.8743237Z         In addition, the device field can be optional and the default value is "cpu".
2024-11-01T17:51:20.8743608Z     module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2024-11-01T17:51:20.8743746Z         the created remote module.
2024-11-01T17:51:20.8744147Z     _module_interface_cls (type, optional): The TorchScript interface type for the module
2024-11-01T17:51:20.8744611Z         to be created. The type object should be decorated by @torch.jit.interface.
2024-11-01T17:51:20.8745034Z         If not provided, the generated RemoteModule is not torchscript-able.
2024-11-01T17:51:20.8745384Z         Warning, this is an experimental API and susceptible to frequent changes.
2024-11-01T17:51:20.8745389Z 
2024-11-01T17:51:20.8745508Z Returns:
2024-11-01T17:51:20.8745858Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2024-11-01T17:51:20.8746276Z     user-provided ``module_rref``, it has a blocking ``forward`` method and an
2024-11-01T17:51:20.8746666Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2024-11-01T17:51:20.8746926Z     on the user-provided module on the remote side.
2024-11-01T17:51:20.8746933Z 
2024-11-01T17:51:20.8747374Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8747380Z 
2024-11-01T17:51:20.8748390Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606.
2024-11-01T17:51:20.8748852Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8748858Z 
2024-11-01T17:51:20.8749180Z     A RemoteModule instance can only be created after RPC initialization.
2024-11-01T17:51:20.8749190Z 
2024-11-01T17:51:20.8749558Z     It creates a user-specified module on a specified remote node.
2024-11-01T17:51:20.8749904Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2024-11-01T17:51:20.8750058Z     executed on the remote node.
2024-11-01T17:51:20.8750405Z     It takes care of autograd recording to ensure the backward pass propagates
2024-11-01T17:51:20.8750620Z     gradients back to the corresponding remote module.
2024-11-01T17:51:20.8750638Z 
2024-11-01T17:51:20.8750951Z     It generates two methods ``forward_async`` and ``forward`` based on the
2024-11-01T17:51:20.8751274Z     signature of the ``forward`` method of ``module_cls``. ``forward_async``
2024-11-01T17:51:20.8751640Z     runs asynchronously and returns a Future. The arguments of ``forward_async``
2024-11-01T17:51:20.8751930Z     and ``forward`` are the same as the ``forward`` method of the module
2024-11-01T17:51:20.8752082Z     returned by the ``module_cls``.
2024-11-01T17:51:20.8752088Z 
2024-11-01T17:51:20.8752467Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2024-11-01T17:51:20.8752936Z     that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2024-11-01T17:51:20.8753256Z     the generated ``RemoteModule`` will have 2 methods with the signatures:
2024-11-01T17:51:20.8753262Z 
2024-11-01T17:51:20.8753503Z     | ``def forward(input: Tensor) -> Tensor:``
2024-11-01T17:51:20.8753796Z     | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2024-11-01T17:51:20.8753802Z 
2024-11-01T17:51:20.8754105Z Args:
2024-11-01T17:51:20.8754653Z     remote_device (str): Device on the destination worker where we'd like to place this module.
2024-11-01T17:51:20.8755148Z         The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2024-11-01T17:51:20.8755368Z         E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2024-11-01T17:51:20.8755728Z         In addition, the device field can be optional and the default value is "cpu".
2024-11-01T17:51:20.8756119Z     module_cls (nn.Module): Class for the module to be created remotely. For example,
2024-11-01T17:51:20.8756125Z 
2024-11-01T17:51:20.8756266Z         >>> class MyModule(nn.Module):
2024-11-01T17:51:20.8756413Z         >>>     def forward(input):
2024-11-01T17:51:20.8756542Z         >>>         return input + 1
2024-11-01T17:51:20.8756644Z         >>>
2024-11-01T17:51:20.8756791Z         >>> module_cls = MyModule
2024-11-01T17:51:20.8756796Z 
2024-11-01T17:51:20.8757070Z     args (Sequence, optional): args to be passed to ``module_cls``.
2024-11-01T17:51:20.8757442Z     kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2024-11-01T17:51:20.8757448Z 
2024-11-01T17:51:20.8757551Z Returns:
2024-11-01T17:51:20.8757912Z     A remote module instance which wraps the :class:`~nn.Module` created by the
2024-11-01T17:51:20.8758320Z     user-provided ``module_cls``, it has a blocking ``forward`` method and an
2024-11-01T17:51:20.8758692Z     asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2024-11-01T17:51:20.8758966Z     on the user-provided module on the remote side.
2024-11-01T17:51:20.8758972Z 
2024-11-01T17:51:20.8759088Z Example::
2024-11-01T17:51:20.8759321Z     Run the following code in two different processes:
2024-11-01T17:51:20.8759327Z 
2024-11-01T17:51:20.8759478Z     >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:20.8759607Z     >>> # On worker 0:
2024-11-01T17:51:20.8759719Z     >>> import torch
2024-11-01T17:51:20.8759888Z     >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:20.8760042Z     >>> from torch import nn, Tensor
2024-11-01T17:51:20.8760359Z     >>> from torch.distributed.nn.api.remote_module import RemoteModule
2024-11-01T17:51:20.8760473Z     >>>
2024-11-01T17:51:20.8760660Z     >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2024-11-01T17:51:20.8760839Z     >>> remote_linear_module = RemoteModule(
2024-11-01T17:51:20.8761019Z     >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2024-11-01T17:51:20.8761127Z     >>> )
2024-11-01T17:51:20.8761274Z     >>> input = torch.randn(128, 20)
2024-11-01T17:51:20.8761489Z     >>> ret_fut = remote_linear_module.forward_async(input)
2024-11-01T17:51:20.8761627Z     >>> ret = ret_fut.wait()
2024-11-01T17:51:20.8761748Z     >>> rpc.shutdown()
2024-11-01T17:51:20.8761754Z 
2024-11-01T17:51:20.8761881Z     >>> # On worker 1:
2024-11-01T17:51:20.8761991Z     >>> import torch
2024-11-01T17:51:20.8762157Z     >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:20.8762270Z     >>>
2024-11-01T17:51:20.8762456Z     >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2024-11-01T17:51:20.8762590Z     >>> rpc.shutdown()
2024-11-01T17:51:20.8762595Z 
2024-11-01T17:51:20.8762859Z     Furthermore, a more practical example that is combined with
2024-11-01T17:51:20.8763541Z     `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2024-11-01T17:51:20.8764020Z     can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2024-11-01T17:51:20.8764098Z 
2024-11-01T17:51:20.8764530Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8764548Z 
2024-11-01T17:51:20.8903464Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130.
2024-11-01T17:51:20.8903932Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8903972Z 
2024-11-01T17:51:20.8904293Z DistributedOptimizer takes remote references to parameters scattered
2024-11-01T17:51:20.8904663Z across workers and applies the given optimizer locally for each parameter.
2024-11-01T17:51:20.8904669Z 
2024-11-01T17:51:20.8905009Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2024-11-01T17:51:20.8905218Z to retrieve the gradients for specific parameters.
2024-11-01T17:51:20.8905236Z 
2024-11-01T17:51:20.8905364Z Concurrent calls to
2024-11-01T17:51:20.8905667Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2024-11-01T17:51:20.8905877Z either from the same or different clients, will
2024-11-01T17:51:20.8906283Z be serialized on each worker -- as each worker's optimizer can only work
2024-11-01T17:51:20.8906833Z on one set of gradients at a time. However, there is no guarantee that
2024-11-01T17:51:20.8907264Z the full forward-backward-optimizer sequence will execute for one client
2024-11-01T17:51:20.8907612Z at a time. This means that the gradients being applied may not correspond
2024-11-01T17:51:20.8908165Z to the latest forward pass executed on a given worker. Also, there is no
2024-11-01T17:51:20.8908311Z guaranteed ordering across workers.
2024-11-01T17:51:20.8908318Z 
2024-11-01T17:51:20.8908680Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2024-11-01T17:51:20.8909014Z by default, so that optimizer updates are not blocked by the Python Global
2024-11-01T17:51:20.8909392Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2024-11-01T17:51:20.8909735Z Model Parallel). This feature is currently enabled for most optimizers. You
2024-11-01T17:51:20.8910119Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2024-11-01T17:51:20.8910256Z for your own custom optimizers.
2024-11-01T17:51:20.8910262Z 
2024-11-01T17:51:20.8910373Z Args:
2024-11-01T17:51:20.8910645Z     optimizer_class (optim.Optimizer): the class of optimizer to
2024-11-01T17:51:20.8910782Z         instantiate on each worker.
2024-11-01T17:51:20.8911107Z     params_rref (list[RRef]): list of RRefs to local or remote parameters
2024-11-01T17:51:20.8911219Z         to optimize.
2024-11-01T17:51:20.8911539Z     args: arguments to pass to the optimizer constructor on each worker.
2024-11-01T17:51:20.8911854Z     kwargs: arguments to pass to the optimizer constructor on each worker.
2024-11-01T17:51:20.8911860Z 
2024-11-01T17:51:20.8911998Z Example::
2024-11-01T17:51:20.8912158Z     >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:20.8912398Z     >>> import torch.distributed.autograd as dist_autograd
2024-11-01T17:51:20.8912578Z     >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:20.8912712Z     >>> from torch import optim
2024-11-01T17:51:20.8912998Z     >>> from torch.distributed.optim import DistributedOptimizer
2024-11-01T17:51:20.8913101Z     >>>
2024-11-01T17:51:20.8913297Z     >>> with dist_autograd.context() as context_id:
2024-11-01T17:51:20.8913418Z     >>>   # Forward pass.
2024-11-01T17:51:20.8913717Z     >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2024-11-01T17:51:20.8914122Z     >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2024-11-01T17:51:20.8914296Z     >>>   loss = rref1.to_here() + rref2.to_here()
2024-11-01T17:51:20.8914413Z     >>>
2024-11-01T17:51:20.8914534Z     >>>   # Backward pass.
2024-11-01T17:51:20.8914747Z     >>>   dist_autograd.backward(context_id, [loss.sum()])
2024-11-01T17:51:20.8914966Z     >>>
2024-11-01T17:51:20.8915080Z     >>>   # Optimizer.
2024-11-01T17:51:20.8915275Z     >>>   dist_optim = DistributedOptimizer(
2024-11-01T17:51:20.8915461Z     >>>      optim.SGD,
2024-11-01T17:51:20.8915660Z     >>>      [rref1, rref2],
2024-11-01T17:51:20.8915796Z     >>>      lr=0.05,
2024-11-01T17:51:20.8915896Z     >>>   )
2024-11-01T17:51:20.8916054Z     >>>   dist_optim.step(context_id)
2024-11-01T17:51:20.8916060Z 
2024-11-01T17:51:20.8916306Z __ https://github.com/pytorch/tutorials/pull/1465
2024-11-01T17:51:20.8916315Z 
2024-11-01T17:51:20.8917005Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8917015Z 
2024-11-01T17:51:20.8918836Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9.
2024-11-01T17:51:20.8919307Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.8919346Z 
2024-11-01T17:51:20.8920023Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2024-11-01T17:51:20.8920245Z This optimizer runs local optimizer at every step.
2024-11-01T17:51:20.8920815Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.
2024-11-01T17:51:20.8920821Z 
2024-11-01T17:51:20.8920933Z Args:
2024-11-01T17:51:20.8921068Z     optim: The local optimizer.
2024-11-01T17:51:20.8921464Z     averager: A model averager instance to run post-localSGD algorithm.
2024-11-01T17:51:20.8921470Z 
2024-11-01T17:51:20.8921682Z Example::
2024-11-01T17:51:20.8921689Z 
2024-11-01T17:51:20.8921886Z     >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:20.8922003Z     >>> import torch
2024-11-01T17:51:20.8922157Z     >>> import torch.distributed as dist
2024-11-01T17:51:20.8922559Z     >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2024-11-01T17:51:20.8922690Z     >>> import torch.nn as nn
2024-11-01T17:51:20.8922984Z     >>> from torch.distributed.optim import PostLocalSGDOptimizer
2024-11-01T17:51:20.8923366Z     >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2024-11-01T17:51:20.8923498Z     >>>   PostLocalSGDState,
2024-11-01T17:51:20.8923641Z     >>>   post_localSGD_hook,
2024-11-01T17:51:20.8923741Z     >>> )
2024-11-01T17:51:20.8923857Z     >>>
2024-11-01T17:51:20.8924077Z     >>> model = nn.parallel.DistributedDataParallel(
2024-11-01T17:51:20.8924288Z     >>>    module, device_ids=[rank], output_device=rank
2024-11-01T17:51:20.8924388Z     >>> )
2024-11-01T17:51:20.8924494Z     >>>
2024-11-01T17:51:20.8924779Z     >>> # Register a post-localSGD communication hook.
2024-11-01T17:51:20.8925187Z     >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2024-11-01T17:51:20.8925422Z     >>> model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:20.8925524Z     >>>
2024-11-01T17:51:20.8925910Z     >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2024-11-01T17:51:20.8926281Z     >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2024-11-01T17:51:20.8926519Z     >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2024-11-01T17:51:20.8926831Z     >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2024-11-01T17:51:20.8926982Z     >>> opt = PostLocalSGDOptimizer(
2024-11-01T17:51:20.8927122Z     >>>     optim=local_optim,
2024-11-01T17:51:20.8927469Z     >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2024-11-01T17:51:20.8927591Z     >>> )
2024-11-01T17:51:20.8927689Z     >>>
2024-11-01T17:51:20.8928036Z     >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2024-11-01T17:51:20.8928590Z     >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2024-11-01T17:51:20.8929232Z     >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2024-11-01T17:51:20.8929449Z     >>> for step in range(0, 200):
2024-11-01T17:51:20.8929570Z     >>>    opt.zero_grad()
2024-11-01T17:51:20.8929734Z     >>>    loss = loss_fn(output, labels)
2024-11-01T17:51:20.8929852Z     >>>    loss.backward()
2024-11-01T17:51:20.8929965Z     >>>    opt.step()
2024-11-01T17:51:20.8929972Z 
2024-11-01T17:51:20.8930415Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.8930420Z 
2024-11-01T17:51:20.9049026Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282.
2024-11-01T17:51:20.9049491Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.9049498Z 
2024-11-01T17:51:20.9050129Z Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2024-11-01T17:51:20.9050144Z 
2024-11-01T17:51:20.9050324Z The sharing is done as described by ZeRO_.
2024-11-01T17:51:20.9050330Z 
2024-11-01T17:51:20.9050539Z The local optimizer instance in each rank is only
2024-11-01T17:51:20.9050875Z responsible for updating approximately ``1 / world_size`` parameters and
2024-11-01T17:51:20.9051166Z hence only needs to keep ``1 / world_size`` optimizer states. After
2024-11-01T17:51:20.9051529Z parameters are updated locally, each rank will broadcast its parameters to
2024-11-01T17:51:20.9051794Z all other peers to keep all model replicas in the same state.
2024-11-01T17:51:20.9052311Z ``ZeroRedundancyOptimizer`` can be used in conjunction with
2024-11-01T17:51:20.9052771Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2024-11-01T17:51:20.9052908Z memory consumption.
2024-11-01T17:51:20.9052914Z 
2024-11-01T17:51:20.9053338Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2024-11-01T17:51:20.9053702Z of parameters at each rank. Each parameter belongs to a single rank and is
2024-11-01T17:51:20.9054053Z not divided among ranks. The partition is arbitrary and might not match the
2024-11-01T17:51:20.9054222Z the parameter registration or usage order.
2024-11-01T17:51:20.9054228Z 
2024-11-01T17:51:20.9054348Z Arguments:
2024-11-01T17:51:20.9054628Z     params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2024-11-01T17:51:20.9054921Z         or :class:`dict` s giving all parameters, which will be sharded
2024-11-01T17:51:20.9055035Z         across ranks.
2024-11-01T17:51:20.9055041Z 
2024-11-01T17:51:20.9055171Z Keyword Args:
2024-11-01T17:51:20.9055486Z     optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2024-11-01T17:51:20.9055595Z         optimizer.
2024-11-01T17:51:20.9055897Z     process_group (``ProcessGroup``, optional): ``torch.distributed``
2024-11-01T17:51:20.9056174Z         ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2024-11-01T17:51:20.9056393Z         :meth:`torch.distributed.init_process_group`).
2024-11-01T17:51:20.9056710Z     parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2024-11-01T17:51:20.9057026Z         packed into buckets to speed up communication, and ``param.data``
2024-11-01T17:51:20.9057323Z         fields point to bucket views at different offsets; if ``False``,
2024-11-01T17:51:20.9057625Z         each individual parameter is communicated separately, and each
2024-11-01T17:51:20.9057836Z         ``params.data`` stays intact (default: ``False``).
2024-11-01T17:51:20.9058112Z     overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2024-11-01T17:51:20.9058482Z         overlapped with :class:`DistributedDataParallel` 's gradient
2024-11-01T17:51:20.9058775Z         synchronization; this requires (1) either a functional optimizer
2024-11-01T17:51:20.9059052Z         for the ``optimizer_class`` argument or one with a functional
2024-11-01T17:51:20.9059298Z         equivalent and (2) registering a DDP communication hook
2024-11-01T17:51:20.9059693Z         constructed from one of the functions in ``ddp_zero_hook.py``;
2024-11-01T17:51:20.9059925Z         parameters are packed into buckets matching those in
2024-11-01T17:51:20.9060151Z         :class:`DistributedDataParallel`, meaning that the
2024-11-01T17:51:20.9060375Z         ``parameters_as_bucket_view`` argument is ignored.
2024-11-01T17:51:20.9060653Z         If ``False``, :meth:`step` runs disjointly after the backward pass
2024-11-01T17:51:20.9060782Z         (per normal).
2024-11-01T17:51:20.9060906Z         (default: ``False``)
2024-11-01T17:51:20.9061227Z     **defaults: any trailing arguments, which are forwarded to the local
2024-11-01T17:51:20.9061338Z         optimizer.
2024-11-01T17:51:20.9061344Z 
2024-11-01T17:51:20.9061476Z Example::
2024-11-01T17:51:20.9061482Z 
2024-11-01T17:51:20.9061618Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.9061743Z     >>> import torch.nn as nn
2024-11-01T17:51:20.9062046Z     >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2024-11-01T17:51:20.9062342Z     >>> from torch.nn.parallel import DistributedDataParallel as DDP
2024-11-01T17:51:20.9062683Z     >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2024-11-01T17:51:20.9062836Z     >>> ddp = DDP(model, device_ids=[rank])
2024-11-01T17:51:20.9062994Z     >>> opt = ZeroRedundancyOptimizer(
2024-11-01T17:51:20.9063133Z     >>>     ddp.parameters(),
2024-11-01T17:51:20.9063299Z     >>>     optimizer_class=torch.optim.Adam,
2024-11-01T17:51:20.9063418Z     >>>     lr=0.01
2024-11-01T17:51:20.9063519Z     >>> )
2024-11-01T17:51:20.9063725Z     >>> ddp(inputs).sum().backward()
2024-11-01T17:51:20.9063835Z     >>> opt.step()
2024-11-01T17:51:20.9063840Z 
2024-11-01T17:51:20.9063948Z .. warning::
2024-11-01T17:51:20.9064252Z     Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2024-11-01T17:51:20.9064518Z     passed-in parameters are the same dense type.
2024-11-01T17:51:20.9064523Z 
2024-11-01T17:51:20.9064649Z .. warning::
2024-11-01T17:51:20.9064959Z     If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2024-11-01T17:51:20.9065266Z     the way that overlapping :class:`DistributedDataParallel` with
2024-11-01T17:51:20.9065622Z     :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2024-11-01T17:51:20.9066015Z     two or three training iterations do not perform parameter updates in
2024-11-01T17:51:20.9066294Z     the optimizer step, depending on if ``static_graph=False`` or
2024-11-01T17:51:20.9066548Z     ``static_graph=True``, respectively. This is because it needs
2024-11-01T17:51:20.9066816Z     information about the gradient bucketing strategy used by
2024-11-01T17:51:20.9067123Z     :class:`DistributedDataParallel`, which is not finalized until the
2024-11-01T17:51:20.9067419Z     second forward pass if ``static_graph=False`` or until the third
2024-11-01T17:51:20.9067723Z     forward pass if ``static_graph=True``. To adjust for this, one option
2024-11-01T17:51:20.9067866Z     is to prepend dummy inputs.
2024-11-01T17:51:20.9067885Z 
2024-11-01T17:51:20.9068233Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2024-11-01T17:51:20.9068238Z 
2024-11-01T17:51:20.9068406Z .. _ZeRO: https://arxiv.org/abs/1910.02054
2024-11-01T17:51:20.9068411Z 
2024-11-01T17:51:20.9068429Z 
2024-11-01T17:51:20.9068863Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.9068868Z 
2024-11-01T17:51:20.9263416Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28.
2024-11-01T17:51:20.9263968Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.9263975Z 
2024-11-01T17:51:20.9264343Z Custom reducer class that can be used to specify a custom operation that
2024-11-01T17:51:20.9264649Z reduces losses of multiple microbatches into one value.
2024-11-01T17:51:20.9264869Z 
2024-11-01T17:51:20.9264975Z Example:
2024-11-01T17:51:20.9265133Z >>> # xdoctest: +SKIP
2024-11-01T17:51:20.9265336Z >>> sum_reducer = _CustomReducer(
2024-11-01T17:51:20.9265470Z >>>     torch.tensor(0.0),
2024-11-01T17:51:20.9265588Z >>>     lambda a, b: a + b
2024-11-01T17:51:20.9265686Z >>> )
2024-11-01T17:51:20.9265691Z 
2024-11-01T17:51:20.9266230Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.9266236Z 
2024-11-01T17:51:20.9795445Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6.
2024-11-01T17:51:20.9796924Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.9797461Z 
2024-11-01T17:51:20.9797893Z A decorator for a function indicating that the return value of the function
2024-11-01T17:51:20.9798724Z is guaranteed to be a :class:`~torch.futures.Future` object and this
2024-11-01T17:51:20.9799572Z function can run asynchronously on the RPC callee. More specifically, the
2024-11-01T17:51:20.9800437Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2024-11-01T17:51:20.9801289Z function and installs subsequent processing steps as a callback to that
2024-11-01T17:51:20.9802137Z :class:`~torch.futures.Future`. The installed callback will read the value
2024-11-01T17:51:20.9802932Z from the :class:`~torch.futures.Future` when completed and send the
2024-11-01T17:51:20.9803673Z value back as the RPC response. That also means the returned
2024-11-01T17:51:20.9804716Z :class:`~torch.futures.Future` only exists on the callee side and is never
2024-11-01T17:51:20.9805596Z sent through RPC. This decorator is useful when the wrapped function's
2024-11-01T17:51:20.9806377Z (``fn``) execution needs to pause and resume due to, e.g., containing
2024-11-01T17:51:20.9807420Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2024-11-01T17:51:20.9807914Z 
2024-11-01T17:51:20.9808267Z .. note:: To enable asynchronous execution, applications must pass the
2024-11-01T17:51:20.9809066Z     function object returned by this decorator to RPC APIs. If RPC detected
2024-11-01T17:51:20.9809886Z     attributes installed by this decorator, it knows that this function
2024-11-01T17:51:20.9810633Z     returns a ``Future`` object and will handle that accordingly.
2024-11-01T17:51:20.9811387Z     However, this does not mean this decorator has to be outmost one when
2024-11-01T17:51:20.9812209Z     defining a function. For example, when combined with ``@staticmethod``
2024-11-01T17:51:20.9813023Z     or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2024-11-01T17:51:20.9813818Z     inner decorator to allow the target function be recognized as a static
2024-11-01T17:51:20.9814655Z     or class function. This target function can still execute asynchronously
2024-11-01T17:51:20.9815502Z     because, when accessed, the static or class method preserves attributes
2024-11-01T17:51:20.9816273Z     installed by ``@rpc.functions.async_execution``.
2024-11-01T17:51:20.9816646Z 
2024-11-01T17:51:20.9816651Z 
2024-11-01T17:51:20.9816776Z Example::
2024-11-01T17:51:20.9817241Z     The returned :class:`~torch.futures.Future` object can come from
2024-11-01T17:51:20.9817876Z     :meth:`~torch.distributed.rpc.rpc_async`,
2024-11-01T17:51:20.9818549Z     :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2024-11-01T17:51:20.9819298Z     constructor. The example below shows directly using the
2024-11-01T17:51:20.9819904Z     :class:`~torch.futures.Future` returned by
2024-11-01T17:51:20.9820422Z     :meth:`~torch.futures.Future.then`.
2024-11-01T17:51:20.9820743Z 
2024-11-01T17:51:20.9820917Z     >>> from torch.distributed import rpc
2024-11-01T17:51:20.9821344Z     >>>
2024-11-01T17:51:20.9821658Z     >>> # omitting setup and shutdown RPC
2024-11-01T17:51:20.9822090Z     >>>
2024-11-01T17:51:20.9822369Z     >>> # On all workers
2024-11-01T17:51:20.9822754Z     >>> @rpc.functions.async_execution
2024-11-01T17:51:20.9823380Z     >>> def async_add_chained(to, x, y, z):
2024-11-01T17:51:20.9823997Z     >>>     # This function runs on "worker1" and returns immediately when
2024-11-01T17:51:20.9824730Z     >>>     # the callback is installed through the `then(cb)` API. In the
2024-11-01T17:51:20.9825466Z     >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2024-11-01T17:51:20.9826165Z     >>>     # When the return value of that `rpc_async` arrives at
2024-11-01T17:51:20.9826847Z     >>>     # "worker1", "worker1" will run the lambda function accordingly
2024-11-01T17:51:20.9827568Z     >>>     # and set the value for the previously returned `Future`, which
2024-11-01T17:51:20.9828308Z     >>>     # will then trigger RPC to send the result back to "worker0".
2024-11-01T17:51:20.9829002Z     >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:20.9829571Z     >>>         lambda fut: fut.wait() + z
2024-11-01T17:51:20.9829994Z     >>>     )
2024-11-01T17:51:20.9830273Z     >>>
2024-11-01T17:51:20.9830548Z     >>> # On worker0
2024-11-01T17:51:20.9830885Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:20.9831250Z     >>> ret = rpc.rpc_sync(
2024-11-01T17:51:20.9831616Z     >>>     "worker1",
2024-11-01T17:51:20.9831942Z     >>>     async_add_chained,
2024-11-01T17:51:20.9832370Z     >>>     args=("worker2", torch.ones(2), 1, 1)
2024-11-01T17:51:20.9832815Z     >>> )
2024-11-01T17:51:20.9833145Z     >>> print(ret)  # prints tensor([3., 3.])
2024-11-01T17:51:20.9833470Z 
2024-11-01T17:51:20.9833809Z     When combined with TorchScript decorators, this decorator must be the
2024-11-01T17:51:20.9834660Z     outmost one.
2024-11-01T17:51:20.9834853Z 
2024-11-01T17:51:20.9834989Z     >>> from torch import Tensor
2024-11-01T17:51:20.9835420Z     >>> from torch.futures import Future
2024-11-01T17:51:20.9835899Z     >>> from torch.distributed import rpc
2024-11-01T17:51:20.9836331Z     >>>
2024-11-01T17:51:20.9836644Z     >>> # omitting setup and shutdown RPC
2024-11-01T17:51:20.9837107Z     >>>
2024-11-01T17:51:20.9837458Z     >>> # On all workers
2024-11-01T17:51:20.9837836Z     >>> @torch.jit.script
2024-11-01T17:51:20.9838616Z     >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2024-11-01T17:51:20.9839253Z     >>>     return x + y
2024-11-01T17:51:20.9839624Z     >>>
2024-11-01T17:51:20.9839932Z     >>> @rpc.functions.async_execution
2024-11-01T17:51:20.9840376Z     >>> @torch.jit.script
2024-11-01T17:51:20.9840980Z     >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2024-11-01T17:51:20.9841655Z     >>>     return rpc.rpc_async(to, script_add, (x, y))
2024-11-01T17:51:20.9842191Z     >>>
2024-11-01T17:51:20.9842474Z     >>> # On worker0
2024-11-01T17:51:20.9842810Z     >>> ret = rpc.rpc_sync(
2024-11-01T17:51:20.9843179Z     >>>     "worker1",
2024-11-01T17:51:20.9843523Z     >>>     async_add,
2024-11-01T17:51:20.9843903Z     >>>     args=("worker2", torch.ones(2), 1)
2024-11-01T17:51:20.9844326Z     >>> )
2024-11-01T17:51:20.9844674Z     >>> print(ret)  # prints tensor([2., 2.])
2024-11-01T17:51:20.9845020Z 
2024-11-01T17:51:20.9845340Z     When combined with static or class method, this decorator must be the
2024-11-01T17:51:20.9845953Z     inner one.
2024-11-01T17:51:20.9846133Z 
2024-11-01T17:51:20.9846307Z     >>> from torch.distributed import rpc
2024-11-01T17:51:20.9846721Z     >>>
2024-11-01T17:51:20.9847042Z     >>> # omitting setup and shutdown RPC
2024-11-01T17:51:20.9847470Z     >>>
2024-11-01T17:51:20.9847744Z     >>> # On all workers
2024-11-01T17:51:20.9848122Z     >>> class AsyncExecutionClass:
2024-11-01T17:51:20.9848503Z     >>>
2024-11-01T17:51:20.9848784Z     >>>     @staticmethod
2024-11-01T17:51:20.9849172Z     >>>     @rpc.functions.async_execution
2024-11-01T17:51:20.9849666Z     >>>     def static_async_add(to, x, y, z):
2024-11-01T17:51:20.9850246Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:20.9850833Z     >>>             lambda fut: fut.wait() + z
2024-11-01T17:51:20.9851432Z     >>>         )
2024-11-01T17:51:20.9851726Z     >>>
2024-11-01T17:51:20.9852001Z     >>>     @classmethod
2024-11-01T17:51:20.9852388Z     >>>     @rpc.functions.async_execution
2024-11-01T17:51:20.9852887Z     >>>     def class_async_add(cls, to, x, y, z):
2024-11-01T17:51:20.9853403Z     >>>         ret_fut = torch.futures.Future()
2024-11-01T17:51:20.9853968Z     >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:20.9854657Z     >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2024-11-01T17:51:20.9855174Z     >>>         )
2024-11-01T17:51:20.9855494Z     >>>         return ret_fut
2024-11-01T17:51:20.9855846Z     >>>
2024-11-01T17:51:20.9856270Z     >>>     @rpc.functions.async_execution
2024-11-01T17:51:20.9856812Z     >>>     def bound_async_add(self, to, x, y, z):
2024-11-01T17:51:20.9857416Z     >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:20.9857996Z     >>>             lambda fut: fut.wait() + z
2024-11-01T17:51:20.9858436Z     >>>         )
2024-11-01T17:51:20.9858714Z     >>>
2024-11-01T17:51:20.9858986Z     >>> # On worker0
2024-11-01T17:51:20.9859326Z     >>> ret = rpc.rpc_sync(
2024-11-01T17:51:20.9859689Z     >>>     "worker1",
2024-11-01T17:51:20.9860090Z     >>>     AsyncExecutionClass.static_async_add,
2024-11-01T17:51:20.9860610Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2024-11-01T17:51:20.9861045Z     >>> )
2024-11-01T17:51:20.9861367Z     >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:20.9861793Z     >>>
2024-11-01T17:51:20.9862065Z     >>> ret = rpc.rpc_sync(
2024-11-01T17:51:20.9862406Z     >>>     "worker1",
2024-11-01T17:51:20.9862887Z     >>>     AsyncExecutionClass.class_async_add,
2024-11-01T17:51:20.9863410Z     >>>     args=("worker2", torch.ones(2), 1, 2)
2024-11-01T17:51:20.9863851Z     >>> )
2024-11-01T17:51:20.9864173Z     >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:20.9864495Z 
2024-11-01T17:51:20.9864717Z     This decorator also works with RRef helpers, i.e., .
2024-11-01T17:51:20.9865313Z     :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2024-11-01T17:51:20.9865896Z     :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2024-11-01T17:51:20.9866480Z     :meth:`torch.distributed.rpc.RRef.remote`.
2024-11-01T17:51:20.9866835Z 
2024-11-01T17:51:20.9867009Z     >>> from torch.distributed import rpc
2024-11-01T17:51:20.9867440Z     >>>
2024-11-01T17:51:20.9867772Z     >>> # reuse the AsyncExecutionClass class above
2024-11-01T17:51:20.9868366Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2024-11-01T17:51:20.9869069Z     >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2024-11-01T17:51:20.9869716Z     >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:20.9870149Z     >>>
2024-11-01T17:51:20.9870508Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2024-11-01T17:51:20.9871247Z     >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2024-11-01T17:51:20.9871926Z     >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:20.9872367Z     >>>
2024-11-01T17:51:20.9872740Z     >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2024-11-01T17:51:20.9873470Z     >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2024-11-01T17:51:20.9874298Z     >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:20.9874639Z 
2024-11-01T17:51:20.9875119Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.9875643Z 
2024-11-01T17:51:20.9876844Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108.
2024-11-01T17:51:20.9878427Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:20.9878971Z 
2024-11-01T17:51:20.9879255Z Set device mapping between each RPC caller and callee pair. This
2024-11-01T17:51:20.9879962Z function can be called multiple times to incrementally add
2024-11-01T17:51:20.9880636Z device placement configurations.
2024-11-01T17:51:20.9880913Z 
2024-11-01T17:51:20.9881028Z Args:
2024-11-01T17:51:20.9881292Z     to (str): Callee name.
2024-11-01T17:51:20.9881811Z     device_map (Dict of int, str, or torch.device): Device placement
2024-11-01T17:51:20.9882526Z         mappings from this worker to the callee. This map must be
2024-11-01T17:51:20.9883064Z         invertible.
2024-11-01T17:51:20.9883258Z 
2024-11-01T17:51:20.9883373Z Example:
2024-11-01T17:51:20.9883682Z     >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:20.9884097Z     >>> # both workers
2024-11-01T17:51:20.9884427Z     >>> def add(x, y):
2024-11-01T17:51:20.9884903Z     >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2024-11-01T17:51:20.9885400Z     >>>     return x + y, (x + y).to(2)
2024-11-01T17:51:20.9885801Z     >>>
2024-11-01T17:51:20.9894936Z     >>> # on worker 0
2024-11-01T17:51:20.9895422Z     >>> options = TensorPipeRpcBackendOptions(
2024-11-01T17:51:20.9895935Z     >>>     num_worker_threads=8,
2024-11-01T17:51:20.9896369Z     >>>     device_maps={"worker1": {0: 1}}
2024-11-01T17:51:20.9897019Z     >>>     # maps worker0's cuda:0 to worker1's cuda:1
2024-11-01T17:51:20.9897488Z     >>> )
2024-11-01T17:51:20.9897829Z     >>> options.set_device_map("worker1", {1: 2})
2024-11-01T17:51:20.9898427Z     >>> # maps worker0's cuda:1 to worker1's cuda:2
2024-11-01T17:51:20.9898869Z     >>>
2024-11-01T17:51:20.9899136Z     >>> rpc.init_rpc(
2024-11-01T17:51:20.9899436Z     >>>     "worker0",
2024-11-01T17:51:20.9899756Z     >>>     rank=0,
2024-11-01T17:51:20.9900064Z     >>>     world_size=2,
2024-11-01T17:51:20.9900624Z     >>>     backend=rpc.BackendType.TENSORPIPE,
2024-11-01T17:51:20.9901123Z     >>>     rpc_backend_options=options
2024-11-01T17:51:20.9901538Z     >>> )
2024-11-01T17:51:20.9901800Z     >>>
2024-11-01T17:51:20.9902074Z     >>> x = torch.ones(2)
2024-11-01T17:51:20.9902526Z     >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2024-11-01T17:51:20.9903210Z     >>> # The first argument will be moved to cuda:1 on worker1. When
2024-11-01T17:51:20.9903916Z     >>> # sending the return value back, it will follow the invert of
2024-11-01T17:51:20.9904626Z     >>> # the device map, and hence will be moved back to cuda:0 and
2024-11-01T17:51:20.9905186Z     >>> # cuda:1 on worker0
2024-11-01T17:51:20.9905714Z     >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2024-11-01T17:51:20.9906359Z     >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2024-11-01T17:51:20.9907026Z 
2024-11-01T17:51:20.9907466Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:20.9908006Z 
2024-11-01T17:51:21.0961333Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_func_map.py line=32.
2024-11-01T17:51:21.0962864Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.0963458Z 
2024-11-01T17:51:21.0963853Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2024-11-01T17:51:21.0964851Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2024-11-01T17:51:21.0965859Z the local components of :class:`DTensor`, call the function, and wrap the outputs to
2024-11-01T17:51:21.0966639Z :class:`DTensor` according to the ``out_placements``.
2024-11-01T17:51:21.0967016Z 
2024-11-01T17:51:21.0967126Z Args:
2024-11-01T17:51:21.0967572Z     func (Callable): the function to be applied on each local shard of
2024-11-01T17:51:21.0968169Z         :class:`DTensor` s.
2024-11-01T17:51:21.0968736Z     out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2024-11-01T17:51:21.0969698Z         the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2024-11-01T17:51:21.0970612Z         If the flattened ``output`` is a single value, the ``out_placements`` should be
2024-11-01T17:51:21.0971486Z         of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2024-11-01T17:51:21.0972648Z         values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2024-11-01T17:51:21.0973346Z         mapping to the flattened ``output``.
2024-11-01T17:51:21.0973981Z         Besides, for :class:`Tensor` output, we use `PlacementType` as its
2024-11-01T17:51:21.0975039Z         placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2024-11-01T17:51:21.0975978Z         should be `None`.
2024-11-01T17:51:21.0976818Z         Note that the only exception is when no :class:`DTensor` argument is passed
2024-11-01T17:51:21.0978008Z         in. In this case, even if `out_placements` is not `None`, the result function
2024-11-01T17:51:21.0978900Z         should ignore the desired placements because the function is not running with
2024-11-01T17:51:21.0979639Z         :class:`DTensor` s.
2024-11-01T17:51:21.0980112Z     in_placements (Tuple[`PlacementType`, ...], optional):
2024-11-01T17:51:21.0980935Z         the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2024-11-01T17:51:21.0981865Z         If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2024-11-01T17:51:21.0982707Z         placements of each :class:`DTensor` argument is the same as the required
2024-11-01T17:51:21.0983474Z         placements or not. If the placements are not the same and
2024-11-01T17:51:21.0984259Z         ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2024-11-01T17:51:21.0985306Z         ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2024-11-01T17:51:21.0986210Z         the required sharding placements before passing its local tensor to ``func``.
2024-11-01T17:51:21.0987090Z         The only exception is when required placements are not ``None`` and the
2024-11-01T17:51:21.0987948Z         argument is a :class:`torch.Tensor`. In this case, the placements examination
2024-11-01T17:51:21.0988823Z         will be skipped and the argument will be directly passed to ``func``.
2024-11-01T17:51:21.0989658Z         If ``in_placements`` is ``None``, no placements examination will be performed.
2024-11-01T17:51:21.0990289Z         Default: None
2024-11-01T17:51:21.0990686Z     device_mesh (:class:`DeviceMesh`, optional):
2024-11-01T17:51:21.0991381Z         the device mesh that all the :class:`DTensor` s are placed on. If not
2024-11-01T17:51:21.0992347Z         specified, this will be inferred from the input :class:`DTensor` s' device
2024-11-01T17:51:21.0993236Z         mesh. `local_map` requires every :class:`DTensor` s to be placed on the same
2024-11-01T17:51:21.0994062Z         device mesh. Default: None.
2024-11-01T17:51:21.0994527Z     redistribute_inputs (bool, optional):
2024-11-01T17:51:21.0995229Z         the bool value indicating whether to reshard the input :class:`DTensor` s when
2024-11-01T17:51:21.0996132Z         their placements are different from the required input placements. If this
2024-11-01T17:51:21.0997001Z         value is ``False`` and some :class:`DTensor` input has a different placement,
2024-11-01T17:51:21.0997725Z         an exception will be raised. Default: False.
2024-11-01T17:51:21.0998101Z 
2024-11-01T17:51:21.0998203Z Returns:
2024-11-01T17:51:21.0998752Z     A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2024-11-01T17:51:21.0999659Z     and returns a :class:`DTensor` constructed from the return value of ``func``.
2024-11-01T17:51:21.1000182Z 
2024-11-01T17:51:21.1000296Z Raises:
2024-11-01T17:51:21.1000819Z     AssertionError: If the input :class:`DTensor` is not placed on the same device
2024-11-01T17:51:21.1001713Z         mesh, or if they are placed on a different device mesh than the ``device_mesh``
2024-11-01T17:51:21.1002396Z         argument passed in.
2024-11-01T17:51:21.1002650Z 
2024-11-01T17:51:21.1003085Z     AssertionError: For any non-DTensor output, we require its corresponding
2024-11-01T17:51:21.1004120Z         output placement in ``out_placements`` be None. An AssertionError will be raised
2024-11-01T17:51:21.1004810Z         if this is not the case.
2024-11-01T17:51:21.1005077Z 
2024-11-01T17:51:21.1005457Z     ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2024-11-01T17:51:21.1006205Z         a redistribution according to ``in_placements``.
2024-11-01T17:51:21.1006924Z 
2024-11-01T17:51:21.1007072Z Example:
2024-11-01T17:51:21.1007394Z     >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:21.1007905Z     >>> def mm_allreduce_forward(device_mesh, W, X):
2024-11-01T17:51:21.1008447Z     >>>     partial_sum_tensor = torch.mm(W, X)
2024-11-01T17:51:21.1009129Z     >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2024-11-01T17:51:21.1009756Z     >>>     return reduced_tensor
2024-11-01T17:51:21.1010133Z     >>>
2024-11-01T17:51:21.1010468Z     >>> W = torch.randn(12, 8, requires_grad=False)
2024-11-01T17:51:21.1010999Z     >>> X = torch.randn(8, 16, requires_grad=False)
2024-11-01T17:51:21.1011476Z     >>> Y = torch.mm(W, X)
2024-11-01T17:51:21.1012103Z     >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2024-11-01T17:51:21.1012909Z     >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2024-11-01T17:51:21.1013465Z     >>>
2024-11-01T17:51:21.1014008Z     >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
2024-11-01T17:51:21.1014744Z     >>> local_mm_allreduce_forward = local_map(
2024-11-01T17:51:21.1015212Z     >>>     mm_allreduce_forward,
2024-11-01T17:51:21.1015805Z     >>>     out_placements=[Replicate()],
2024-11-01T17:51:21.1016294Z     >>>     in_placements=[col_wise, row_wise],
2024-11-01T17:51:21.1016764Z     >>>     device_mesh=device_mesh,
2024-11-01T17:51:21.1017159Z     >>> )
2024-11-01T17:51:21.1017423Z     >>>
2024-11-01T17:51:21.1018035Z     >>> W_dt = distribute_tensor(W, device_mesh, (col_wise))  # col-wisely sharded W tensor
2024-11-01T17:51:21.1019057Z     >>> X_dt = distribute_tensor(X, device_mesh, (row_wise))  # row-wisely sharded X tensor
2024-11-01T17:51:21.1020098Z     >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt)  # apply local_mm_allreduce_forward to DTensors
2024-11-01T17:51:21.1020769Z 
2024-11-01T17:51:21.1021079Z .. note:: This API is currently experimental and subject to change
2024-11-01T17:51:21.1021529Z 
2024-11-01T17:51:21.1021970Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.1022482Z 
2024-11-01T17:51:21.1023713Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25.
2024-11-01T17:51:21.1025317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.1025845Z 
2024-11-01T17:51:21.1026258Z :meth:`register_sharding` is an experimental API that allows users to register sharding
2024-11-01T17:51:21.1027179Z strategies for an operator when the tensor inputs and outputs are DTensor.
2024-11-01T17:51:21.1028160Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2024-11-01T17:51:21.1029096Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2024-11-01T17:51:21.1030040Z when users would like to overwrite default sharding strategies of existing operators.
2024-11-01T17:51:21.1030625Z 
2024-11-01T17:51:21.1030722Z Args:
2024-11-01T17:51:21.1031048Z     op (Union[OpOverload, List[OpOverload]]):
2024-11-01T17:51:21.1031703Z         An op or a list of ops to register the customized sharding function.
2024-11-01T17:51:21.1032183Z 
2024-11-01T17:51:21.1032286Z Returns:
2024-11-01T17:51:21.1032839Z     A function decorator which can be used to wrap a function that defines the sharding
2024-11-01T17:51:21.1033800Z     strategy for the operator specified in ``op``. The defined sharding strategy will be
2024-11-01T17:51:21.1034913Z     registered to DTensor and will override the default sharding strategy if DTensor has
2024-11-01T17:51:21.1036104Z     already implemented the operator. The customized sharding function takes the same inputs
2024-11-01T17:51:21.1037072Z     as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2024-11-01T17:51:21.1038093Z     replaced by a tensor-like object that DTensor uses internally). The function should
2024-11-01T17:51:21.1039151Z     return a sequence of 2-tuples, each specifying acceptable output placements and its
2024-11-01T17:51:21.1039871Z     corresponding intput placements.
2024-11-01T17:51:21.1040187Z 
2024-11-01T17:51:21.1040295Z Example:
2024-11-01T17:51:21.1040607Z     >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:21.1041100Z     >>> @register_sharding(aten._softmax.default)
2024-11-01T17:51:21.1041653Z     >>> def custom_softmax_sharding(x, dim, half_to_float):
2024-11-01T17:51:21.1042249Z     >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2024-11-01T17:51:21.1042771Z     >>>     acceptable_shardings = []
2024-11-01T17:51:21.1043171Z     >>>
2024-11-01T17:51:21.1043578Z     >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2024-11-01T17:51:21.1044204Z     >>>     acceptable_shardings.append(all_replicate)
2024-11-01T17:51:21.1044660Z     >>>
2024-11-01T17:51:21.1044979Z     >>>     for sharding_dim in range(x.ndim):
2024-11-01T17:51:21.1045526Z     >>>         if sharding_dim != softmax_dim:
2024-11-01T17:51:21.1045984Z     >>>             all_sharded = (
2024-11-01T17:51:21.1046417Z     >>>                 [Shard(sharding_dim)],
2024-11-01T17:51:21.1046987Z     >>>                 [Shard(sharding_dim), None, None],
2024-11-01T17:51:21.1047454Z     >>>             )
2024-11-01T17:51:21.1047864Z     >>>             acceptable_shardings.append(all_sharded)
2024-11-01T17:51:21.1048341Z     >>>
2024-11-01T17:51:21.1048638Z     >>>     return acceptable_shardings
2024-11-01T17:51:21.1048940Z 
2024-11-01T17:51:21.1049231Z .. note:: This API is currently experimental and subject to change
2024-11-01T17:51:21.1049700Z 
2024-11-01T17:51:21.1050132Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.1050654Z 
2024-11-01T17:51:21.1221422Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378.
2024-11-01T17:51:21.1223536Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.1224085Z 
2024-11-01T17:51:21.1224810Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2024-11-01T17:51:21.1226220Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2024-11-01T17:51:21.1227218Z 
2024-11-01T17:51:21.1227440Z Keyword Args:
2024-11-01T17:51:21.1228130Z     input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2024-11-01T17:51:21.1229074Z         The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2024-11-01T17:51:21.1230299Z         DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2024-11-01T17:51:21.1231151Z         as a placeholder. default: None.
2024-11-01T17:51:21.1231784Z     desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2024-11-01T17:51:21.1232835Z         The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2024-11-01T17:51:21.1234265Z         have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2024-11-01T17:51:21.1235203Z     input_kwarg_layouts (Dict[str, Placement]):
2024-11-01T17:51:21.1236132Z         The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2024-11-01T17:51:21.1236979Z         default: None
2024-11-01T17:51:21.1237660Z     desired_input_kwarg_layouts: (Dict[str, Placement]):
2024-11-01T17:51:21.1238617Z         The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2024-11-01T17:51:21.1239560Z         have the desired DTensor layouts. default: None.
2024-11-01T17:51:21.1240088Z     use_local_output (bool, optional):
2024-11-01T17:51:21.1240930Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2024-11-01T17:51:21.1241843Z Returns:
2024-11-01T17:51:21.1242606Z     A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2024-11-01T17:51:21.1243242Z 
2024-11-01T17:51:21.1243371Z Example::
2024-11-01T17:51:21.1243674Z     >>> # xdoctest: +SKIP(failing)
2024-11-01T17:51:21.1244400Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2024-11-01T17:51:21.1245289Z     >>> from torch.distributed.device_mesh import init_device_mesh
2024-11-01T17:51:21.1245827Z     >>> ...
2024-11-01T17:51:21.1246446Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2024-11-01T17:51:21.1247244Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2024-11-01T17:51:21.1247688Z     >>>
2024-11-01T17:51:21.1248340Z     >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2024-11-01T17:51:21.1249212Z     >>> # and then redistributed to Replicated DTensor.
2024-11-01T17:51:21.1249704Z     >>> parallelize_module(
2024-11-01T17:51:21.1250262Z     >>>     block, # this can be a submodule or module
2024-11-01T17:51:21.1250740Z     >>>     tp_mesh,
2024-11-01T17:51:21.1251078Z     >>>     parallelize_plan={
2024-11-01T17:51:21.1251502Z     >>>         "attn": PrepareModuleInput(
2024-11-01T17:51:21.1252017Z     >>>             input_layouts=(Shard(0), None, None, ...),
2024-11-01T17:51:21.1252630Z     >>>             desired_input_layouts=(Replicate(), None, None, ...)
2024-11-01T17:51:21.1253155Z     >>>         ),
2024-11-01T17:51:21.1253449Z     >>>     }
2024-11-01T17:51:21.1253722Z     >>> )
2024-11-01T17:51:21.1253876Z 
2024-11-01T17:51:21.1254319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.1254848Z 
2024-11-01T17:51:21.1255970Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533.
2024-11-01T17:51:21.1257469Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.1258014Z 
2024-11-01T17:51:21.1258712Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2024-11-01T17:51:21.1259930Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2024-11-01T17:51:21.1260566Z 
2024-11-01T17:51:21.1260676Z Keyword Args:
2024-11-01T17:51:21.1261083Z     output_layouts (Union[Placement, Tuple[Placement]]):
2024-11-01T17:51:21.1261992Z         The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2024-11-01T17:51:21.1263241Z         DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2024-11-01T17:51:21.1264172Z         ``None`` need to be specified as a placeholder.
2024-11-01T17:51:21.1264814Z     desired_output_layouts (Union[Placement, Tuple[Placement]]):
2024-11-01T17:51:21.1265838Z         The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2024-11-01T17:51:21.1266744Z         have the desired DTensor layouts.
2024-11-01T17:51:21.1267211Z     use_local_output (bool, optional):
2024-11-01T17:51:21.1268057Z         Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2024-11-01T17:51:21.1268853Z Returns:
2024-11-01T17:51:21.1269695Z     A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2024-11-01T17:51:21.1270287Z 
2024-11-01T17:51:21.1270400Z Example::
2024-11-01T17:51:21.1270702Z     >>> # xdoctest: +SKIP(failing)
2024-11-01T17:51:21.1271435Z     >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2024-11-01T17:51:21.1272330Z     >>> from torch.distributed.device_mesh import init_device_mesh
2024-11-01T17:51:21.1272906Z     >>> ...
2024-11-01T17:51:21.1273543Z     >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2024-11-01T17:51:21.1274476Z     >>> tp_mesh = init_device_mesh("cuda", (8,))
2024-11-01T17:51:21.1274925Z     >>>
2024-11-01T17:51:21.1275677Z     >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2024-11-01T17:51:21.1276630Z     >>> # and then redistributed to Sharded DTensor.
2024-11-01T17:51:21.1277115Z     >>> parallelize_module(
2024-11-01T17:51:21.1277609Z     >>>     block, # this can be a submodule or module
2024-11-01T17:51:21.1278088Z     >>>     tp_mesh,
2024-11-01T17:51:21.1278488Z     >>>     parallelize_plan = PrepareModuleOutput(
2024-11-01T17:51:21.1279012Z     >>>         output_layouts=Replicate(),
2024-11-01T17:51:21.1279489Z     >>>         desired_output_layouts=Shard(0)
2024-11-01T17:51:21.1279929Z     >>>     )
2024-11-01T17:51:21.1280207Z     >>> )
2024-11-01T17:51:21.1280361Z 
2024-11-01T17:51:21.1280822Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.1281337Z 
2024-11-01T17:51:21.1873560Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13.
2024-11-01T17:51:21.1875228Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.1875765Z 
2024-11-01T17:51:21.1876086Z The `MixtureSameFamily` distribution implements a (batch of) mixture
2024-11-01T17:51:21.1876961Z distribution where all component are from different parameterizations of
2024-11-01T17:51:21.1877761Z the same distribution type. It is parameterized by a `Categorical`
2024-11-01T17:51:21.1878504Z "selecting distribution" (over `k` component) and a component
2024-11-01T17:51:21.1879233Z distribution, i.e., a `Distribution` with a rightmost batch shape
2024-11-01T17:51:21.1879919Z (equal to `[k]`) which indexes each (batch of) component.
2024-11-01T17:51:21.1880310Z 
2024-11-01T17:51:21.1880449Z Examples::
2024-11-01T17:51:21.1880614Z 
2024-11-01T17:51:21.1880787Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:21.1881402Z     >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2024-11-01T17:51:21.1882009Z     >>> # weighted normal distributions
2024-11-01T17:51:21.1882483Z     >>> mix = D.Categorical(torch.ones(5,))
2024-11-01T17:51:21.1883018Z     >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2024-11-01T17:51:21.1883557Z     >>> gmm = MixtureSameFamily(mix, comp)
2024-11-01T17:51:21.1883885Z 
2024-11-01T17:51:21.1884183Z     >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2024-11-01T17:51:21.1884811Z     >>> # weighted bivariate normal distributions
2024-11-01T17:51:21.1885324Z     >>> mix = D.Categorical(torch.ones(5,))
2024-11-01T17:51:21.1885797Z     >>> comp = D.Independent(D.Normal(
2024-11-01T17:51:21.1886287Z     ...          torch.randn(5,2), torch.rand(5,2)), 1)
2024-11-01T17:51:21.1886803Z     >>> gmm = MixtureSameFamily(mix, comp)
2024-11-01T17:51:21.1887125Z 
2024-11-01T17:51:21.1887405Z     >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2024-11-01T17:51:21.1888114Z     >>> # consisting of 5 random weighted bivariate normal distributions
2024-11-01T17:51:21.1888739Z     >>> mix = D.Categorical(torch.rand(3,5))
2024-11-01T17:51:21.1889216Z     >>> comp = D.Independent(D.Normal(
2024-11-01T17:51:21.1889713Z     ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2024-11-01T17:51:21.1890379Z     >>> gmm = MixtureSameFamily(mix, comp)
2024-11-01T17:51:21.1890705Z 
2024-11-01T17:51:21.1890819Z Args:
2024-11-01T17:51:21.1891326Z     mixture_distribution: `torch.distributions.Categorical`-like
2024-11-01T17:51:21.1892036Z         instance. Manages the probability of selecting component.
2024-11-01T17:51:21.1892720Z         The number of categories must match the rightmost batch
2024-11-01T17:51:21.1893409Z         dimension of the `component_distribution`. Must have either
2024-11-01T17:51:21.1894050Z         scalar `batch_shape` or `batch_shape` matching
2024-11-01T17:51:21.1894674Z         `component_distribution.batch_shape[:-1]`
2024-11-01T17:51:21.1895413Z     component_distribution: `torch.distributions.Distribution`-like
2024-11-01T17:51:21.1896201Z         instance. Right-most batch dimension indexes component.
2024-11-01T17:51:21.1896630Z 
2024-11-01T17:51:21.1897056Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.1897575Z 
2024-11-01T17:51:21.1997226Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111.
2024-11-01T17:51:21.1998727Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.1999265Z 
2024-11-01T17:51:21.1999544Z Creates a RelaxedBernoulli distribution, parametrized by
2024-11-01T17:51:21.2000232Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2024-11-01T17:51:21.2000987Z (but not both). This is a relaxed version of the `Bernoulli` distribution,
2024-11-01T17:51:21.2002030Z so the values are in (0, 1), and has reparametrizable samples.
2024-11-01T17:51:21.2002487Z 
2024-11-01T17:51:21.2002615Z Example::
2024-11-01T17:51:21.2002833Z 
2024-11-01T17:51:21.2003103Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:21.2003666Z     >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2024-11-01T17:51:21.2004213Z     ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2024-11-01T17:51:21.2004689Z     >>> m.sample()
2024-11-01T17:51:21.2005049Z     tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2024-11-01T17:51:21.2005389Z 
2024-11-01T17:51:21.2005489Z Args:
2024-11-01T17:51:21.2005828Z     temperature (Tensor): relaxation temperature
2024-11-01T17:51:21.2006423Z     probs (Number, Tensor): the probability of sampling `1`
2024-11-01T17:51:21.2007348Z     logits (Number, Tensor): the log-odds of sampling `1`
2024-11-01T17:51:21.2007737Z 
2024-11-01T17:51:21.2008164Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.2008699Z 
2024-11-01T17:51:21.2013563Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99.
2024-11-01T17:51:21.2015131Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.2015696Z 
2024-11-01T17:51:21.2015993Z Creates a RelaxedOneHotCategorical distribution parametrized by
2024-11-01T17:51:21.2016744Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2024-11-01T17:51:21.2017541Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2024-11-01T17:51:21.2018292Z its samples are on simplex, and are reparametrizable.
2024-11-01T17:51:21.2018683Z 
2024-11-01T17:51:21.2018813Z Example::
2024-11-01T17:51:21.2018973Z 
2024-11-01T17:51:21.2019239Z     >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:21.2019830Z     >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2024-11-01T17:51:21.2020424Z     ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2024-11-01T17:51:21.2020911Z     >>> m.sample()
2024-11-01T17:51:21.2021266Z     tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2024-11-01T17:51:21.2021590Z 
2024-11-01T17:51:21.2021701Z Args:
2024-11-01T17:51:21.2022026Z     temperature (Tensor): relaxation temperature
2024-11-01T17:51:21.2022562Z     probs (Tensor): event probabilities
2024-11-01T17:51:21.2023329Z     logits (Tensor): unnormalized log probability for each event
2024-11-01T17:51:21.2023759Z 
2024-11-01T17:51:21.2024204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.2024722Z 
2024-11-01T17:51:21.5675791Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245.
2024-11-01T17:51:21.5677963Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.5679021Z Return a new dict with new, potentially nested, key value pair
2024-11-01T17:51:21.5679521Z 
2024-11-01T17:51:21.5679725Z     >>> purchase = {
2024-11-01T17:51:21.5680265Z     ...     "name": "Alice",
2024-11-01T17:51:21.5680895Z     ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2024-11-01T17:51:21.5681668Z     ...     "credit card": "5555-1234-1234-1234",
2024-11-01T17:51:21.5682162Z     ... }
2024-11-01T17:51:21.5682719Z     >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2024-11-01T17:51:21.5683576Z     {'credit card': '5555-1234-1234-1234',
2024-11-01T17:51:21.5684089Z      'name': 'Alice',
2024-11-01T17:51:21.5684774Z      'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2024-11-01T17:51:21.5685346Z     
2024-11-01T17:51:21.5685924Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.5686442Z 
2024-11-01T17:51:21.5687960Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=261.
2024-11-01T17:51:21.5689526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.5690249Z Update value in a (potentially) nested dictionary
2024-11-01T17:51:21.5690633Z 
2024-11-01T17:51:21.5690741Z     inputs:
2024-11-01T17:51:21.5691113Z     d - dictionary on which to operate
2024-11-01T17:51:21.5691840Z     keys - list or tuple giving the location of the value to be changed in d
2024-11-01T17:51:21.5692564Z     func - function to operate on that value
2024-11-01T17:51:21.5692895Z 
2024-11-01T17:51:21.5693218Z     If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2024-11-01T17:51:21.5694021Z     original dictionary with v replaced by func(v), but does not mutate the
2024-11-01T17:51:21.5694654Z     original dictionary.
2024-11-01T17:51:21.5694895Z 
2024-11-01T17:51:21.5695234Z     If k0 is not a key in d, update_in creates nested dictionaries to the depth
2024-11-01T17:51:21.5696069Z     specified by the keys, with the innermost value set to func(default).
2024-11-01T17:51:21.5696554Z 
2024-11-01T17:51:21.5696695Z     >>> inc = lambda x: x + 1
2024-11-01T17:51:21.5697084Z     >>> update_in({"a": 0}, ["a"], inc)
2024-11-01T17:51:21.5697512Z     {'a': 1}
2024-11-01T17:51:21.5697693Z 
2024-11-01T17:51:21.5697817Z     >>> transaction = {
2024-11-01T17:51:21.5698166Z     ...     "name": "Alice",
2024-11-01T17:51:21.5698684Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2024-11-01T17:51:21.5699363Z     ...     "credit card": "5555-1234-1234-1234",
2024-11-01T17:51:21.5699784Z     ... }
2024-11-01T17:51:21.5700256Z     >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2024-11-01T17:51:21.5700938Z     {'credit card': '5555-1234-1234-1234',
2024-11-01T17:51:21.5701402Z      'name': 'Alice',
2024-11-01T17:51:21.5701913Z      'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2024-11-01T17:51:21.5702314Z 
2024-11-01T17:51:21.5702502Z     >>> # updating a value when k0 is not in d
2024-11-01T17:51:21.5703016Z     >>> update_in({}, [1, 2, 3], str, default="bar")
2024-11-01T17:51:21.5703530Z     {1: {2: {3: 'bar'}}}
2024-11-01T17:51:21.5703917Z     >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2024-11-01T17:51:21.5704421Z     {1: 'foo', 2: {3: {4: 1}}}
2024-11-01T17:51:21.5704772Z     
2024-11-01T17:51:21.5705459Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.5705992Z 
2024-11-01T17:51:21.5707367Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=320.
2024-11-01T17:51:21.5708900Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.5709657Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2024-11-01T17:51:21.5710070Z 
2024-11-01T17:51:21.5710357Z     If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2024-11-01T17:51:21.5711113Z     ``no_default`` is specified, then it raises KeyError or IndexError.
2024-11-01T17:51:21.5711575Z 
2024-11-01T17:51:21.5711885Z     ``get_in`` is a generalization of ``operator.getitem`` for nested data
2024-11-01T17:51:21.5712546Z     structures such as dictionaries and lists.
2024-11-01T17:51:21.5712887Z 
2024-11-01T17:51:21.5713024Z     >>> transaction = {
2024-11-01T17:51:21.5713363Z     ...     "name": "Alice",
2024-11-01T17:51:21.5713972Z     ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2024-11-01T17:51:21.5714665Z     ...     "credit card": "5555-1234-1234-1234",
2024-11-01T17:51:21.5715110Z     ... }
2024-11-01T17:51:21.5715466Z     >>> get_in(["purchase", "items", 0], transaction)
2024-11-01T17:51:21.5715964Z     'Apple'
2024-11-01T17:51:21.5716259Z     >>> get_in(["name"], transaction)
2024-11-01T17:51:21.5716686Z     'Alice'
2024-11-01T17:51:21.5717026Z     >>> get_in(["purchase", "total"], transaction)
2024-11-01T17:51:21.5717735Z     >>> get_in(["purchase", "items", "apple"], transaction)
2024-11-01T17:51:21.5718317Z     >>> get_in(["purchase", "items", 10], transaction)
2024-11-01T17:51:21.5718861Z     >>> get_in(["purchase", "total"], transaction, 0)
2024-11-01T17:51:21.5719330Z     0
2024-11-01T17:51:21.5719628Z     >>> get_in(["y"], {}, no_default=True)
2024-11-01T17:51:21.5720093Z     Traceback (most recent call last):
2024-11-01T17:51:21.5720511Z         ...
2024-11-01T17:51:21.5720811Z     KeyError: 'y'
2024-11-01T17:51:21.5721014Z 
2024-11-01T17:51:21.5721120Z     See Also:
2024-11-01T17:51:21.5721410Z         itertoolz.get
2024-11-01T17:51:21.5721745Z         operator.getitem
2024-11-01T17:51:21.5722077Z     
2024-11-01T17:51:21.5722644Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.5723173Z 
2024-11-01T17:51:21.5724253Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=373.
2024-11-01T17:51:21.5725781Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:21.5726455Z Group a collection by a key function
2024-11-01T17:51:21.5726768Z 
2024-11-01T17:51:21.5727012Z     >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2024-11-01T17:51:21.5727600Z     >>> groupby(len, names)  # doctest: +SKIP
2024-11-01T17:51:21.5728267Z     {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2024-11-01T17:51:21.5728685Z 
2024-11-01T17:51:21.5728827Z     >>> iseven = lambda x: x % 2 == 0
2024-11-01T17:51:21.5729374Z     >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2024-11-01T17:51:21.5729948Z     {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2024-11-01T17:51:21.5730278Z 
2024-11-01T17:51:21.5730526Z     Non-callable keys imply grouping on a member.
2024-11-01T17:51:21.5730882Z 
2024-11-01T17:51:21.5731001Z     >>> groupby(
2024-11-01T17:51:21.5731300Z     ...     "gender",
2024-11-01T17:51:21.5731602Z     ...     [
2024-11-01T17:51:21.5731950Z     ...         {"name": "Alice", "gender": "F"},
2024-11-01T17:51:21.5732444Z     ...         {"name": "Bob", "gender": "M"},
2024-11-01T17:51:21.5732938Z     ...         {"name": "Charlie", "gender": "M"},
2024-11-01T17:51:21.5733384Z     ...     ],
2024-11-01T17:51:21.5733672Z     ... )  # doctest:+SKIP
2024-11-01T17:51:21.5734124Z     {'F': [{'gender': 'F', 'name': 'Alice'}],
2024-11-01T17:51:21.5734785Z      'M': [{'gender': 'M', 'name': 'Bob'},
2024-11-01T17:51:21.5735320Z            {'gender': 'M', 'name': 'Charlie'}]}
2024-11-01T17:51:21.5735648Z 
2024-11-01T17:51:21.5735862Z     Not to be confused with ``itertools.groupby``
2024-11-01T17:51:21.5736218Z 
2024-11-01T17:51:21.5736339Z     See Also:
2024-11-01T17:51:21.5736613Z         countby
2024-11-01T17:51:21.5736896Z     
2024-11-01T17:51:21.5737467Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:21.5737979Z 
2024-11-01T17:51:22.0974773Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601.
2024-11-01T17:51:22.0977558Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.0979095Z Applies Batch Normalization over a N-Dimensional input.
2024-11-01T17:51:22.0979769Z 
2024-11-01T17:51:22.0980841Z     The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2024-11-01T17:51:22.0982646Z     `Batch Normalization: Accelerating Deep Network Training by Reducing
2024-11-01T17:51:22.0983942Z     Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2024-11-01T17:51:22.0984762Z 
2024-11-01T17:51:22.0984991Z     .. math::
2024-11-01T17:51:22.0985261Z 
2024-11-01T17:51:22.0986108Z         y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2024-11-01T17:51:22.0987101Z 
2024-11-01T17:51:22.0988337Z     The mean and standard-deviation are calculated per-dimension over all
2024-11-01T17:51:22.0990048Z     mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2024-11-01T17:51:22.0991613Z     are learnable parameter vectors of size `C` (where `C` is the input size).
2024-11-01T17:51:22.0993095Z     By default, the elements of :math:`\gamma` are sampled from
2024-11-01T17:51:22.0994513Z     :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2024-11-01T17:51:22.0996340Z     The standard-deviation is calculated via the biased estimator, equivalent to
2024-11-01T17:51:22.0997628Z     `torch.var(input, unbiased=False)`.
2024-11-01T17:51:22.0998203Z 
2024-11-01T17:51:22.0998784Z     Also by default, during training this layer keeps running estimates of its
2024-11-01T17:51:22.1000186Z     computed mean and variance, which are then used for normalization during
2024-11-01T17:51:22.1001697Z     evaluation. The running estimates are kept with a default :attr:`momentum`
2024-11-01T17:51:22.1002736Z     of 0.1.
2024-11-01T17:51:22.1003013Z 
2024-11-01T17:51:22.1003638Z     If :attr:`track_running_stats` is set to ``False``, this layer then does not
2024-11-01T17:51:22.1005103Z     keep running estimates, and batch statistics are instead used during
2024-11-01T17:51:22.1006209Z     evaluation time as well.
2024-11-01T17:51:22.1006971Z 
2024-11-01T17:51:22.1007201Z     .. note::
2024-11-01T17:51:22.1008027Z         This :attr:`momentum` argument is different from one used in optimizer
2024-11-01T17:51:22.1009552Z         classes and the conventional notion of momentum. Mathematically, the
2024-11-01T17:51:22.1010802Z         update rule for running statistics here is
2024-11-01T17:51:22.1012476Z         :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2024-11-01T17:51:22.1014151Z         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2024-11-01T17:51:22.1015313Z         new observed value.
2024-11-01T17:51:22.1015786Z 
2024-11-01T17:51:22.1016617Z     Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2024-11-01T17:51:22.1018690Z     statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2024-11-01T17:51:22.1020324Z     Normalization or Spatio-temporal Batch Normalization.
2024-11-01T17:51:22.1021094Z 
2024-11-01T17:51:22.1021470Z     Currently :class:`SyncBatchNorm` only supports
2024-11-01T17:51:22.1023131Z     :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2024-11-01T17:51:22.1024736Z     :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2024-11-01T17:51:22.1026182Z     :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2024-11-01T17:51:22.1027266Z     Network with DDP.
2024-11-01T17:51:22.1027656Z 
2024-11-01T17:51:22.1027852Z     Args:
2024-11-01T17:51:22.1028558Z         num_features: :math:`C` from an expected input of size
2024-11-01T17:51:22.1029522Z             :math:`(N, C, +)`
2024-11-01T17:51:22.1030550Z         eps: a value added to the denominator for numerical stability.
2024-11-01T17:51:22.1031804Z             Default: ``1e-5``
2024-11-01T17:51:22.1032757Z         momentum: the value used for the running_mean and running_var
2024-11-01T17:51:22.1034260Z             computation. Can be set to ``None`` for cumulative moving average
2024-11-01T17:51:22.1035476Z             (i.e. simple average). Default: 0.1
2024-11-01T17:51:22.1036619Z         affine: a boolean value that when set to ``True``, this module has
2024-11-01T17:51:22.1037889Z             learnable affine parameters. Default: ``True``
2024-11-01T17:51:22.1039117Z         track_running_stats: a boolean value that when set to ``True``, this
2024-11-01T17:51:22.1040590Z             module tracks the running mean and variance, and when set to ``False``,
2024-11-01T17:51:22.1042087Z             this module does not track such statistics, and initializes statistics
2024-11-01T17:51:22.1043142Z             buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2024-11-01T17:51:22.1044759Z             When these buffers are ``None``, this module always uses batch statistics.
2024-11-01T17:51:22.1046139Z             in both training and eval modes. Default: ``True``
2024-11-01T17:51:22.1047505Z         process_group: synchronization of stats happen within each process group
2024-11-01T17:51:22.1049078Z             individually. Default behavior is synchronization across the whole
2024-11-01T17:51:22.1050216Z             world
2024-11-01T17:51:22.1050559Z 
2024-11-01T17:51:22.1050764Z     Shape:
2024-11-01T17:51:22.1051470Z         - Input: :math:`(N, C, +)`
2024-11-01T17:51:22.1052469Z         - Output: :math:`(N, C, +)` (same shape as input)
2024-11-01T17:51:22.1053172Z 
2024-11-01T17:51:22.1053399Z     .. note::
2024-11-01T17:51:22.1054311Z         Synchronization of batchnorm statistics occurs only while training, i.e.
2024-11-01T17:51:22.1055644Z         synchronization is disabled when ``model.eval()`` is set or if
2024-11-01T17:51:22.1056653Z         ``self.training`` is otherwise ``False``.
2024-11-01T17:51:22.1057205Z 
2024-11-01T17:51:22.1057400Z     Examples::
2024-11-01T17:51:22.1057716Z 
2024-11-01T17:51:22.1057928Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:22.1058594Z         >>> # With Learnable Parameters
2024-11-01T17:51:22.1059366Z         >>> m = nn.SyncBatchNorm(100)
2024-11-01T17:51:22.1060159Z         >>> # creating process group (optional)
2024-11-01T17:51:22.1061093Z         >>> # ranks is a list of int identifying rank ids.
2024-11-01T17:51:22.1061950Z         >>> ranks = list(range(8))
2024-11-01T17:51:22.1062675Z         >>> r1, r2 = ranks[:4], ranks[4:]
2024-11-01T17:51:22.1063550Z         >>> # Note: every rank calls into new_group for every
2024-11-01T17:51:22.1064578Z         >>> # process group created, even if that rank is not
2024-11-01T17:51:22.1065463Z         >>> # part of the group.
2024-11-01T17:51:22.1066518Z         >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2024-11-01T17:51:22.1067937Z         >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2024-11-01T17:51:22.1069005Z         >>> # Without Learnable Parameters
2024-11-01T17:51:22.1070089Z         >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2024-11-01T17:51:22.1071248Z         >>> input = torch.randn(20, 100, 35, 45, 10)
2024-11-01T17:51:22.1072060Z         >>> output = m(input)
2024-11-01T17:51:22.1072784Z 
2024-11-01T17:51:22.1073052Z         >>> # network is nn.BatchNorm layer
2024-11-01T17:51:22.1074450Z         >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2024-11-01T17:51:22.1075815Z         >>> # only single gpu per process is currently supported
2024-11-01T17:51:22.1077059Z         >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2024-11-01T17:51:22.1078155Z         >>>                         sync_bn_network,
2024-11-01T17:51:22.1079048Z         >>>                         device_ids=[args.local_rank],
2024-11-01T17:51:22.1080007Z         >>>                         output_device=args.local_rank)
2024-11-01T17:51:22.1080837Z     
2024-11-01T17:51:22.1081949Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.1082767Z 
2024-11-01T17:51:22.1085016Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824.
2024-11-01T17:51:22.1087694Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.1089309Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2024-11-01T17:51:22.1090321Z 
2024-11-01T17:51:22.1090488Z         Args:
2024-11-01T17:51:22.1091329Z             module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2024-11-01T17:51:22.1092785Z             process_group (optional): process group to scope synchronization,
2024-11-01T17:51:22.1094110Z                 default is the whole world
2024-11-01T17:51:22.1094651Z 
2024-11-01T17:51:22.1094821Z         Returns:
2024-11-01T17:51:22.1095712Z             The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2024-11-01T17:51:22.1097261Z             layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2024-11-01T17:51:22.1098584Z             a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2024-11-01T17:51:22.1099630Z             instead.
2024-11-01T17:51:22.1099914Z 
2024-11-01T17:51:22.1100112Z         Example::
2024-11-01T17:51:22.1100400Z 
2024-11-01T17:51:22.1100698Z             >>> # Network with nn.BatchNorm layer
2024-11-01T17:51:22.1101608Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:22.1102468Z             >>> module = torch.nn.Sequential(
2024-11-01T17:51:22.1103309Z             >>>            torch.nn.Linear(20, 100),
2024-11-01T17:51:22.1104256Z             >>>            torch.nn.BatchNorm1d(100),
2024-11-01T17:51:22.1105180Z             >>>          ).cuda()
2024-11-01T17:51:22.1105971Z             >>> # creating process group (optional)
2024-11-01T17:51:22.1107343Z             >>> # ranks is a list of int identifying rank ids.
2024-11-01T17:51:22.1108281Z             >>> ranks = list(range(8))
2024-11-01T17:51:22.1109086Z             >>> r1, r2 = ranks[:4], ranks[4:]
2024-11-01T17:51:22.1110078Z             >>> # Note: every rank calls into new_group for every
2024-11-01T17:51:22.1111121Z             >>> # process group created, even if that rank is not
2024-11-01T17:51:22.1112059Z             >>> # part of the group.
2024-11-01T17:51:22.1112896Z             >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:22.1114280Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2024-11-01T17:51:22.1115755Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2024-11-01T17:51:22.1117371Z             >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2024-11-01T17:51:22.1118483Z 
2024-11-01T17:51:22.1118669Z         
2024-11-01T17:51:22.1119940Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.1120821Z 
2024-11-01T17:51:22.1338776Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60.
2024-11-01T17:51:22.1341772Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.1342759Z 
2024-11-01T17:51:22.1343578Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2024-11-01T17:51:22.1344649Z 
2024-11-01T17:51:22.1345327Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2024-11-01T17:51:22.1347293Z   be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2024-11-01T17:51:22.1348203Z 
2024-11-01T17:51:22.1349117Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2024-11-01T17:51:22.1351110Z   a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2024-11-01T17:51:22.1352649Z   (tuple of `(name, size)` tuples) for `NamedTensor` input.
2024-11-01T17:51:22.1353457Z 
2024-11-01T17:51:22.1353656Z Shape:
2024-11-01T17:51:22.1369300Z     - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2024-11-01T17:51:22.1371335Z       dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2024-11-01T17:51:22.1373238Z     - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2024-11-01T17:51:22.1374497Z       :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2024-11-01T17:51:22.1375155Z 
2024-11-01T17:51:22.1375342Z Args:
2024-11-01T17:51:22.1376025Z     dim (Union[int, str]): Dimension to be unflattened
2024-11-01T17:51:22.1377877Z     unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2024-11-01T17:51:22.1379098Z 
2024-11-01T17:51:22.1379318Z Examples:
2024-11-01T17:51:22.1379886Z     >>> input = torch.randn(2, 50)
2024-11-01T17:51:22.1380653Z     >>> # With tuple of ints
2024-11-01T17:51:22.1381361Z     >>> m = nn.Sequential(
2024-11-01T17:51:22.1382040Z     >>>     nn.Linear(50, 50),
2024-11-01T17:51:22.1382757Z     >>>     nn.Unflatten(1, (2, 5, 5))
2024-11-01T17:51:22.1383520Z     >>> )
2024-11-01T17:51:22.1383946Z     >>> output = m(input)
2024-11-01T17:51:22.1384542Z     >>> output.size()
2024-11-01T17:51:22.1385109Z     torch.Size([2, 2, 5, 5])
2024-11-01T17:51:22.1385814Z     >>> # With torch.Size
2024-11-01T17:51:22.1386452Z     >>> m = nn.Sequential(
2024-11-01T17:51:22.1387107Z     >>>     nn.Linear(50, 50),
2024-11-01T17:51:22.1387868Z     >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2024-11-01T17:51:22.1388713Z     >>> )
2024-11-01T17:51:22.1389235Z     >>> output = m(input)
2024-11-01T17:51:22.1389870Z     >>> output.size()
2024-11-01T17:51:22.1390463Z     torch.Size([2, 2, 5, 5])
2024-11-01T17:51:22.1391183Z     >>> # With namedshape (tuple of tuples)
2024-11-01T17:51:22.1392385Z     >>> input = torch.randn(2, 50, names=('N', 'features'))
2024-11-01T17:51:22.1394016Z     >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
2024-11-01T17:51:22.1395138Z     >>> output = unflatten(input)
2024-11-01T17:51:22.1395863Z     >>> output.size()
2024-11-01T17:51:22.1396483Z     torch.Size([2, 2, 5, 5])
2024-11-01T17:51:22.1396976Z 
2024-11-01T17:51:22.1397799Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.1398622Z 
2024-11-01T17:51:22.1764555Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696.
2024-11-01T17:51:22.1767481Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.1769044Z Creates a criterion that measures the triplet loss given input
2024-11-01T17:51:22.1770452Z     tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2024-11-01T17:51:22.1772022Z     positive, and negative examples, respectively), and a nonnegative,
2024-11-01T17:51:22.1773681Z     real-valued function ("distance function") used to compute the relationship
2024-11-01T17:51:22.1775246Z     between the anchor and positive example ("positive distance") and the
2024-11-01T17:51:22.1777134Z     anchor and negative example ("negative distance").
2024-11-01T17:51:22.1777889Z 
2024-11-01T17:51:22.1778666Z     The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2024-11-01T17:51:22.1779812Z     can be described as:
2024-11-01T17:51:22.1780242Z 
2024-11-01T17:51:22.1780483Z     .. math::
2024-11-01T17:51:22.1781194Z         \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2024-11-01T17:51:22.1782505Z         l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2024-11-01T17:51:22.1783281Z 
2024-11-01T17:51:22.1784191Z     where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2024-11-01T17:51:22.1786044Z     quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2024-11-01T17:51:22.1787817Z     and :math:`margin` is a nonnegative margin representing the minimum difference
2024-11-01T17:51:22.1789518Z     between the positive and negative distances that is required for the loss to
2024-11-01T17:51:22.1791217Z     be 0.  The input tensors have :math:`N` elements each and can be of any shape
2024-11-01T17:51:22.1792512Z     that the distance function can handle.
2024-11-01T17:51:22.1793153Z 
2024-11-01T17:51:22.1793558Z     If :attr:`reduction` is not ``'none'``
2024-11-01T17:51:22.1794664Z     (default ``'mean'``), then:
2024-11-01T17:51:22.1795168Z 
2024-11-01T17:51:22.1795343Z     .. math::
2024-11-01T17:51:22.1795801Z         \ell(x, y) =
2024-11-01T17:51:22.1796322Z         \begin{cases}
2024-11-01T17:51:22.1797348Z             \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2024-11-01T17:51:22.1799220Z             \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2024-11-01T17:51:22.1800245Z         \end{cases}
2024-11-01T17:51:22.1800579Z 
2024-11-01T17:51:22.1801142Z     See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2024-11-01T17:51:22.1802692Z     loss for input tensors using the :math:`l_p` distance as the distance function.
2024-11-01T17:51:22.1803583Z 
2024-11-01T17:51:22.1803794Z     Args:
2024-11-01T17:51:22.1804930Z         distance_function (Callable, optional): A nonnegative, real-valued function that
2024-11-01T17:51:22.1806523Z             quantifies the closeness of two tensors. If not specified,
2024-11-01T17:51:22.1808196Z             `nn.PairwiseDistance` will be used.  Default: ``None``
2024-11-01T17:51:22.1809721Z         margin (float, optional): A nonnegative margin representing the minimum difference
2024-11-01T17:51:22.1811574Z             between the positive and negative distances required for the loss to be 0. Larger
2024-11-01T17:51:22.1813261Z             margins penalize cases where the negative examples are not distant enough from the
2024-11-01T17:51:22.1814829Z             anchors, relative to the positives. Default: :math:`1`.
2024-11-01T17:51:22.1816298Z         swap (bool, optional): Whether to use the distance swap described in the paper
2024-11-01T17:51:22.1818038Z             `Learning shallow convolutional feature descriptors with triplet losses` by
2024-11-01T17:51:22.1819771Z             V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2024-11-01T17:51:22.1821467Z             negative example than the anchor is, swaps the positive example and the anchor in
2024-11-01T17:51:22.1822805Z             the loss computation. Default: ``False``.
2024-11-01T17:51:22.1824015Z         reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2024-11-01T17:51:22.1825718Z             ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2024-11-01T17:51:22.1827112Z             ``'mean'``: the sum of the output will be divided by the number of
2024-11-01T17:51:22.1828748Z             elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2024-11-01T17:51:22.1829756Z 
2024-11-01T17:51:22.1829764Z 
2024-11-01T17:51:22.1829960Z     Shape:
2024-11-01T17:51:22.1831098Z         - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2024-11-01T17:51:22.1832638Z           as supported by the distance function.
2024-11-01T17:51:22.1834242Z         - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2024-11-01T17:51:22.1835379Z           otherwise.
2024-11-01T17:51:22.1835776Z 
2024-11-01T17:51:22.1836011Z     Examples::
2024-11-01T17:51:22.1836350Z 
2024-11-01T17:51:22.1836593Z     >>> # Initialize embeddings
2024-11-01T17:51:22.1837328Z     >>> embedding = nn.Embedding(1000, 128)
2024-11-01T17:51:22.1838170Z     >>> anchor_ids = torch.randint(0, 1000, (1,))
2024-11-01T17:51:22.1839143Z     >>> positive_ids = torch.randint(0, 1000, (1,))
2024-11-01T17:51:22.1840108Z     >>> negative_ids = torch.randint(0, 1000, (1,))
2024-11-01T17:51:22.1841037Z     >>> anchor = embedding(anchor_ids)
2024-11-01T17:51:22.1841906Z     >>> positive = embedding(positive_ids)
2024-11-01T17:51:22.1842797Z     >>> negative = embedding(negative_ids)
2024-11-01T17:51:22.1843463Z     >>>
2024-11-01T17:51:22.1844100Z     >>> # Built-in Distance Function
2024-11-01T17:51:22.1844868Z     >>> triplet_loss = \
2024-11-01T17:51:22.1846009Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2024-11-01T17:51:22.1847419Z     >>> output = triplet_loss(anchor, positive, negative)
2024-11-01T17:51:22.1848361Z     >>> output.backward()
2024-11-01T17:51:22.1848988Z     >>>
2024-11-01T17:51:22.1849505Z     >>> # Custom Distance Function
2024-11-01T17:51:22.1850282Z     >>> def l_infinity(x1, x2):
2024-11-01T17:51:22.1851721Z     >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2024-11-01T17:51:22.1852594Z     >>>
2024-11-01T17:51:22.1853402Z     >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2024-11-01T17:51:22.1854434Z     >>> triplet_loss = (
2024-11-01T17:51:22.1855484Z     >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2024-11-01T17:51:22.1856653Z     >>> output = triplet_loss(anchor, positive, negative)
2024-11-01T17:51:22.1857628Z     >>> output.backward()
2024-11-01T17:51:22.1858254Z     >>>
2024-11-01T17:51:22.1858795Z     >>> # Custom Distance Function (Lambda)
2024-11-01T17:51:22.1859623Z     >>> triplet_loss = (
2024-11-01T17:51:22.1860383Z     >>>     nn.TripletMarginWithDistanceLoss(
2024-11-01T17:51:22.1861777Z     >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2024-11-01T17:51:22.1863037Z     >>> output = triplet_loss(anchor, positive, negative)
2024-11-01T17:51:22.1863954Z     >>> output.backward()
2024-11-01T17:51:22.1864386Z 
2024-11-01T17:51:22.1864595Z     Reference:
2024-11-01T17:51:22.1865740Z         V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2024-11-01T17:51:22.1867308Z         http://www.bmva.org/bmvc/2016/papers/paper119/index.html
2024-11-01T17:51:22.1868269Z     
2024-11-01T17:51:22.1869376Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2024-11-01T17:51:22.1870365Z 
2024-11-01T17:51:22.2486571Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395.
2024-11-01T17:51:22.2489263Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.2490643Z Computes a partial inverse of :class:`MaxPool2d`.
2024-11-01T17:51:22.2491303Z 
2024-11-01T17:51:22.2492157Z     :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2024-11-01T17:51:22.2493255Z 
2024-11-01T17:51:22.2493879Z     :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2024-11-01T17:51:22.2495486Z     including the indices of the maximal values and computes a partial inverse
2024-11-01T17:51:22.2497037Z     in which all non-maximal values are set to zero.
2024-11-01T17:51:22.2497737Z 
2024-11-01T17:51:22.2497954Z     Note:
2024-11-01T17:51:22.2499078Z         This operation may behave nondeterministically when the input indices has repeat values.
2024-11-01T17:51:22.2501588Z         See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2024-11-01T17:51:22.2502956Z 
2024-11-01T17:51:22.2503548Z     .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2024-11-01T17:51:22.2504943Z               sizes. Hence, the inversion process can get ambiguous.
2024-11-01T17:51:22.2506281Z               To accommodate this, you can provide the needed output size
2024-11-01T17:51:22.2508094Z               as an additional argument :attr:`output_size` in the forward call.
2024-11-01T17:51:22.2509385Z               See the Inputs and Example below.
2024-11-01T17:51:22.2510013Z 
2024-11-01T17:51:22.2510226Z     Args:
2024-11-01T17:51:22.2511007Z         kernel_size (int or tuple): Size of the max pooling window.
2024-11-01T17:51:22.2512272Z         stride (int or tuple): Stride of the max pooling window.
2024-11-01T17:51:22.2513352Z             It is set to :attr:`kernel_size` by default.
2024-11-01T17:51:22.2514664Z         padding (int or tuple): Padding that was added to the input
2024-11-01T17:51:22.2515489Z 
2024-11-01T17:51:22.2515704Z     Inputs:
2024-11-01T17:51:22.2516562Z         - `input`: the input Tensor to invert
2024-11-01T17:51:22.2517820Z         - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2024-11-01T17:51:22.2519239Z         - `output_size` (optional): the targeted output size
2024-11-01T17:51:22.2519993Z 
2024-11-01T17:51:22.2520203Z     Shape:
2024-11-01T17:51:22.2521212Z         - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2024-11-01T17:51:22.2523348Z         - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2024-11-01T17:51:22.2524301Z 
2024-11-01T17:51:22.2524554Z           .. math::
2024-11-01T17:51:22.2525974Z             H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2024-11-01T17:51:22.2527029Z 
2024-11-01T17:51:22.2527252Z           .. math::
2024-11-01T17:51:22.2528667Z             W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2024-11-01T17:51:22.2529828Z 
2024-11-01T17:51:22.2530284Z           or as given by :attr:`output_size` in the call operator
2024-11-01T17:51:22.2531023Z 
2024-11-01T17:51:22.2531231Z     Example::
2024-11-01T17:51:22.2531548Z 
2024-11-01T17:51:22.2531974Z         >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2024-11-01T17:51:22.2533064Z         >>> unpool = nn.MaxUnpool2d(2, stride=2)
2024-11-01T17:51:22.2534063Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2024-11-01T17:51:22.2535065Z                                     [ 5.,  6.,  7.,  8.],
2024-11-01T17:51:22.2536017Z                                     [ 9., 10., 11., 12.],
2024-11-01T17:51:22.2536950Z                                     [13., 14., 15., 16.]]]])
2024-11-01T17:51:22.2537818Z         >>> output, indices = pool(input)
2024-11-01T17:51:22.2538685Z         >>> unpool(output, indices)
2024-11-01T17:51:22.2539432Z         tensor([[[[  0.,   0.,   0.,   0.],
2024-11-01T17:51:22.2540203Z                   [  0.,   6.,   0.,   8.],
2024-11-01T17:51:22.2540958Z                   [  0.,   0.,   0.,   0.],
2024-11-01T17:51:22.2541798Z                   [  0.,  14.,   0.,  16.]]]])
2024-11-01T17:51:22.2542960Z         >>> # Now using output_size to resolve an ambiguous size for the inverse
2024-11-01T17:51:22.2544235Z         >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2024-11-01T17:51:22.2545206Z                                     [ 6.,  7.,  8.,  9., 10.],
2024-11-01T17:51:22.2546185Z                                     [11., 12., 13., 14., 15.],
2024-11-01T17:51:22.2547128Z                                     [16., 17., 18., 19., 20.]]]])
2024-11-01T17:51:22.2548077Z         >>> output, indices = pool(input)
2024-11-01T17:51:22.2549150Z         >>> # This call will not work without specifying output_size
2024-11-01T17:51:22.2550347Z         >>> unpool(output, indices, output_size=input.size())
2024-11-01T17:51:22.2551780Z         tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2024-11-01T17:51:22.2552582Z                   [ 0.,  7.,  0.,  9.,  0.],
2024-11-01T17:51:22.2553429Z                   [ 0.,  0.,  0.,  0.,  0.],
2024-11-01T17:51:22.2554424Z                   [ 0., 17.,  0., 19.,  0.]]]])
2024-11-01T17:51:22.2555019Z 
2024-11-01T17:51:22.2555028Z 
2024-11-01T17:51:22.2555231Z     
2024-11-01T17:51:22.2556423Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.2557412Z 
2024-11-01T17:51:22.2840022Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270.
2024-11-01T17:51:22.2842804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.2844833Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2024-11-01T17:51:22.2846068Z 
2024-11-01T17:51:22.2846975Z     For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2024-11-01T17:51:22.2848473Z     and with 2D inputs, this class
2024-11-01T17:51:22.2849031Z 
2024-11-01T17:51:22.2849839Z         * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2024-11-01T17:51:22.2851858Z         * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2024-11-01T17:51:22.2853872Z         * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2024-11-01T17:51:22.2855510Z 
2024-11-01T17:51:22.2856472Z     However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2024-11-01T17:51:22.2857953Z     operations.
2024-11-01T17:51:22.2858299Z 
2024-11-01T17:51:22.2859158Z     EmbeddingBag also supports per-sample weights as an argument to the forward
2024-11-01T17:51:22.2860750Z     pass. This scales the output of the Embedding before performing a weighted
2024-11-01T17:51:22.2862344Z     reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2024-11-01T17:51:22.2863912Z     only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2024-11-01T17:51:22.2865119Z     :attr:`per_sample_weights`.
2024-11-01T17:51:22.2865613Z 
2024-11-01T17:51:22.2865826Z     Args:
2024-11-01T17:51:22.2866580Z         num_embeddings (int): size of the dictionary of embeddings
2024-11-01T17:51:22.2867794Z         embedding_dim (int): the size of each embedding vector
2024-11-01T17:51:22.2869397Z         max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2024-11-01T17:51:22.2870842Z                                     is renormalized to have norm :attr:`max_norm`.
2024-11-01T17:51:22.2872895Z         norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2024-11-01T17:51:22.2875233Z         scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2024-11-01T17:51:22.2877071Z                                                 the words in the mini-batch. Default ``False``.
2024-11-01T17:51:22.2878383Z                                                 Note: this option is not supported when ``mode="max"``.
2024-11-01T17:51:22.2879793Z         mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2024-11-01T17:51:22.2881192Z                                  ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2024-11-01T17:51:22.2882734Z                                  into consideration. ``"mean"`` computes the average of the values
2024-11-01T17:51:22.2884150Z                                  in the bag, ``"max"`` computes the max value over each bag.
2024-11-01T17:51:22.2885306Z                                  Default: ``"mean"``
2024-11-01T17:51:22.2887310Z         sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2024-11-01T17:51:22.2889222Z                                  Notes for more details regarding sparse gradients. Note: this option is not
2024-11-01T17:51:22.2890568Z                                  supported when ``mode="max"``.
2024-11-01T17:51:22.2892268Z         include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2024-11-01T17:51:22.2894245Z                                       is equivalent to the size of `indices`. This matches the CSR format.
2024-11-01T17:51:22.2896136Z         padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2024-11-01T17:51:22.2898099Z                                      gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2024-11-01T17:51:22.2899597Z                                      during training, i.e. it remains as a fixed "pad". For a newly constructed
2024-11-01T17:51:22.2900856Z                                      EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2024-11-01T17:51:22.2902533Z                                      zeros, but can be updated to another value to be used as the padding vector.
2024-11-01T17:51:22.2904207Z                                      Note that the embedding vector at :attr:`padding_idx` is excluded from the
2024-11-01T17:51:22.2905441Z                                      reduction.
2024-11-01T17:51:22.2905993Z 
2024-11-01T17:51:22.2906209Z     Attributes:
2024-11-01T17:51:22.2908015Z         weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2024-11-01T17:51:22.2909427Z                          initialized from :math:`\mathcal{N}(0, 1)`.
2024-11-01T17:51:22.2910098Z 
2024-11-01T17:51:22.2910319Z     Examples::
2024-11-01T17:51:22.2910618Z 
2024-11-01T17:51:22.2911024Z         >>> # an EmbeddingBag module containing 10 tensors of size 3
2024-11-01T17:51:22.2912393Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2024-11-01T17:51:22.2913371Z         >>> # a batch of 2 samples of 4 indices each
2024-11-01T17:51:22.2914591Z         >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2024-11-01T17:51:22.2915724Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2024-11-01T17:51:22.2916843Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:22.2917729Z         >>> embedding_sum(input, offsets)
2024-11-01T17:51:22.2918638Z         tensor([[-0.8861, -5.4350, -0.0523],
2024-11-01T17:51:22.2919550Z                 [ 1.1306, -2.5798, -1.0044]])
2024-11-01T17:51:22.2920085Z 
2024-11-01T17:51:22.2920360Z         >>> # Example with padding_idx
2024-11-01T17:51:22.2921540Z         >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2024-11-01T17:51:22.2922832Z         >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2024-11-01T17:51:22.2924038Z         >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2024-11-01T17:51:22.2924977Z         >>> embedding_sum(input, offsets)
2024-11-01T17:51:22.2925785Z         tensor([[ 0.0000,  0.0000,  0.0000],
2024-11-01T17:51:22.2926686Z                 [-0.7082,  3.2145, -2.6251]])
2024-11-01T17:51:22.2927210Z 
2024-11-01T17:51:22.2927659Z         >>> # An EmbeddingBag can be loaded from an Embedding like so
2024-11-01T17:51:22.2928728Z         >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2024-11-01T17:51:22.2929697Z         >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2024-11-01T17:51:22.2930652Z                 embedding.weight,
2024-11-01T17:51:22.2931543Z                 padding_idx=embedding.padding_idx,
2024-11-01T17:51:22.2932354Z                 mode='sum')
2024-11-01T17:51:22.2932871Z     
2024-11-01T17:51:22.2933804Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.2934692Z 
2024-11-01T17:51:22.3279297Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748.
2024-11-01T17:51:22.3282809Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.3283871Z 
2024-11-01T17:51:22.3284520Z Context manager for training with uneven inputs across processes in DDP.
2024-11-01T17:51:22.3285505Z 
2024-11-01T17:51:22.3286266Z This context manager will keep track of already-joined DDP processes,
2024-11-01T17:51:22.3287808Z and "shadow" the forward and backward passes by inserting collective
2024-11-01T17:51:22.3289406Z communication operations to match with the ones created by non-joined
2024-11-01T17:51:22.3290897Z DDP processes. This will ensure each collective call has a corresponding
2024-11-01T17:51:22.3292616Z call by already-joined DDP processes, preventing hangs or errors that
2024-11-01T17:51:22.3293960Z would otherwise happen when training with uneven inputs across
2024-11-01T17:51:22.3295377Z processes. Alternatively, if the flag ``throw_on_early_termination`` is
2024-11-01T17:51:22.3296871Z specified to be ``True``, all trainers will throw an error once one rank
2024-11-01T17:51:22.3298285Z runs out of inputs, allowing these errors to be caught and handled
2024-11-01T17:51:22.3299425Z according to application logic.
2024-11-01T17:51:22.3299961Z 
2024-11-01T17:51:22.3300573Z Once all DDP processes have joined, the context manager will broadcast
2024-11-01T17:51:22.3302118Z the model corresponding to the last joined process to all processes to
2024-11-01T17:51:22.3303745Z ensure the model is the same across all processes
2024-11-01T17:51:22.3304667Z (which is guaranteed by DDP).
2024-11-01T17:51:22.3305132Z 
2024-11-01T17:51:22.3305684Z To use this to enable training with uneven inputs across processes,
2024-11-01T17:51:22.3307532Z simply wrap this context manager around your training loop. No further
2024-11-01T17:51:22.3308875Z modifications to the model or data loading is required.
2024-11-01T17:51:22.3309659Z 
2024-11-01T17:51:22.3309906Z .. warning::
2024-11-01T17:51:22.3310824Z     If the model or training loop this context manager is wrapped around
2024-11-01T17:51:22.3312173Z     has additional distributed collective operations, such as
2024-11-01T17:51:22.3313676Z     ``SyncBatchNorm`` in the model's forward pass, then the flag
2024-11-01T17:51:22.3315120Z     ``throw_on_early_termination`` must be enabled. This is because this
2024-11-01T17:51:22.3316733Z     context manager is not aware of non-DDP collective communication.
2024-11-01T17:51:22.3318115Z     This flag will cause all ranks to throw when any one rank
2024-11-01T17:51:22.3319477Z     exhausts inputs, allowing these errors to be caught and recovered
2024-11-01T17:51:22.3320581Z     from across all ranks.
2024-11-01T17:51:22.3321033Z 
2024-11-01T17:51:22.3321220Z Args:
2024-11-01T17:51:22.3321947Z     divide_by_initial_world_size (bool): If ``True``, will divide
2024-11-01T17:51:22.3323324Z         gradients by the initial ``world_size`` DDP training was launched
2024-11-01T17:51:22.3324603Z         with. If ``False``, will compute the effective world size
2024-11-01T17:51:22.3325911Z         (number of ranks that have not depleted their inputs yet) and
2024-11-01T17:51:22.3327136Z         divide gradients by that during allreduce. Set
2024-11-01T17:51:22.3328306Z         ``divide_by_initial_world_size=True`` to ensure every input
2024-11-01T17:51:22.3329574Z         sample including the uneven inputs have equal weight in terms of
2024-11-01T17:51:22.3330824Z         how much they contribute to the global gradient. This is
2024-11-01T17:51:22.3332064Z         achieved by always dividing the gradient by the initial
2024-11-01T17:51:22.3333378Z         ``world_size`` even when we encounter uneven inputs. If you set
2024-11-01T17:51:22.3334688Z         this to ``False``, we divide the gradient by the remaining
2024-11-01T17:51:22.3336023Z         number of nodes. This ensures parity with training on a smaller
2024-11-01T17:51:22.3337656Z         ``world_size`` although it also means the uneven inputs would
2024-11-01T17:51:22.3338979Z         contribute more towards the global gradient. Typically, you
2024-11-01T17:51:22.3340341Z         would want to set this to ``True`` for cases where the last few
2024-11-01T17:51:22.3341732Z         inputs of your training job are uneven. In extreme cases, where
2024-11-01T17:51:22.3343132Z         there is a large discrepancy in the number of inputs, setting
2024-11-01T17:51:22.3344292Z         this to ``False`` might provide better results.
2024-11-01T17:51:22.3345398Z     enable (bool): Whether to enable uneven input detection or not. Pass
2024-11-01T17:51:22.3346679Z         in ``enable=False`` to disable in cases where you know that
2024-11-01T17:51:22.3347899Z         inputs are even across participating processes. Default is
2024-11-01T17:51:22.3348914Z         ``True``.
2024-11-01T17:51:22.3349724Z     throw_on_early_termination (bool): Whether to throw an error
2024-11-01T17:51:22.3350912Z         or continue training when at least one rank has exhausted
2024-11-01T17:51:22.3352197Z         inputs. If ``True``, will throw upon the first rank reaching end
2024-11-01T17:51:22.3353518Z         of data. If ``False``, will continue training with a smaller
2024-11-01T17:51:22.3354938Z         effective world size until all ranks are joined. Note that if
2024-11-01T17:51:22.3356004Z         this flag is specified, then the flag
2024-11-01T17:51:22.3357037Z         ``divide_by_initial_world_size`` would be ignored. Default
2024-11-01T17:51:22.3357979Z         is ``False``.
2024-11-01T17:51:22.3358358Z 
2024-11-01T17:51:22.3358602Z 
2024-11-01T17:51:22.3358822Z Example::
2024-11-01T17:51:22.3359107Z 
2024-11-01T17:51:22.3359348Z     >>> # xdoctest: +SKIP("Distributed")
2024-11-01T17:51:22.3360123Z     >>> import torch
2024-11-01T17:51:22.3360760Z     >>> import torch.distributed as dist
2024-11-01T17:51:22.3361515Z     >>> import os
2024-11-01T17:51:22.3362087Z     >>> import torch.multiprocessing as mp
2024-11-01T17:51:22.3362942Z     >>> import torch.nn as nn
2024-11-01T17:51:22.3363569Z     >>> # On each spawned worker
2024-11-01T17:51:22.3364180Z     >>> def worker(rank):
2024-11-01T17:51:22.3364927Z     >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2024-11-01T17:51:22.3365960Z     >>>     torch.cuda.set_device(rank)
2024-11-01T17:51:22.3366839Z     >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2024-11-01T17:51:22.3367910Z     >>>     model = torch.nn.parallel.DistributedDataParallel(
2024-11-01T17:51:22.3368991Z     >>>         model, device_ids=[rank], output_device=rank
2024-11-01T17:51:22.3369821Z     >>>     )
2024-11-01T17:51:22.3370417Z     >>>     # Rank 1 gets one more input than rank 0.
2024-11-01T17:51:22.3371539Z     >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2024-11-01T17:51:22.3372569Z     >>>     with model.join():
2024-11-01T17:51:22.3373274Z     >>>         for _ in range(5):
2024-11-01T17:51:22.3374030Z     >>>             for inp in inputs:
2024-11-01T17:51:22.3374843Z     >>>                 loss = model(inp).sum()
2024-11-01T17:51:22.3375693Z     >>>                 loss.backward()
2024-11-01T17:51:22.3376757Z     >>>     # Without the join() API, the below synchronization will hang
2024-11-01T17:51:22.3378249Z     >>>     # blocking for rank 1's allreduce to complete.
2024-11-01T17:51:22.3379158Z     >>>     torch.cuda.synchronize(device=rank)
2024-11-01T17:51:22.3379778Z 
2024-11-01T17:51:22.3380572Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.3381539Z 
2024-11-01T17:51:22.3383724Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039.
2024-11-01T17:51:22.3386709Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.3387693Z 
2024-11-01T17:51:22.3388561Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2024-11-01T17:51:22.3389916Z 
2024-11-01T17:51:22.3390480Z Registers an optimizer with DDP such that the optimization for a
2024-11-01T17:51:22.3391998Z parameter will run immediately when that parameter's gradient is
2024-11-01T17:51:22.3393296Z finished with reduction, instead of waiting for all parameters'
2024-11-01T17:51:22.3394812Z gradients to finish reduction. This can result in a training speedup
2024-11-01T17:51:22.3396291Z depending on your workload since the optimizer can run while gradient
2024-11-01T17:51:22.3397872Z reduction for other parameters are still ongoing. In addition, this has
2024-11-01T17:51:22.3399404Z the potential to reduce peak memory consumption during training, as it
2024-11-01T17:51:22.3401093Z only needs to load the per-parameter optimizer states of a single
2024-11-01T17:51:22.3402682Z parameter at a time, instead of loading all per-parameter optimizer
2024-11-01T17:51:22.3403787Z states at once.
2024-11-01T17:51:22.3404146Z 
2024-11-01T17:51:22.3404326Z Args:
2024-11-01T17:51:22.3405127Z     optim (Type): a ``torch.optim.Optimizer`` class to be registered
2024-11-01T17:51:22.3406199Z     as a fused optimizer.
2024-11-01T17:51:22.3407314Z     *args (Sequence[Any]): Arguments to forward to `optim`.
2024-11-01T17:51:22.3408626Z     optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2024-11-01T17:51:22.3409990Z     to optimize, similar to `params` argument of traditional `torch.optim`
2024-11-01T17:51:22.3411383Z     Optimizers. If this is omitted, all DDP model parameters will be
2024-11-01T17:51:22.3412439Z     optimized.
2024-11-01T17:51:22.3413612Z     **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2024-11-01T17:51:22.3414444Z 
2024-11-01T17:51:22.3414674Z .. warning ::
2024-11-01T17:51:22.3415568Z     _register_fused_optim should only be called once on a DDP instance,
2024-11-01T17:51:22.3417035Z     and registering multiple fused optimizers for the same DDP model
2024-11-01T17:51:22.3418217Z     is not currently supported. Please ping
2024-11-01T17:51:22.3419378Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2024-11-01T17:51:22.3420418Z     for your use case.
2024-11-01T17:51:22.3420796Z 
2024-11-01T17:51:22.3421008Z .. warning ::
2024-11-01T17:51:22.3421801Z     _register_fused_optim and register_comm_hook currently do not
2024-11-01T17:51:22.3423114Z     compose together, meaning that custom DDP communication hooks are
2024-11-01T17:51:22.3424352Z     not supported with overlapped optimizers. Please ping
2024-11-01T17:51:22.3425669Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2024-11-01T17:51:22.3426791Z     for your use case.
2024-11-01T17:51:22.3427186Z 
2024-11-01T17:51:22.3427403Z .. warning ::
2024-11-01T17:51:22.3428302Z     Gradient accumulation and DDP `no_sync` are currently not supported
2024-11-01T17:51:22.3429471Z     with overlapped optimizer. Please ping
2024-11-01T17:51:22.3430714Z     https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2024-11-01T17:51:22.3431819Z     for your use case.
2024-11-01T17:51:22.3432227Z 
2024-11-01T17:51:22.3432429Z Example::
2024-11-01T17:51:22.3432737Z 
2024-11-01T17:51:22.3433059Z     >>> # xdoctest: +SKIP("No rendezvous handler")
2024-11-01T17:51:22.3434856Z     >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2024-11-01T17:51:22.3436443Z     >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2024-11-01T17:51:22.3437509Z     >>> lr = 1e-2
2024-11-01T17:51:22.3438077Z     >>> betas = (0.9, 0.99)
2024-11-01T17:51:22.3438772Z     >>> eps = 1e-6
2024-11-01T17:51:22.3439672Z     >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2024-11-01T17:51:22.3440845Z     >>> # Example with subset of parameters
2024-11-01T17:51:22.3441792Z     >>> params_to_opt = [list(net.parameters())[0]]
2024-11-01T17:51:22.3442688Z     >>> net._register_fused_optim(
2024-11-01T17:51:22.3443807Z     ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2024-11-01T17:51:22.3445253Z     ... )
2024-11-01T17:51:22.3445563Z 
2024-11-01T17:51:22.3446421Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.3447403Z 
2024-11-01T17:51:22.3663440Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6.
2024-11-01T17:51:22.3666377Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.3668037Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2024-11-01T17:51:22.3668883Z 
2024-11-01T17:51:22.3669592Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2024-11-01T17:51:22.3671327Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2024-11-01T17:51:22.3673014Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2024-11-01T17:51:22.3674953Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2024-11-01T17:51:22.3676046Z 
2024-11-01T17:51:22.3676269Z     .. note::
2024-11-01T17:51:22.3677195Z         Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2024-11-01T17:51:22.3678625Z         than the utility function ``convert_conv2d_weight_memory_format``. Any
2024-11-01T17:51:22.3680150Z         layer with 4d weight will be affected by ``model.to``, which does not
2024-11-01T17:51:22.3681689Z         necessarily benefit from conversion to specified ``memory_format``.
2024-11-01T17:51:22.3683383Z         One place we are confident in is that NHWC(channels_last) conversion for
2024-11-01T17:51:22.3684960Z         convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2024-11-01T17:51:22.3686446Z         even in cases where we have to apply permutation to input tensors.
2024-11-01T17:51:22.3687360Z 
2024-11-01T17:51:22.3687976Z         Hence our strategy here is to convert only the weight of convolution to
2024-11-01T17:51:22.3689221Z         channels_last. This ensures that;
2024-11-01T17:51:22.3690397Z         1. Fast convolution kernels will be used, the benefit of which could
2024-11-01T17:51:22.3691908Z         outweigh overhead of permutation (if input is not in the same format).
2024-11-01T17:51:22.3693334Z         2. No unnecessary permutations are applied on layers that do not benefit
2024-11-01T17:51:22.3694405Z         from memory_format conversion.
2024-11-01T17:51:22.3694962Z 
2024-11-01T17:51:22.3695600Z         The optimal case is that, layers between convolution layers are channels
2024-11-01T17:51:22.3697140Z         last compatible. Input tensor would be permuted to channels last when it
2024-11-01T17:51:22.3698538Z         encounters the first convolution layer and stay in that memory format.
2024-11-01T17:51:22.3699960Z         Hence following convolutions will not need to permute its input tensor.
2024-11-01T17:51:22.3700886Z 
2024-11-01T17:51:22.3701481Z         In case where a channels last incompatible layer is between convolution
2024-11-01T17:51:22.3702989Z         layers, we need to permute the input tensor back to contiguous format
2024-11-01T17:51:22.3704437Z         for that layer. The input tensor will go through the remaining layers in
2024-11-01T17:51:22.3705935Z         contiguous format and be permuted to channels last when it encounters
2024-11-01T17:51:22.3708039Z         another convolution layer. There's no point in propagating that
2024-11-01T17:51:22.3709441Z         permutation to an earlier layer, as most layers are quite agnostic to
2024-11-01T17:51:22.3710610Z         ``memory_format``.
2024-11-01T17:51:22.3711018Z 
2024-11-01T17:51:22.3711591Z         This claim might change when PyTorch supports fusion of permutation, as
2024-11-01T17:51:22.3713165Z         there might have been a better spot to fuse the permutation other than
2024-11-01T17:51:22.3714485Z         immediately before a convolution.
2024-11-01T17:51:22.3715347Z 
2024-11-01T17:51:22.3715547Z     Args:
2024-11-01T17:51:22.3716408Z         module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2024-11-01T17:51:22.3717441Z                             ``nn.Module``
2024-11-01T17:51:22.3718318Z         memory_format: user specified ``memory_format``,
2024-11-01T17:51:22.3719505Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2024-11-01T17:51:22.3720316Z 
2024-11-01T17:51:22.3720520Z     Returns:
2024-11-01T17:51:22.3721180Z         The original module with updated ``nn.Conv2d``
2024-11-01T17:51:22.3721836Z 
2024-11-01T17:51:22.3722062Z     Example:
2024-11-01T17:51:22.3722703Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:22.3723726Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2024-11-01T17:51:22.3724951Z         >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda")
2024-11-01T17:51:22.3726171Z         >>> model = nn.Sequential(
2024-11-01T17:51:22.3727046Z         >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2024-11-01T17:51:22.3727913Z         >>> # This is identical to:
2024-11-01T17:51:22.3729127Z         >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2024-11-01T17:51:22.3730866Z         >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2024-11-01T17:51:22.3732172Z         >>> out = model(input)
2024-11-01T17:51:22.3732832Z     
2024-11-01T17:51:22.3733906Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.3734805Z 
2024-11-01T17:51:22.3737142Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81.
2024-11-01T17:51:22.3739891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.3741427Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2024-11-01T17:51:22.3743015Z     The conversion recursively applies to nested ``nn.Module``, including ``module``.
2024-11-01T17:51:22.3744906Z     Note that it only changes the memory_format, but not the semantics of each dimensions.
2024-11-01T17:51:22.3746728Z     This function is used to facilitate the computation to adopt NHWC kernels, which
2024-11-01T17:51:22.3748472Z     provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2024-11-01T17:51:22.3749666Z 
2024-11-01T17:51:22.3749915Z     .. note::
2024-11-01T17:51:22.3750895Z         Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2024-11-01T17:51:22.3752464Z         than the utility function ``convert_conv3d_weight_memory_format``. Any
2024-11-01T17:51:22.3754112Z         layer with 4d weight will be affected by ``model.to``, which does not
2024-11-01T17:51:22.3755638Z         necessarily benefit from conversion to specified ``memory_format``.
2024-11-01T17:51:22.3757274Z         One place we are confident in is that NDHWC(channels_last_3d) conversion for
2024-11-01T17:51:22.3758931Z         convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2024-11-01T17:51:22.3760455Z         even in cases where we have to apply permutation to input tensors.
2024-11-01T17:51:22.3761383Z 
2024-11-01T17:51:22.3761949Z         Hence our strategy here is to convert only the weight of convolution to
2024-11-01T17:51:22.3763155Z         channels_last_3d. This ensures that;
2024-11-01T17:51:22.3764371Z         1. Fast convolution kernels will be used, the benefit of which could
2024-11-01T17:51:22.3765901Z         outweigh overhead of permutation (if input is not in the same format).
2024-11-01T17:51:22.3767456Z         2. No unnecessary permutations are applied on layers that do not benefit
2024-11-01T17:51:22.3768689Z         from memory_format conversion.
2024-11-01T17:51:22.3769221Z 
2024-11-01T17:51:22.3769826Z         The optimal case is that, layers between convolution layers are channels
2024-11-01T17:51:22.3771530Z         last compatible. Input tensor would be permuted to channels last when it
2024-11-01T17:51:22.3772999Z         encounters the first convolution layer and stay in that memory format.
2024-11-01T17:51:22.3774449Z         Hence following convolutions will not need to permute its input tensor.
2024-11-01T17:51:22.3775379Z 
2024-11-01T17:51:22.3775977Z         In case where a channels last incompatible layer is between convolution
2024-11-01T17:51:22.3777499Z         layers, we need to permute the input tensor back to contiguous format
2024-11-01T17:51:22.3778996Z         for that layer. The input tensor will go through the remaining layers in
2024-11-01T17:51:22.3780566Z         contiguous format and be permuted to channels last when it encounters
2024-11-01T17:51:22.3782413Z         another convolution layer. There's no point in propagating that
2024-11-01T17:51:22.3783740Z         permutation to an earlier layer, as most layers are quite agnostic to
2024-11-01T17:51:22.3784916Z         ``memory_format``.
2024-11-01T17:51:22.3785356Z 
2024-11-01T17:51:22.3786012Z         This claim might change when PyTorch supports fusion of permutation, as
2024-11-01T17:51:22.3787583Z         there might have been a better spot to fuse the permutation other than
2024-11-01T17:51:22.3788812Z         immediately before a convolution.
2024-11-01T17:51:22.3789387Z 
2024-11-01T17:51:22.3789584Z     Args:
2024-11-01T17:51:22.3790479Z         module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2024-11-01T17:51:22.3791659Z                             ``nn.Module``
2024-11-01T17:51:22.3792969Z         memory_format: user specified ``memory_format``,
2024-11-01T17:51:22.3794191Z             e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2024-11-01T17:51:22.3794761Z 
2024-11-01T17:51:22.3794974Z     Returns:
2024-11-01T17:51:22.3795640Z         The original module with updated ``nn.Conv3d``
2024-11-01T17:51:22.3796337Z 
2024-11-01T17:51:22.3796537Z     Example:
2024-11-01T17:51:22.3797229Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:22.3798317Z         >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2024-11-01T17:51:22.3799679Z         >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda")
2024-11-01T17:51:22.3800883Z         >>> model = nn.Sequential(
2024-11-01T17:51:22.3801705Z         >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2024-11-01T17:51:22.3802574Z         >>> # This is identical to:
2024-11-01T17:51:22.3803769Z         >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2024-11-01T17:51:22.3805288Z         >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2024-11-01T17:51:22.3806400Z         >>> out = model(input)
2024-11-01T17:51:22.3807360Z     
2024-11-01T17:51:22.3808486Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.3809399Z 
2024-11-01T17:51:22.3987568Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936.
2024-11-01T17:51:22.3990269Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.3991896Z Prune tensor by removing random channels along the specified dimension.
2024-11-01T17:51:22.3992946Z 
2024-11-01T17:51:22.3993550Z     Prunes tensor corresponding to parameter called ``name`` in ``module``
2024-11-01T17:51:22.3995241Z     by removing the specified ``amount`` of (currently unpruned) channels
2024-11-01T17:51:22.3996580Z     along the specified ``dim`` selected at random.
2024-11-01T17:51:22.3997745Z     Modifies module in place (and also return the modified module)
2024-11-01T17:51:22.3998756Z     by:
2024-11-01T17:51:22.3999058Z 
2024-11-01T17:51:22.3999857Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:22.4001352Z        binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:22.4003317Z     2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:22.4004779Z        original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:22.4005978Z        ``name+'_orig'``.
2024-11-01T17:51:22.4006369Z 
2024-11-01T17:51:22.4006886Z     Args:
2024-11-01T17:51:22.4007602Z         module (nn.Module): module containing the tensor to prune
2024-11-01T17:51:22.4008876Z         name (str): parameter name within ``module`` on which pruning
2024-11-01T17:51:22.4009925Z                 will act.
2024-11-01T17:51:22.4010783Z         amount (int or float): quantity of parameters to prune.
2024-11-01T17:51:22.4012062Z             If ``float``, should be between 0.0 and 1.0 and represent the
2024-11-01T17:51:22.4013380Z             fraction of parameters to prune. If ``int``, it represents the
2024-11-01T17:51:22.4014576Z             absolute number of parameters to prune.
2024-11-01T17:51:22.4015722Z         dim (int): index of the dim along which we define channels to prune.
2024-11-01T17:51:22.4016536Z 
2024-11-01T17:51:22.4016719Z     Returns:
2024-11-01T17:51:22.4017620Z         module (nn.Module): modified (i.e. pruned) version of the input module
2024-11-01T17:51:22.4018570Z 
2024-11-01T17:51:22.4018792Z     Examples:
2024-11-01T17:51:22.4019334Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:22.4020073Z         >>> m = prune.random_structured(
2024-11-01T17:51:22.4021230Z         ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
2024-11-01T17:51:22.4022121Z         ... )
2024-11-01T17:51:22.4022921Z         >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
2024-11-01T17:51:22.4024243Z         >>> print(columns_pruned)
2024-11-01T17:51:22.4024960Z         3
2024-11-01T17:51:22.4025436Z     
2024-11-01T17:51:22.4026559Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.4027564Z 
2024-11-01T17:51:22.4029239Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977.
2024-11-01T17:51:22.4031835Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.4034054Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2024-11-01T17:51:22.4035255Z 
2024-11-01T17:51:22.4035907Z     Prunes tensor corresponding to parameter called ``name`` in ``module``
2024-11-01T17:51:22.4037499Z     by removing the specified ``amount`` of (currently unpruned) channels
2024-11-01T17:51:22.4039075Z     along the specified ``dim`` with the lowest L\ ``n``-norm.
2024-11-01T17:51:22.4040413Z     Modifies module in place (and also return the modified module)
2024-11-01T17:51:22.4041485Z     by:
2024-11-01T17:51:22.4041782Z 
2024-11-01T17:51:22.4042552Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:22.4043973Z        binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:22.4045318Z     2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:22.4046597Z        original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:22.4047704Z        ``name+'_orig'``.
2024-11-01T17:51:22.4048079Z 
2024-11-01T17:51:22.4048273Z     Args:
2024-11-01T17:51:22.4048955Z         module (nn.Module): module containing the tensor to prune
2024-11-01T17:51:22.4050215Z         name (str): parameter name within ``module`` on which pruning
2024-11-01T17:51:22.4051163Z                 will act.
2024-11-01T17:51:22.4052021Z         amount (int or float): quantity of parameters to prune.
2024-11-01T17:51:22.4053201Z             If ``float``, should be between 0.0 and 1.0 and represent the
2024-11-01T17:51:22.4054534Z             fraction of parameters to prune. If ``int``, it represents the
2024-11-01T17:51:22.4055734Z             absolute number of parameters to prune.
2024-11-01T17:51:22.4057042Z         n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2024-11-01T17:51:22.4058724Z             entries for argument ``p`` in :func:`torch.norm`.
2024-11-01T17:51:22.4075661Z         dim (int): index of the dim along which we define channels to prune.
2024-11-01T17:51:22.4077047Z         importance_scores (torch.Tensor): tensor of importance scores (of same
2024-11-01T17:51:22.4078366Z             shape as module parameter) used to compute mask for pruning.
2024-11-01T17:51:22.4079628Z             The values in this tensor indicate the importance of the corresponding
2024-11-01T17:51:22.4080888Z             elements in the parameter being pruned.
2024-11-01T17:51:22.4082091Z             If unspecified or None, the module parameter will be used in its place.
2024-11-01T17:51:22.4083080Z 
2024-11-01T17:51:22.4083283Z     Returns:
2024-11-01T17:51:22.4084126Z         module (nn.Module): modified (i.e. pruned) version of the input module
2024-11-01T17:51:22.4085004Z 
2024-11-01T17:51:22.4085226Z     Examples:
2024-11-01T17:51:22.4085823Z         >>> from torch.nn.utils import prune
2024-11-01T17:51:22.4086620Z         >>> m = prune.ln_structured(
2024-11-01T17:51:22.4087878Z         ...     nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf')
2024-11-01T17:51:22.4088824Z         ... )
2024-11-01T17:51:22.4089268Z     
2024-11-01T17:51:22.4090279Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.4091265Z 
2024-11-01T17:51:22.4093018Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024.
2024-11-01T17:51:22.4095830Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.4096792Z 
2024-11-01T17:51:22.4097915Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2024-11-01T17:51:22.4099469Z 
2024-11-01T17:51:22.4099709Z Modifies modules in place by:
2024-11-01T17:51:22.4100226Z 
2024-11-01T17:51:22.4100964Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:22.4102419Z    binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:22.4103921Z 2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:22.4105345Z    original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:22.4106539Z    ``name+'_orig'``.
2024-11-01T17:51:22.4107278Z 
2024-11-01T17:51:22.4107472Z Args:
2024-11-01T17:51:22.4108276Z     parameters (Iterable of (module, name) tuples): parameters of
2024-11-01T17:51:22.4109688Z         the model to prune in a global fashion, i.e. by aggregating all
2024-11-01T17:51:22.4111125Z         weights prior to deciding which ones to prune. module must be of
2024-11-01T17:51:22.4112445Z         type :class:`nn.Module`, and name must be a string.
2024-11-01T17:51:22.4113776Z     pruning_method (function): a valid pruning function from this module,
2024-11-01T17:51:22.4115304Z         or a custom one implemented by the user that satisfies the
2024-11-01T17:51:22.4116878Z         implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2024-11-01T17:51:22.4118159Z     importance_scores (dict): a dictionary mapping (module, name) tuples to
2024-11-01T17:51:22.4119750Z         the corresponding parameter's importance scores tensor. The tensor
2024-11-01T17:51:22.4121261Z         should be the same shape as the parameter, and is used for computing
2024-11-01T17:51:22.4122421Z         mask for pruning.
2024-11-01T17:51:22.4123271Z         If unspecified or None, the parameter will be used in place of its
2024-11-01T17:51:22.4124285Z         importance scores.
2024-11-01T17:51:22.4125049Z     kwargs: other keyword arguments such as:
2024-11-01T17:51:22.4126233Z         amount (int or float): quantity of parameters to prune across the
2024-11-01T17:51:22.4127347Z         specified parameters.
2024-11-01T17:51:22.4128257Z         If ``float``, should be between 0.0 and 1.0 and represent the
2024-11-01T17:51:22.4130009Z         fraction of parameters to prune. If ``int``, it represents the
2024-11-01T17:51:22.4131229Z         absolute number of parameters to prune.
2024-11-01T17:51:22.4131894Z 
2024-11-01T17:51:22.4132085Z Raises:
2024-11-01T17:51:22.4132911Z     TypeError: if ``PRUNING_TYPE != 'unstructured'``
2024-11-01T17:51:22.4133618Z 
2024-11-01T17:51:22.4133825Z Note:
2024-11-01T17:51:22.4134644Z     Since global structured pruning doesn't make much sense unless the
2024-11-01T17:51:22.4136017Z     norm is normalized by the size of the parameter, we now limit the
2024-11-01T17:51:22.4137266Z     scope of global pruning to unstructured methods.
2024-11-01T17:51:22.4138028Z 
2024-11-01T17:51:22.4138249Z Examples:
2024-11-01T17:51:22.4138818Z     >>> from torch.nn.utils import prune
2024-11-01T17:51:22.4139631Z     >>> from collections import OrderedDict
2024-11-01T17:51:22.4140497Z     >>> net = nn.Sequential(OrderedDict([
2024-11-01T17:51:22.4141515Z     ...     ('first', nn.Linear(10, 4)),
2024-11-01T17:51:22.4142484Z     ...     ('second', nn.Linear(4, 1)),
2024-11-01T17:51:22.4143252Z     ... ]))
2024-11-01T17:51:22.4143810Z     >>> parameters_to_prune = (
2024-11-01T17:51:22.4144634Z     ...     (net.first, 'weight'),
2024-11-01T17:51:22.4145486Z     ...     (net.second, 'weight'),
2024-11-01T17:51:22.4146124Z     ... )
2024-11-01T17:51:22.4146672Z     >>> prune.global_unstructured(
2024-11-01T17:51:22.4147414Z     ...     parameters_to_prune,
2024-11-01T17:51:22.4148241Z     ...     pruning_method=prune.L1Unstructured,
2024-11-01T17:51:22.4149116Z     ...     amount=10,
2024-11-01T17:51:22.4149702Z     ... )
2024-11-01T17:51:22.4150955Z     >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2024-11-01T17:51:22.4152137Z     tensor(10)
2024-11-01T17:51:22.4152437Z 
2024-11-01T17:51:22.4152445Z 
2024-11-01T17:51:22.4153265Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.4154320Z 
2024-11-01T17:51:22.4156025Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143.
2024-11-01T17:51:22.4158611Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.4160902Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2024-11-01T17:51:22.4162321Z 
2024-11-01T17:51:22.4162898Z     Modifies module in place (and also return the modified module) by:
2024-11-01T17:51:22.4163792Z 
2024-11-01T17:51:22.4164548Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:22.4166002Z        binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:22.4167508Z     2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:22.4168936Z        original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:22.4170137Z        ``name+'_orig'``.
2024-11-01T17:51:22.4170581Z 
2024-11-01T17:51:22.4170775Z     Args:
2024-11-01T17:51:22.4171548Z         module (nn.Module): module containing the tensor to prune
2024-11-01T17:51:22.4172839Z         name (str): parameter name within ``module`` on which pruning
2024-11-01T17:51:22.4173884Z             will act.
2024-11-01T17:51:22.4174732Z         mask (Tensor): binary mask to be applied to the parameter.
2024-11-01T17:51:22.4175505Z 
2024-11-01T17:51:22.4175700Z     Returns:
2024-11-01T17:51:22.4176602Z         module (nn.Module): modified (i.e. pruned) version of the input module
2024-11-01T17:51:22.4177553Z 
2024-11-01T17:51:22.4177774Z     Examples:
2024-11-01T17:51:22.4178422Z         >>> from torch.nn.utils import prune
2024-11-01T17:51:22.4179309Z         >>> m = prune.custom_from_mask(
2024-11-01T17:51:22.4180542Z         ...     nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0])
2024-11-01T17:51:22.4181545Z         ... )
2024-11-01T17:51:22.4182116Z         >>> print(m.bias_mask)
2024-11-01T17:51:22.4183048Z         tensor([0., 1., 0.])
2024-11-01T17:51:22.4183509Z 
2024-11-01T17:51:22.4183691Z     
2024-11-01T17:51:22.4184765Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.4185760Z 
2024-11-01T17:51:22.5331247Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=116.
2024-11-01T17:51:22.5333949Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.5335852Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2024-11-01T17:51:22.5337213Z 
2024-11-01T17:51:22.5337891Z     Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2024-11-01T17:51:22.5339482Z     Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2024-11-01T17:51:22.5340951Z     Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2024-11-01T17:51:22.5342011Z     (UAI 2018).
2024-11-01T17:51:22.5342376Z 
2024-11-01T17:51:22.5342893Z     Exponential Moving Average is a variation of `Polyak averaging`_,
2024-11-01T17:51:22.5344462Z     but using exponential weights instead of equal weights across iterations.
2024-11-01T17:51:22.5345396Z 
2024-11-01T17:51:22.5346015Z     AveragedModel class creates a copy of the provided module :attr:`model`
2024-11-01T17:51:22.5347542Z     on the device :attr:`device` and allows to compute running averages of the
2024-11-01T17:51:22.5348673Z     parameters of the :attr:`model`.
2024-11-01T17:51:22.5349204Z 
2024-11-01T17:51:22.5349410Z     Args:
2024-11-01T17:51:22.5350500Z         model (torch.nn.Module): model to use with SWA/EMA
2024-11-01T17:51:22.5351807Z         device (torch.device, optional): if provided, the averaged model will be
2024-11-01T17:51:22.5352992Z             stored on the :attr:`device`
2024-11-01T17:51:22.5354266Z         avg_fn (function, optional): the averaging function used to update
2024-11-01T17:51:22.5355675Z             parameters; the function must take in the current value of the
2024-11-01T17:51:22.5357089Z             :class:`AveragedModel` parameter, the current value of :attr:`model`
2024-11-01T17:51:22.5358455Z             parameter, and the number of models already averaged; if None,
2024-11-01T17:51:22.5359679Z             an equally weighted average is used (default: None)
2024-11-01T17:51:22.5361006Z         multi_avg_fn (function, optional): the averaging function used to update
2024-11-01T17:51:22.5362560Z             parameters inplace; the function must take in the current values of the
2024-11-01T17:51:22.5364221Z             :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2024-11-01T17:51:22.5365918Z             parameters as a list, and the number of models already averaged; if None,
2024-11-01T17:51:22.5367291Z             an equally weighted average is used (default: None)
2024-11-01T17:51:22.5368558Z         use_buffers (bool): if ``True``, it will compute running averages for
2024-11-01T17:51:22.5370070Z             both the parameters and the buffers of the model. (default: ``False``)
2024-11-01T17:51:22.5370989Z 
2024-11-01T17:51:22.5371194Z     Example:
2024-11-01T17:51:22.5371820Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:22.5372804Z         >>> loader, optimizer, model, loss_fn = ...
2024-11-01T17:51:22.5373875Z         >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2024-11-01T17:51:22.5375128Z         >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2024-11-01T17:51:22.5376306Z         >>>                                     T_max=300)
2024-11-01T17:51:22.5377180Z         >>> swa_start = 160
2024-11-01T17:51:22.5378007Z         >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2024-11-01T17:51:22.5378922Z         >>> for i in range(300):
2024-11-01T17:51:22.5379685Z         >>>      for input, target in loader:
2024-11-01T17:51:22.5380530Z         >>>          optimizer.zero_grad()
2024-11-01T17:51:22.5381938Z         >>>          loss_fn(model(input), target).backward()
2024-11-01T17:51:22.5382884Z         >>>          optimizer.step()
2024-11-01T17:51:22.5383668Z         >>>      if i > swa_start:
2024-11-01T17:51:22.5384512Z         >>>          swa_model.update_parameters(model)
2024-11-01T17:51:22.5385451Z         >>>          swa_scheduler.step()
2024-11-01T17:51:22.5386224Z         >>>      else:
2024-11-01T17:51:22.5386868Z         >>>          scheduler.step()
2024-11-01T17:51:22.5387605Z         >>>
2024-11-01T17:51:22.5388322Z         >>> # Update bn statistics for the swa_model at the end
2024-11-01T17:51:22.5389472Z         >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2024-11-01T17:51:22.5390184Z 
2024-11-01T17:51:22.5390941Z     You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2024-11-01T17:51:22.5392523Z     If no averaging function is provided, the default is to compute
2024-11-01T17:51:22.5394195Z     equally-weighted average of the weights (SWA).
2024-11-01T17:51:22.5394916Z 
2024-11-01T17:51:22.5395108Z     Example:
2024-11-01T17:51:22.5395759Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:22.5396906Z         >>> # Compute exponential moving averages of the weights and buffers
2024-11-01T17:51:22.5398191Z         >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2024-11-01T17:51:22.5399506Z         >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2024-11-01T17:51:22.5400399Z 
2024-11-01T17:51:22.5400606Z     .. note::
2024-11-01T17:51:22.5401700Z         When using SWA/EMA with models containing Batch Normalization you may
2024-11-01T17:51:22.5403191Z         need to update the activation statistics for Batch Normalization.
2024-11-01T17:51:22.5404762Z         This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2024-11-01T17:51:22.5406391Z         or by setting :attr:`use_buffers` to `True`. The first approach updates the
2024-11-01T17:51:22.5408616Z         statistics in a post-training step by passing data through the model. The
2024-11-01T17:51:22.5410290Z         second does it during the parameter update phase by averaging all buffers.
2024-11-01T17:51:22.5411946Z         Empirical evidence has shown that updating the statistics in normalization
2024-11-01T17:51:22.5413620Z         layers increases accuracy, but you may wish to empirically test which
2024-11-01T17:51:22.5414962Z         approach yields the best results in your problem.
2024-11-01T17:51:22.5415658Z 
2024-11-01T17:51:22.5415890Z     .. note::
2024-11-01T17:51:22.5416951Z         :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2024-11-01T17:51:22.5418054Z 
2024-11-01T17:51:22.5418254Z     .. note::
2024-11-01T17:51:22.5419137Z         When :meth:`update_parameters` is called for the first time (i.e.
2024-11-01T17:51:22.5420475Z         :attr:`n_averaged` is `0`) the parameters of `model` are copied
2024-11-01T17:51:22.5421883Z         to the parameters of :class:`AveragedModel`. For every subsequent
2024-11-01T17:51:22.5423320Z         call of :meth:`update_parameters` the function `avg_fn` is used
2024-11-01T17:51:22.5424440Z         to update the parameters.
2024-11-01T17:51:22.5424993Z 
2024-11-01T17:51:22.5425561Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2024-11-01T17:51:22.5426717Z         https://arxiv.org/abs/1803.05407
2024-11-01T17:51:22.5427996Z     .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2024-11-01T17:51:22.5429176Z         Average:
2024-11-01T17:51:22.5429783Z         https://arxiv.org/abs/1806.05594
2024-11-01T17:51:22.5431177Z     .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2024-11-01T17:51:22.5432337Z         https://arxiv.org/abs/1904.11943
2024-11-01T17:51:22.5433742Z     .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2024-11-01T17:51:22.5435042Z         Generalizes Well:
2024-11-01T17:51:22.5435735Z         https://arxiv.org/abs/2001.02312
2024-11-01T17:51:22.5436866Z     .. _Polyak averaging:
2024-11-01T17:51:22.5437821Z         https://paperswithcode.com/method/polyak-averaging
2024-11-01T17:51:22.5438716Z     
2024-11-01T17:51:22.5439820Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.5440797Z 
2024-11-01T17:51:22.5442446Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=368.
2024-11-01T17:51:22.5444919Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:22.5446531Z Anneals the learning rate in each parameter group to a fixed value.
2024-11-01T17:51:22.5447411Z 
2024-11-01T17:51:22.5448044Z     This learning rate scheduler is meant to be used with Stochastic Weight
2024-11-01T17:51:22.5449510Z     Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2024-11-01T17:51:22.5450425Z 
2024-11-01T17:51:22.5450616Z     Args:
2024-11-01T17:51:22.5451343Z         optimizer (torch.optim.Optimizer): wrapped optimizer
2024-11-01T17:51:22.5452620Z         swa_lrs (float or list): the learning rate value for all param groups
2024-11-01T17:51:22.5453784Z             together or separately for each group.
2024-11-01T17:51:22.5454977Z         annealing_epochs (int): number of epochs in the annealing phase
2024-11-01T17:51:22.5456002Z             (default: 10)
2024-11-01T17:51:22.5456968Z         annealing_strategy (str): "cos" or "linear"; specifies the annealing
2024-11-01T17:51:22.5458395Z             strategy: "cos" for cosine annealing, "linear" for linear annealing
2024-11-01T17:51:22.5459760Z             (default: "cos")
2024-11-01T17:51:22.5460918Z         last_epoch (int): the index of the last epoch (default: -1)
2024-11-01T17:51:22.5461732Z 
2024-11-01T17:51:22.5462245Z     The :class:`SWALR` scheduler can be used together with other
2024-11-01T17:51:22.5463647Z     schedulers to switch to a constant learning rate late in the training
2024-11-01T17:51:22.5464798Z     as in the example below.
2024-11-01T17:51:22.5465237Z 
2024-11-01T17:51:22.5465434Z     Example:
2024-11-01T17:51:22.5466095Z         >>> # xdoctest: +SKIP("Undefined variables")
2024-11-01T17:51:22.5467058Z         >>> loader, optimizer, model = ...
2024-11-01T17:51:22.5467939Z         >>> lr_lambda = lambda epoch: 0.9
2024-11-01T17:51:22.5469128Z         >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2024-11-01T17:51:22.5470278Z         >>>        lr_lambda=lr_lambda)
2024-11-01T17:51:22.5471292Z         >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2024-11-01T17:51:22.5472504Z         >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2024-11-01T17:51:22.5473510Z         >>> swa_start = 160
2024-11-01T17:51:22.5474314Z         >>> for i in range(300):
2024-11-01T17:51:22.5475035Z         >>>      for input, target in loader:
2024-11-01T17:51:22.5475943Z         >>>          optimizer.zero_grad()
2024-11-01T17:51:22.5476911Z         >>>          loss_fn(model(input), target).backward()
2024-11-01T17:51:22.5477892Z         >>>          optimizer.step()
2024-11-01T17:51:22.5478694Z         >>>      if i > swa_start:
2024-11-01T17:51:22.5479470Z         >>>          swa_scheduler.step()
2024-11-01T17:51:22.5480269Z         >>>      else:
2024-11-01T17:51:22.5480917Z         >>>          scheduler.step()
2024-11-01T17:51:22.5481471Z 
2024-11-01T17:51:22.5482080Z     .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2024-11-01T17:51:22.5483310Z         https://arxiv.org/abs/1803.05407
2024-11-01T17:51:22.5484074Z     
2024-11-01T17:51:22.5485245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:22.5486257Z 
2024-11-01T17:51:23.0768683Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274.
2024-11-01T17:51:23.0771176Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:23.0773020Z Asserts that ``actual`` and ``expected`` are close.
2024-11-01T17:51:23.0773723Z 
2024-11-01T17:51:23.0774928Z     If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2024-11-01T17:51:23.0776109Z 
2024-11-01T17:51:23.0776355Z     .. math::
2024-11-01T17:51:23.0776682Z 
2024-11-01T17:51:23.0778000Z         \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2024-11-01T17:51:23.0779377Z 
2024-11-01T17:51:23.0780588Z     Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2024-11-01T17:51:23.0782232Z     only considered equal to each other if ``equal_nan`` is ``True``.
2024-11-01T17:51:23.0783096Z 
2024-11-01T17:51:23.0783586Z     In addition, they are only considered close if they have the same
2024-11-01T17:51:23.0784337Z 
2024-11-01T17:51:23.0784954Z     - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2024-11-01T17:51:23.0786122Z     - ``dtype`` (if ``check_dtype`` is ``True``),
2024-11-01T17:51:23.0787159Z     - ``layout`` (if ``check_layout`` is ``True``), and
2024-11-01T17:51:23.0788182Z     - stride (if ``check_stride`` is ``True``).
2024-11-01T17:51:23.0788790Z 
2024-11-01T17:51:23.0789595Z     If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2024-11-01T17:51:23.0790682Z 
2024-11-01T17:51:23.0791696Z     If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2024-11-01T17:51:23.0794316Z     checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2024-11-01T17:51:23.0796222Z     or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2024-11-01T17:51:23.0798119Z     are always checked for equality whereas the values are checked for closeness according to the definition above.
2024-11-01T17:51:23.0799346Z 
2024-11-01T17:51:23.0799993Z     If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2024-11-01T17:51:23.0801730Z     :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2024-11-01T17:51:23.0803053Z     definition above.
2024-11-01T17:51:23.0803387Z 
2024-11-01T17:51:23.0804502Z     ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2024-11-01T17:51:23.0807370Z     :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2024-11-01T17:51:23.0809977Z     have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2024-11-01T17:51:23.0812664Z     or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2024-11-01T17:51:23.0814485Z     their elements are considered close according to the above definition.
2024-11-01T17:51:23.0815366Z 
2024-11-01T17:51:23.0815592Z     .. note::
2024-11-01T17:51:23.0815857Z 
2024-11-01T17:51:23.0816786Z         Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2024-11-01T17:51:23.0819081Z         :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2024-11-01T17:51:23.0820815Z         Python scalars of different types can be checked, but require ``check_dtype=False``.
2024-11-01T17:51:23.0821937Z 
2024-11-01T17:51:23.0822126Z     Args:
2024-11-01T17:51:23.0822702Z         actual (Any): Actual input.
2024-11-01T17:51:23.0823482Z         expected (Any): Expected input.
2024-11-01T17:51:23.0824948Z         allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2024-11-01T17:51:23.0826403Z             are allowed. Otherwise type equality is required.
2024-11-01T17:51:23.0827979Z         rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2024-11-01T17:51:23.0830216Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2024-11-01T17:51:23.0832011Z         atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2024-11-01T17:51:23.0834030Z             values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2024-11-01T17:51:23.0835620Z         equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2024-11-01T17:51:23.0837316Z         check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2024-11-01T17:51:23.0839032Z             :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2024-11-01T17:51:23.0840901Z             :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2024-11-01T17:51:23.0842780Z         check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2024-11-01T17:51:23.0845116Z             check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2024-11-01T17:51:23.0846776Z             :func:`torch.promote_types`) before being compared.
2024-11-01T17:51:23.0848517Z         check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2024-11-01T17:51:23.0850796Z             check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2024-11-01T17:51:23.0852787Z             compared.
2024-11-01T17:51:23.0854114Z         check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2024-11-01T17:51:23.0856345Z         msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2024-11-01T17:51:23.0858633Z             the comparison. Can also passed as callable in which case it will be called with the generated message and
2024-11-01T17:51:23.0859896Z             should return the new message.
2024-11-01T17:51:23.0860405Z 
2024-11-01T17:51:23.0860623Z     Raises:
2024-11-01T17:51:23.0861543Z         ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2024-11-01T17:51:23.0862919Z         ValueError: If only ``rtol`` or ``atol`` is specified.
2024-11-01T17:51:23.0864525Z         AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2024-11-01T17:51:23.0866719Z         AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2024-11-01T17:51:23.0868265Z             different types.
2024-11-01T17:51:23.0870003Z         AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2024-11-01T17:51:23.0872475Z         AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2024-11-01T17:51:23.0874787Z         AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2024-11-01T17:51:23.0876707Z         AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2024-11-01T17:51:23.0878094Z             :attr:`~torch.Tensor.layout`.
2024-11-01T17:51:23.0879092Z         AssertionError: If only one of corresponding tensors is quantized.
2024-11-01T17:51:23.0880842Z         AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2024-11-01T17:51:23.0882999Z         AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2024-11-01T17:51:23.0884446Z             :attr:`~torch.Tensor.device`.
2024-11-01T17:51:23.0885913Z         AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2024-11-01T17:51:23.0888128Z         AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2024-11-01T17:51:23.0890654Z         AssertionError: If the values of corresponding tensors are not close according to the definition above.
2024-11-01T17:51:23.0891918Z 
2024-11-01T17:51:23.0893155Z     The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2024-11-01T17:51:23.0895017Z     ``dtype``'s, the maximum of both tolerances is used.
2024-11-01T17:51:23.0895727Z 
2024-11-01T17:51:23.0896201Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0897202Z     | ``dtype``                 | ``rtol``   | ``atol`` |
2024-11-01T17:51:23.0898062Z     +===========================+============+==========+
2024-11-01T17:51:23.0899065Z     | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2024-11-01T17:51:23.0900004Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0901007Z     | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2024-11-01T17:51:23.0902036Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0903079Z     | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:23.0904067Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0905103Z     | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2024-11-01T17:51:23.0906132Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0907534Z     | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2024-11-01T17:51:23.0908552Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0909930Z     | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:23.0910951Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0911961Z     | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2024-11-01T17:51:23.0912977Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0914128Z     | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:23.0915114Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0916107Z     | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:23.0917119Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0918126Z     | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:23.0919147Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0920165Z     | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:23.0921193Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0922184Z     | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:23.0923171Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0924003Z     | other                     | ``0.0``    | ``0.0``  |
2024-11-01T17:51:23.0924934Z     +---------------------------+------------+----------+
2024-11-01T17:51:23.0925492Z 
2024-11-01T17:51:23.0925718Z     .. note::
2024-11-01T17:51:23.0926004Z 
2024-11-01T17:51:23.0926964Z         :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2024-11-01T17:51:23.0929024Z         to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2024-11-01T17:51:23.0931218Z         define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2024-11-01T17:51:23.0932417Z 
2024-11-01T17:51:23.0932601Z         >>> import functools
2024-11-01T17:51:23.0933597Z         >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2024-11-01T17:51:23.0934869Z         >>> assert_equal(1e-9, 1e-10)
2024-11-01T17:51:23.0935558Z         Traceback (most recent call last):
2024-11-01T17:51:23.0936205Z         ...
2024-11-01T17:51:23.0936729Z         AssertionError: Scalars are not equal!
2024-11-01T17:51:23.0937481Z         <BLANKLINE>
2024-11-01T17:51:23.0938596Z         Expected 1e-10 but got 1e-09.
2024-11-01T17:51:23.0939460Z         Absolute difference: 9.000000000000001e-10
2024-11-01T17:51:23.0940212Z         Relative difference: 9.0
2024-11-01T17:51:23.0940655Z 
2024-11-01T17:51:23.0940833Z     Examples:
2024-11-01T17:51:23.0941376Z         >>> # tensor to tensor comparison
2024-11-01T17:51:23.0942256Z         >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2024-11-01T17:51:23.0943128Z         >>> actual = torch.acos(torch.cos(expected))
2024-11-01T17:51:23.0943999Z         >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:23.0944559Z 
2024-11-01T17:51:23.0944870Z         >>> # scalar to scalar comparison
2024-11-01T17:51:23.0945730Z         >>> import math
2024-11-01T17:51:23.0946313Z         >>> expected = math.sqrt(2.0)
2024-11-01T17:51:23.0947031Z         >>> actual = 2.0 / math.sqrt(2.0)
2024-11-01T17:51:23.0947916Z         >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:23.0948598Z 
2024-11-01T17:51:23.0948950Z         >>> # numpy array to numpy array comparison
2024-11-01T17:51:23.0949789Z         >>> import numpy as np
2024-11-01T17:51:23.0950728Z         >>> expected = np.array([1e0, 1e-1, 1e-2])
2024-11-01T17:51:23.0951652Z         >>> actual = np.arccos(np.cos(expected))
2024-11-01T17:51:23.0952640Z         >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:23.0953304Z 
2024-11-01T17:51:23.0953608Z         >>> # sequence to sequence comparison
2024-11-01T17:51:23.0954599Z         >>> import numpy as np
2024-11-01T17:51:23.0955874Z         >>> # The types of the sequences do not have to match. They only have to have the same
2024-11-01T17:51:23.0957099Z         >>> # length and their elements have to match.
2024-11-01T17:51:23.0957984Z         >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2024-11-01T17:51:23.0958845Z         >>> actual = tuple(expected)
2024-11-01T17:51:23.0959642Z         >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:23.0960241Z 
2024-11-01T17:51:23.0960540Z         >>> # mapping to mapping comparison
2024-11-01T17:51:23.0961390Z         >>> from collections import OrderedDict
2024-11-01T17:51:23.0962239Z         >>> import numpy as np
2024-11-01T17:51:23.0962949Z         >>> foo = torch.tensor(1.0)
2024-11-01T17:51:23.0963677Z         >>> bar = 2.0
2024-11-01T17:51:23.0964283Z         >>> baz = np.array(3.0)
2024-11-01T17:51:23.0965416Z         >>> # The types and a possible ordering of mappings do not have to match. They only
2024-11-01T17:51:23.0967024Z         >>> # have to have the same set of keys and their elements have to match.
2024-11-01T17:51:23.0968478Z         >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2024-11-01T17:51:23.0969662Z         >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2024-11-01T17:51:23.0970719Z         >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:23.0971405Z 
2024-11-01T17:51:23.0971748Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2024-11-01T17:51:23.0972679Z         >>> actual = expected.clone()
2024-11-01T17:51:23.0973693Z         >>> # By default, directly related instances can be compared
2024-11-01T17:51:23.0975063Z         >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2024-11-01T17:51:23.0976517Z         >>> # This check can be made more strict with allow_subclasses=False
2024-11-01T17:51:23.0977568Z         >>> torch.testing.assert_close(
2024-11-01T17:51:23.0978577Z         ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2024-11-01T17:51:23.0979624Z         ... )
2024-11-01T17:51:23.0980247Z         Traceback (most recent call last):
2024-11-01T17:51:23.0981046Z         ...
2024-11-01T17:51:23.0981885Z         TypeError: No comparison pair was able to handle inputs of type
2024-11-01T17:51:23.0983688Z         <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2024-11-01T17:51:23.0985229Z         >>> # If the inputs are not directly related, they are never considered close
2024-11-01T17:51:23.0986974Z         >>> torch.testing.assert_close(actual.numpy(), expected)
2024-11-01T17:51:23.0988072Z         Traceback (most recent call last):
2024-11-01T17:51:23.0988880Z         ...
2024-11-01T17:51:23.0990212Z         TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2024-11-01T17:51:23.0991670Z         and <class 'torch.Tensor'>.
2024-11-01T17:51:23.0992919Z         >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2024-11-01T17:51:23.0994459Z         >>> # their type if check_dtype=False.
2024-11-01T17:51:23.0995259Z         >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2024-11-01T17:51:23.0995814Z 
2024-11-01T17:51:23.0995998Z         >>> # NaN != NaN by default.
2024-11-01T17:51:23.0996743Z         >>> expected = torch.tensor(float("Nan"))
2024-11-01T17:51:23.0997634Z         >>> actual = expected.clone()
2024-11-01T17:51:23.0998486Z         >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:23.0999349Z         Traceback (most recent call last):
2024-11-01T17:51:23.1000020Z         ...
2024-11-01T17:51:23.1000549Z         AssertionError: Scalars are not close!
2024-11-01T17:51:23.1001300Z         <BLANKLINE>
2024-11-01T17:51:23.1001859Z         Expected nan but got nan.
2024-11-01T17:51:23.1002856Z         Absolute difference: nan (up to 1e-05 allowed)
2024-11-01T17:51:23.1003953Z         Relative difference: nan (up to 1.3e-06 allowed)
2024-11-01T17:51:23.1005109Z         >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2024-11-01T17:51:23.1005897Z 
2024-11-01T17:51:23.1006509Z         >>> expected = torch.tensor([1.0, 2.0, 3.0])
2024-11-01T17:51:23.1007655Z         >>> actual = torch.tensor([1.0, 4.0, 5.0])
2024-11-01T17:51:23.1008571Z         >>> # The default error message can be overwritten.
2024-11-01T17:51:23.1009926Z         >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
2024-11-01T17:51:23.1011194Z         Traceback (most recent call last):
2024-11-01T17:51:23.1011936Z         ...
2024-11-01T17:51:23.1012594Z         AssertionError: Argh, the tensors are not close!
2024-11-01T17:51:23.1013867Z         >>> # If msg is a callable, it can be used to augment the generated message with
2024-11-01T17:51:23.1014979Z         >>> # extra information
2024-11-01T17:51:23.1015692Z         >>> torch.testing.assert_close(
2024-11-01T17:51:23.1016730Z         ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2024-11-01T17:51:23.1017718Z         ... )
2024-11-01T17:51:23.1018296Z         Traceback (most recent call last):
2024-11-01T17:51:23.1019031Z         ...
2024-11-01T17:51:23.1019565Z         AssertionError: Header
2024-11-01T17:51:23.1020143Z         <BLANKLINE>
2024-11-01T17:51:23.1020871Z         Tensor-likes are not close!
2024-11-01T17:51:23.1021722Z         <BLANKLINE>
2024-11-01T17:51:23.1022324Z         Mismatched elements: 2 / 3 (66.7%)
2024-11-01T17:51:23.1023519Z         Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2024-11-01T17:51:23.1024973Z         Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2024-11-01T17:51:23.1025946Z         <BLANKLINE>
2024-11-01T17:51:23.1026470Z         Footer
2024-11-01T17:51:23.1026940Z     
2024-11-01T17:51:23.1027938Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:23.1028825Z 
2024-11-01T17:51:24.5881636Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=111.
2024-11-01T17:51:24.5884425Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.5885940Z Register a container-like type as pytree node.
2024-11-01T17:51:24.5886653Z 
2024-11-01T17:51:24.5886859Z     Args:
2024-11-01T17:51:24.5887638Z         cls (type): A Python type to treat as an internal pytree node.
2024-11-01T17:51:24.5889249Z         flatten_fn (callable): A function to be used during flattening, taking an instance of
2024-11-01T17:51:24.5891431Z             ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2024-11-01T17:51:24.5893242Z             recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2024-11-01T17:51:24.5894665Z             passed to the ``unflatten_fn``.
2024-11-01T17:51:24.5895997Z         unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2024-11-01T17:51:24.5897810Z             returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2024-11-01T17:51:24.5899219Z             The function should return an instance of ``cls``.
2024-11-01T17:51:24.5900670Z         serialized_type_name (str, optional): A keyword argument used to specify the fully
2024-11-01T17:51:24.5902145Z             qualified name used when serializing the tree spec.
2024-11-01T17:51:24.5903745Z         to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2024-11-01T17:51:24.5905698Z             to convert the context of the pytree to a custom json dumpable representation. This is
2024-11-01T17:51:24.5907875Z             used for json serialization, which is being used in :mod:`torch.export` right now.
2024-11-01T17:51:24.5909783Z         from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2024-11-01T17:51:24.5911666Z             how to convert the custom json dumpable representation of the context back to the
2024-11-01T17:51:24.5913460Z             original context. This is used for json deserialization, which is being used in
2024-11-01T17:51:24.5915152Z             :mod:`torch.export` right now.
2024-11-01T17:51:24.5915752Z 
2024-11-01T17:51:24.5915988Z     Example::
2024-11-01T17:51:24.5916308Z 
2024-11-01T17:51:24.5916554Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:24.5917390Z         >>> # Registry a Python type with lambda functions
2024-11-01T17:51:24.5918298Z         >>> register_pytree_node(
2024-11-01T17:51:24.5919017Z         ...     set,
2024-11-01T17:51:24.5919715Z         ...     lambda s: (sorted(s), None, None),
2024-11-01T17:51:24.5920676Z         ...     lambda children, _: set(children),
2024-11-01T17:51:24.5921514Z         ... )
2024-11-01T17:51:24.5921995Z     
2024-11-01T17:51:24.5923197Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.5924194Z 
2024-11-01T17:51:24.6495862Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201.
2024-11-01T17:51:24.6498574Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.6499544Z 
2024-11-01T17:51:24.6500183Z Context passed to policy function during selective checkpointing.
2024-11-01T17:51:24.6501072Z 
2024-11-01T17:51:24.6501716Z This class is used to pass relevant metadata to the policy function during
2024-11-01T17:51:24.6503331Z selective checkpointing. The metadata includes whether the current invocation
2024-11-01T17:51:24.6504739Z of the policy function is during recomputation or not.
2024-11-01T17:51:24.6505520Z 
2024-11-01T17:51:24.6505726Z Example:
2024-11-01T17:51:24.6506269Z     >>> # xdoctest: +SKIP(stub)
2024-11-01T17:51:24.6507224Z     >>>
2024-11-01T17:51:24.6507861Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2024-11-01T17:51:24.6508758Z     >>>    print(ctx.is_recompute)
2024-11-01T17:51:24.6509475Z     >>>
2024-11-01T17:51:24.6510396Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2024-11-01T17:51:24.6511459Z     >>>
2024-11-01T17:51:24.6512063Z     >>> out = torch.utils.checkpoint.checkpoint(
2024-11-01T17:51:24.6512856Z     >>>     fn, x, y,
2024-11-01T17:51:24.6513422Z     >>>     use_reentrant=False,
2024-11-01T17:51:24.6514211Z     >>>     context_fn=context_fn,
2024-11-01T17:51:24.6514902Z     >>> )
2024-11-01T17:51:24.6515185Z 
2024-11-01T17:51:24.6516080Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.6517461Z 
2024-11-01T17:51:24.6519353Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335.
2024-11-01T17:51:24.6522106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.6523115Z 
2024-11-01T17:51:24.6523650Z Helper to avoid recomputing certain ops during activation checkpointing.
2024-11-01T17:51:24.6524571Z 
2024-11-01T17:51:24.6525156Z Use this with `torch.utils.checkpoint.checkpoint` to control which
2024-11-01T17:51:24.6526494Z operations are recomputed during the backward pass.
2024-11-01T17:51:24.6527202Z 
2024-11-01T17:51:24.6527422Z Args:
2024-11-01T17:51:24.6528001Z     policy_fn_or_list (Callable or List):
2024-11-01T17:51:24.6529163Z       - If a policy function is provided, it should accept a
2024-11-01T17:51:24.6530525Z         :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2024-11-01T17:51:24.6532067Z         kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2024-11-01T17:51:24.6533409Z         indicating whether the execution of the op should be recomputed or not.
2024-11-01T17:51:24.6534841Z       - If a list of operations is provided, it is equivalent to a policy
2024-11-01T17:51:24.6536178Z         returning `CheckpointPolicy.MUST_SAVE` for the specified
2024-11-01T17:51:24.6537497Z         operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2024-11-01T17:51:24.6538603Z         operations.
2024-11-01T17:51:24.6539900Z     allow_cache_entry_mutation (bool, optional): By default, an error is
2024-11-01T17:51:24.6541348Z         raised if any tensors cached by selective activation checkpoint are
2024-11-01T17:51:24.6542837Z         mutated in order to ensure correctness. If set to `True`, this check
2024-11-01T17:51:24.6543988Z         is disabled.
2024-11-01T17:51:24.6544514Z Returns:
2024-11-01T17:51:24.6545071Z     A tuple of two context managers.
2024-11-01T17:51:24.6545611Z 
2024-11-01T17:51:24.6545790Z Example:
2024-11-01T17:51:24.6546275Z     >>> # xdoctest: +REQUIRES(LINUX)
2024-11-01T17:51:24.6546912Z     >>> import functools
2024-11-01T17:51:24.6547427Z     >>>
2024-11-01T17:51:24.6547940Z     >>> x = torch.rand(10, 10, requires_grad=True)
2024-11-01T17:51:24.6548807Z     >>> y = torch.rand(10, 10, requires_grad=True)
2024-11-01T17:51:24.6549562Z     >>>
2024-11-01T17:51:24.6550018Z     >>> ops_to_save = [
2024-11-01T17:51:24.6550640Z     >>>    torch.ops.aten.mm.default,
2024-11-01T17:51:24.6551365Z     >>> ]
2024-11-01T17:51:24.6551823Z     >>>
2024-11-01T17:51:24.6552383Z     >>> def policy_fn(ctx, op, *args, **kwargs):
2024-11-01T17:51:24.6553190Z     >>>    if op in ops_to_save:
2024-11-01T17:51:24.6554045Z     >>>        return CheckpointPolicy.MUST_SAVE
2024-11-01T17:51:24.6554825Z     >>>    else:
2024-11-01T17:51:24.6555465Z     >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2024-11-01T17:51:24.6556263Z     >>>
2024-11-01T17:51:24.6557145Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2024-11-01T17:51:24.6558299Z     >>>
2024-11-01T17:51:24.6558781Z     >>> # or equivalently
2024-11-01T17:51:24.6559835Z     >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2024-11-01T17:51:24.6560977Z     >>>
2024-11-01T17:51:24.6561445Z     >>> def fn(x, y):
2024-11-01T17:51:24.6572911Z     >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2024-11-01T17:51:24.6574167Z     >>>
2024-11-01T17:51:24.6574836Z     >>> out = torch.utils.checkpoint.checkpoint(
2024-11-01T17:51:24.6575756Z     >>>     fn, x, y,
2024-11-01T17:51:24.6576261Z     >>>     use_reentrant=False,
2024-11-01T17:51:24.6576858Z     >>>     context_fn=context_fn,
2024-11-01T17:51:24.6577545Z     >>> )
2024-11-01T17:51:24.6577835Z 
2024-11-01T17:51:24.6578824Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.6580110Z 
2024-11-01T17:51:24.6796305Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=957.
2024-11-01T17:51:24.6798834Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.6799826Z 
2024-11-01T17:51:24.6800268Z Create a :class:`setuptools.Extension` for C++.
2024-11-01T17:51:24.6800978Z 
2024-11-01T17:51:24.6801590Z Convenience method that creates a :class:`setuptools.Extension` with the
2024-11-01T17:51:24.6803119Z bare minimum (but often sufficient) arguments to build a C++ extension.
2024-11-01T17:51:24.6804063Z 
2024-11-01T17:51:24.6804571Z All arguments are forwarded to the :class:`setuptools.Extension`
2024-11-01T17:51:24.6805766Z constructor. Full list arguments can be found at
2024-11-01T17:51:24.6807891Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2024-11-01T17:51:24.6809129Z 
2024-11-01T17:51:24.6809345Z Example:
2024-11-01T17:51:24.6809904Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:24.6810716Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:24.6811707Z     >>> from setuptools import setup
2024-11-01T17:51:24.6812890Z     >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2024-11-01T17:51:24.6814038Z     >>> setup(
2024-11-01T17:51:24.6814655Z     ...     name='extension',
2024-11-01T17:51:24.6815315Z     ...     ext_modules=[
2024-11-01T17:51:24.6815932Z     ...         CppExtension(
2024-11-01T17:51:24.6816736Z     ...             name='extension',
2024-11-01T17:51:24.6818083Z     ...             sources=['extension.cpp'],
2024-11-01T17:51:24.6819144Z     ...             extra_compile_args=['-g'],
2024-11-01T17:51:24.6820234Z     ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2024-11-01T17:51:24.6821130Z     ...     ],
2024-11-01T17:51:24.6821598Z     ...     cmdclass={
2024-11-01T17:51:24.6822366Z     ...         'build_ext': BuildExtension
2024-11-01T17:51:24.6823159Z     ...     })
2024-11-01T17:51:24.6823484Z 
2024-11-01T17:51:24.6824323Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.6825342Z 
2024-11-01T17:51:24.6827284Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1008.
2024-11-01T17:51:24.6829912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.6830882Z 
2024-11-01T17:51:24.6831288Z Create a :class:`setuptools.Extension` for CUDA/C++.
2024-11-01T17:51:24.6832011Z 
2024-11-01T17:51:24.6832644Z Convenience method that creates a :class:`setuptools.Extension` with the
2024-11-01T17:51:24.6834263Z bare minimum (but often sufficient) arguments to build a CUDA/C++
2024-11-01T17:51:24.6835744Z extension. This includes the CUDA include path, library path and runtime
2024-11-01T17:51:24.6836859Z library.
2024-11-01T17:51:24.6837178Z 
2024-11-01T17:51:24.6837637Z All arguments are forwarded to the :class:`setuptools.Extension`
2024-11-01T17:51:24.6838840Z constructor. Full list arguments can be found at
2024-11-01T17:51:24.6840605Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2024-11-01T17:51:24.6841796Z 
2024-11-01T17:51:24.6841992Z Example:
2024-11-01T17:51:24.6842537Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:24.6843356Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:24.6844340Z     >>> from setuptools import setup
2024-11-01T17:51:24.6845473Z     >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2024-11-01T17:51:24.6846464Z     >>> setup(
2024-11-01T17:51:24.6847080Z     ...     name='cuda_extension',
2024-11-01T17:51:24.6847756Z     ...     ext_modules=[
2024-11-01T17:51:24.6848340Z     ...         CUDAExtension(
2024-11-01T17:51:24.6849131Z     ...                 name='cuda_extension',
2024-11-01T17:51:24.6850226Z     ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2024-11-01T17:51:24.6851700Z     ...                 extra_compile_args={'cxx': ['-g'],
2024-11-01T17:51:24.6852705Z     ...                                     'nvcc': ['-O2']},
2024-11-01T17:51:24.6853863Z     ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2024-11-01T17:51:24.6854773Z     ...     ],
2024-11-01T17:51:24.6855262Z     ...     cmdclass={
2024-11-01T17:51:24.6855990Z     ...         'build_ext': BuildExtension
2024-11-01T17:51:24.6856770Z     ...     })
2024-11-01T17:51:24.6857116Z 
2024-11-01T17:51:24.6857324Z Compute capabilities:
2024-11-01T17:51:24.6857738Z 
2024-11-01T17:51:24.6858577Z By default the extension will be compiled to run on all archs of the cards visible during the
2024-11-01T17:51:24.6860471Z building process of the extension, plus PTX. If down the road a new card is installed the
2024-11-01T17:51:24.6862540Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2024-11-01T17:51:24.6864791Z newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch
2024-11-01T17:51:24.6866806Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2024-11-01T17:51:24.6868207Z support (see below for details on PTX).
2024-11-01T17:51:24.6868802Z 
2024-11-01T17:51:24.6869648Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2024-11-01T17:51:24.6871146Z CCs you want the extension to support:
2024-11-01T17:51:24.6871723Z 
2024-11-01T17:51:24.6872202Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2024-11-01T17:51:24.6873816Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2024-11-01T17:51:24.6874962Z 
2024-11-01T17:51:24.6875845Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2024-11-01T17:51:24.6878134Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2024-11-01T17:51:24.6880416Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2024-11-01T17:51:24.6882579Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2024-11-01T17:51:24.6884709Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2024-11-01T17:51:24.6886807Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2024-11-01T17:51:24.6888805Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2024-11-01T17:51:24.6891115Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2024-11-01T17:51:24.6892546Z "8.0 8.6" would be better.
2024-11-01T17:51:24.6892982Z 
2024-11-01T17:51:24.6893957Z Note that while it's possible to include all supported archs, the more archs get included the
2024-11-01T17:51:24.6895821Z slower the building process will be, as it will build a separate kernel image for each arch.
2024-11-01T17:51:24.6896936Z 
2024-11-01T17:51:24.6898070Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2024-11-01T17:51:24.6899914Z To workaround the issue, move python binding logic to pure C++ file.
2024-11-01T17:51:24.6900748Z 
2024-11-01T17:51:24.6900972Z Example use:
2024-11-01T17:51:24.6901523Z     #include <ATen/ATen.h>
2024-11-01T17:51:24.6902330Z     at::Tensor SigmoidAlphaBlendForwardCuda(....)
2024-11-01T17:51:24.6903041Z 
2024-11-01T17:51:24.6903239Z Instead of:
2024-11-01T17:51:24.6903791Z     #include <torch/extension.h>
2024-11-01T17:51:24.6904684Z     torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2024-11-01T17:51:24.6905396Z 
2024-11-01T17:51:24.6906091Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2024-11-01T17:51:24.6908661Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2024-11-01T17:51:24.6910609Z 
2024-11-01T17:51:24.6910884Z Relocatable device code linking:
2024-11-01T17:51:24.6911423Z 
2024-11-01T17:51:24.6912189Z If you want to reference device symbols across compilation units (across object files),
2024-11-01T17:51:24.6914327Z the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2024-11-01T17:51:24.6916186Z An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2024-11-01T17:51:24.6918448Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2024-11-01T17:51:24.6920774Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2024-11-01T17:51:24.6922481Z help reduce the protentional perf degradation of `-rdc`.
2024-11-01T17:51:24.6923680Z Note that it needs to be used at both steps to be useful.
2024-11-01T17:51:24.6924455Z 
2024-11-01T17:51:24.6925742Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2024-11-01T17:51:24.6927760Z There is also a case where `-dlink` is used without `-rdc`:
2024-11-01T17:51:24.6929411Z when an extension is linked against a static lib containing rdc-compiled objects
2024-11-01T17:51:24.6930993Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2024-11-01T17:51:24.6931807Z 
2024-11-01T17:51:24.6932336Z Note: Ninja is required to build a CUDA Extension with RDC linking.
2024-11-01T17:51:24.6933163Z 
2024-11-01T17:51:24.6933347Z Example:
2024-11-01T17:51:24.6933849Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:24.6934846Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:24.6935719Z     >>> CUDAExtension(
2024-11-01T17:51:24.6936455Z     ...        name='cuda_extension',
2024-11-01T17:51:24.6937453Z     ...        sources=['extension.cpp', 'extension_kernel.cu'],
2024-11-01T17:51:24.6938435Z     ...        dlink=True,
2024-11-01T17:51:24.6939130Z     ...        dlink_libraries=["dlink_lib"],
2024-11-01T17:51:24.6940149Z     ...        extra_compile_args={'cxx': ['-g'],
2024-11-01T17:51:24.6941242Z     ...                            'nvcc': ['-O2', '-rdc=true']})
2024-11-01T17:51:24.6941905Z 
2024-11-01T17:51:24.6942687Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.6943626Z 
2024-11-01T17:51:24.6945202Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1278.
2024-11-01T17:51:24.6947588Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.6948585Z 
2024-11-01T17:51:24.6949068Z Load a PyTorch C++ extension just-in-time (JIT).
2024-11-01T17:51:24.6949712Z 
2024-11-01T17:51:24.6950235Z To load an extension, a Ninja build file is emitted, which is used to
2024-11-01T17:51:24.6951598Z compile the given sources into a dynamic library. This library is
2024-11-01T17:51:24.6953024Z subsequently loaded into the current Python process as a module and
2024-11-01T17:51:24.6954329Z returned from this function, ready for use.
2024-11-01T17:51:24.6954963Z 
2024-11-01T17:51:24.6955505Z By default, the directory to which the build file is emitted and the
2024-11-01T17:51:24.6956851Z resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2024-11-01T17:51:24.6958268Z ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2024-11-01T17:51:24.6959749Z the name of the extension. This location can be overridden in two ways.
2024-11-01T17:51:24.6961256Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2024-11-01T17:51:24.6962739Z replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2024-11-01T17:51:24.6964212Z into subfolders of this directory. Second, if the ``build_directory``
2024-11-01T17:51:24.6965746Z argument to this function is supplied, it overrides the entire path, i.e.
2024-11-01T17:51:24.6967143Z the library will be compiled into that folder directly.
2024-11-01T17:51:24.6968109Z 
2024-11-01T17:51:24.6968671Z To compile the sources, the default system compiler (``c++``) is used,
2024-11-01T17:51:24.6970268Z which can be overridden by setting the ``CXX`` environment variable. To pass
2024-11-01T17:51:24.6971856Z additional arguments to the compilation process, ``extra_cflags`` or
2024-11-01T17:51:24.6973365Z ``extra_ldflags`` can be provided. For example, to compile your extension
2024-11-01T17:51:24.6975004Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2024-11-01T17:51:24.6976270Z ``extra_cflags`` to pass further include directories.
2024-11-01T17:51:24.6977012Z 
2024-11-01T17:51:24.6977637Z CUDA support with mixed compilation is provided. Simply pass CUDA source
2024-11-01T17:51:24.6979001Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2024-11-01T17:51:24.6980474Z detected and compiled with nvcc rather than the C++ compiler. This includes
2024-11-01T17:51:24.6982054Z passing the CUDA lib64 directory as a library directory, and linking
2024-11-01T17:51:24.6983363Z ``cudart``. You can pass additional flags to nvcc via
2024-11-01T17:51:24.6984627Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2024-11-01T17:51:24.6986133Z heuristics for finding the CUDA install directory are used, which usually
2024-11-01T17:51:24.6987694Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2024-11-01T17:51:24.6988827Z safest option.
2024-11-01T17:51:24.6989166Z 
2024-11-01T17:51:24.6989359Z Args:
2024-11-01T17:51:24.6990226Z     name: The name of the extension to build. This MUST be the same as the
2024-11-01T17:51:24.6991596Z         name of the pybind11 module!
2024-11-01T17:51:24.6992719Z     sources: A list of relative or absolute paths to C++ source files.
2024-11-01T17:51:24.6994263Z     extra_cflags: optional list of compiler flags to forward to the build.
2024-11-01T17:51:24.6995653Z     extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2024-11-01T17:51:24.6996784Z         when building CUDA sources.
2024-11-01T17:51:24.6997909Z     extra_ldflags: optional list of linker flags to forward to the build.
2024-11-01T17:51:24.6999408Z     extra_include_paths: optional list of include directories to forward
2024-11-01T17:51:24.7000498Z         to the build.
2024-11-01T17:51:24.7001347Z     build_directory: optional path to use as build workspace.
2024-11-01T17:51:24.7002606Z     verbose: If ``True``, turns on verbose logging of load steps.
2024-11-01T17:51:24.7003978Z     with_cuda: Determines whether CUDA headers and libraries are added to
2024-11-01T17:51:24.7005303Z         the build. If set to ``None`` (default), this value is
2024-11-01T17:51:24.7006872Z         automatically determined based on the existence of ``.cu`` or
2024-11-01T17:51:24.7008226Z         ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2024-11-01T17:51:24.7009329Z         and libraries to be included.
2024-11-01T17:51:24.7010402Z     is_python_module: If ``True`` (default), imports the produced shared
2024-11-01T17:51:24.7011783Z         library as a Python module. If ``False``, behavior depends on
2024-11-01T17:51:24.7012835Z         ``is_standalone``.
2024-11-01T17:51:24.7013841Z     is_standalone: If ``False`` (default) loads the constructed extension
2024-11-01T17:51:24.7015297Z         into the process as a plain dynamic library. If ``True``, build a
2024-11-01T17:51:24.7016428Z         standalone executable.
2024-11-01T17:51:24.7016892Z 
2024-11-01T17:51:24.7017102Z Returns:
2024-11-01T17:51:24.7017631Z     If ``is_python_module`` is ``True``:
2024-11-01T17:51:24.7018685Z         Returns the loaded PyTorch extension as a Python module.
2024-11-01T17:51:24.7019445Z 
2024-11-01T17:51:24.7019972Z     If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2024-11-01T17:51:24.7021425Z         Returns nothing. (The shared library is loaded into the process as
2024-11-01T17:51:24.7022559Z         a side effect.)
2024-11-01T17:51:24.7022962Z 
2024-11-01T17:51:24.7023497Z     If ``is_standalone`` is ``True``.
2024-11-01T17:51:24.7024546Z         Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2024-11-01T17:51:24.7025896Z         added to the PATH environment variable as a side effect.)
2024-11-01T17:51:24.7026714Z 
2024-11-01T17:51:24.7026914Z Example:
2024-11-01T17:51:24.7027442Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:24.7028188Z     >>> from torch.utils.cpp_extension import load
2024-11-01T17:51:24.7029040Z     >>> module = load(
2024-11-01T17:51:24.7029760Z     ...     name='extension',
2024-11-01T17:51:24.7030748Z     ...     sources=['extension.cpp', 'extension_kernel.cu'],
2024-11-01T17:51:24.7031813Z     ...     extra_cflags=['-O2'],
2024-11-01T17:51:24.7032534Z     ...     verbose=True)
2024-11-01T17:51:24.7032958Z 
2024-11-01T17:51:24.7033783Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.7034861Z 
2024-11-01T17:51:24.7036687Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1567.
2024-11-01T17:51:24.7039394Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.7040433Z 
2024-11-01T17:51:24.7041180Z Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2024-11-01T17:51:24.7042081Z 
2024-11-01T17:51:24.7042697Z This function behaves exactly like :func:`load`, but takes its sources as
2024-11-01T17:51:24.7044272Z strings rather than filenames. These strings are stored to files in the
2024-11-01T17:51:24.7046028Z build directory, after which the behavior of :func:`load_inline` is
2024-11-01T17:51:24.7047179Z identical to :func:`load`.
2024-11-01T17:51:24.7047633Z 
2024-11-01T17:51:24.7047843Z See `the
2024-11-01T17:51:24.7049013Z tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2024-11-01T17:51:24.7050468Z for good examples of using this function.
2024-11-01T17:51:24.7051092Z 
2024-11-01T17:51:24.7051922Z Sources may omit two required parts of a typical non-inline C++ extension:
2024-11-01T17:51:24.7053384Z the necessary header includes, as well as the (pybind11) binding code. More
2024-11-01T17:51:24.7054945Z precisely, strings passed to ``cpp_sources`` are first concatenated into a
2024-11-01T17:51:24.7056410Z single ``.cpp`` file. This file is then prepended with ``#include
2024-11-01T17:51:24.7057478Z <torch/extension.h>``.
2024-11-01T17:51:24.7057891Z 
2024-11-01T17:51:24.7058501Z Furthermore, if the ``functions`` argument is supplied, bindings will be
2024-11-01T17:51:24.7059969Z automatically generated for each function specified. ``functions`` can
2024-11-01T17:51:24.7061402Z either be a list of function names, or a dictionary mapping from function
2024-11-01T17:51:24.7062910Z names to docstrings. If a list is given, the name of each function is used
2024-11-01T17:51:24.7064084Z as its docstring.
2024-11-01T17:51:24.7064453Z 
2024-11-01T17:51:24.7065060Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2024-11-01T17:51:24.7066451Z file and  prepended with ``torch/types.h``, ``cuda.h`` and
2024-11-01T17:51:24.7067768Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2024-11-01T17:51:24.7069261Z separately, but ultimately linked into a single library. Note that no
2024-11-01T17:51:24.7070800Z bindings are generated for functions in ``cuda_sources`` per  se. To bind
2024-11-01T17:51:24.7072390Z to a CUDA kernel, you must create a C++ function that calls it, and either
2024-11-01T17:51:24.7074012Z declare or define this C++ function in one of the ``cpp_sources`` (and
2024-11-01T17:51:24.7075197Z include its name in ``functions``).
2024-11-01T17:51:24.7075762Z 
2024-11-01T17:51:24.7076231Z See :func:`load` for a description of arguments omitted below.
2024-11-01T17:51:24.7077006Z 
2024-11-01T17:51:24.7077202Z Args:
2024-11-01T17:51:24.7078080Z     cpp_sources: A string, or list of strings, containing C++ source code.
2024-11-01T17:51:24.7079616Z     cuda_sources: A string, or list of strings, containing CUDA source code.
2024-11-01T17:51:24.7081278Z     functions: A list of function names for which to generate function
2024-11-01T17:51:24.7082633Z         bindings. If a dictionary is given, it should map function names to
2024-11-01T17:51:24.7083982Z         docstrings (which are otherwise just the function names).
2024-11-01T17:51:24.7085296Z     with_cuda: Determines whether CUDA headers and libraries are added to
2024-11-01T17:51:24.7086636Z         the build. If set to ``None`` (default), this value is
2024-11-01T17:51:24.7087934Z         automatically determined based on whether ``cuda_sources`` is
2024-11-01T17:51:24.7089192Z         provided. Set it to ``True`` to force CUDA headers
2024-11-01T17:51:24.7090210Z         and libraries to be included.
2024-11-01T17:51:24.7091297Z     with_pytorch_error_handling: Determines whether pytorch error and
2024-11-01T17:51:24.7092737Z         warning macros are handled by pytorch instead of pybind. To do
2024-11-01T17:51:24.7094225Z         this, each function ``foo`` is called via an intermediary ``_safe_foo``
2024-11-01T17:51:24.7095619Z         function. This redirection might cause issues in obscure cases
2024-11-01T17:51:24.7096911Z         of cpp. This flag should be set to ``False`` when this redirect
2024-11-01T17:51:24.7097927Z         causes issues.
2024-11-01T17:51:24.7098330Z 
2024-11-01T17:51:24.7098525Z Example:
2024-11-01T17:51:24.7099195Z     >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:24.7100323Z     >>> from torch.utils.cpp_extension import load_inline
2024-11-01T17:51:24.7101255Z     >>> source = """
2024-11-01T17:51:24.7102262Z     at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2024-11-01T17:51:24.7103155Z       return x.sin() + y.sin();
2024-11-01T17:51:24.7103803Z     }
2024-11-01T17:51:24.7104237Z     """
2024-11-01T17:51:24.7105056Z     >>> module = load_inline(name='inline_extension',
2024-11-01T17:51:24.7106023Z     ...                      cpp_sources=[source],
2024-11-01T17:51:24.7107389Z     ...                      functions=['sin_add'])
2024-11-01T17:51:24.7108041Z 
2024-11-01T17:51:24.7108260Z .. note::
2024-11-01T17:51:24.7109111Z     By default, the Ninja backend uses #CPUS + 2 workers to build the
2024-11-01T17:51:24.7110519Z     extension. This may use up too many resources on some systems. One
2024-11-01T17:51:24.7112017Z     can control the number of workers by setting the `MAX_JOBS` environment
2024-11-01T17:51:24.7113356Z     variable to a non-negative number.
2024-11-01T17:51:24.7114025Z 
2024-11-01T17:51:24.7114813Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.7115795Z 
2024-11-01T17:51:24.7146558Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61.
2024-11-01T17:51:24.7149273Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.7150306Z 
2024-11-01T17:51:24.7151213Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2024-11-01T17:51:24.7152372Z 
2024-11-01T17:51:24.7153133Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2024-11-01T17:51:24.7155040Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2024-11-01T17:51:24.7156673Z server like load. It can emulate multiple calling threads to a single module
2024-11-01T17:51:24.7158286Z provided. In the future we plan to enhance this component to support inter and
2024-11-01T17:51:24.7160179Z intra-op parallelism as well as multiple models running in a single process.
2024-11-01T17:51:24.7161199Z 
2024-11-01T17:51:24.7161891Z Please note that even though nn.Module is supported, it might incur an overhead
2024-11-01T17:51:24.7163523Z from the need to hold GIL every time we execute Python code or pass around
2024-11-01T17:51:24.7165116Z inputs as Python objects. As soon as you have a ScriptModule version of your
2024-11-01T17:51:24.7167081Z model for inference deployment it is better to switch to using it in this
2024-11-01T17:51:24.7168203Z benchmark.
2024-11-01T17:51:24.7168501Z 
2024-11-01T17:51:24.7168719Z Example::
2024-11-01T17:51:24.7169003Z 
2024-11-01T17:51:24.7169286Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:24.7170197Z     >>> from torch.utils import ThroughputBenchmark
2024-11-01T17:51:24.7171129Z     >>> bench = ThroughputBenchmark(my_module)
2024-11-01T17:51:24.7172374Z     >>> # Pre-populate benchmark's data set with the inputs
2024-11-01T17:51:24.7173342Z     >>> for input in inputs:
2024-11-01T17:51:24.7174430Z     ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2024-11-01T17:51:24.7175658Z     ...     bench.add_input(input[0], x2=input[1])
2024-11-01T17:51:24.7176816Z     >>> # Inputs supplied above are randomly used during the execution
2024-11-01T17:51:24.7177921Z     >>> stats = bench.benchmark(
2024-11-01T17:51:24.7178661Z     ...     num_calling_threads=4,
2024-11-01T17:51:24.7179420Z     ...     num_warmup_iters = 100,
2024-11-01T17:51:24.7180134Z     ...     num_iters = 1000,
2024-11-01T17:51:24.7180740Z     ... )
2024-11-01T17:51:24.7181464Z     >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2024-11-01T17:51:24.7182636Z     >>> print("Number of iterations: {}".format(stats.num_iters))
2024-11-01T17:51:24.7183324Z 
2024-11-01T17:51:24.7184170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.7185097Z 
2024-11-01T17:51:24.8234436Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17.
2024-11-01T17:51:24.8237220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:24.8238826Z Sampler that restricts data loading to a subset of the dataset.
2024-11-01T17:51:24.8239665Z 
2024-11-01T17:51:24.8240018Z     It is especially useful in conjunction with
2024-11-01T17:51:24.8241289Z     :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2024-11-01T17:51:24.8242917Z     process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2024-11-01T17:51:24.8244512Z     :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2024-11-01T17:51:24.8245740Z     original dataset that is exclusive to it.
2024-11-01T17:51:24.8246345Z 
2024-11-01T17:51:24.8246579Z     .. note::
2024-11-01T17:51:24.8247538Z         Dataset is assumed to be of constant size and that any instance of it always
2024-11-01T17:51:24.8248781Z         returns the same elements in the same order.
2024-11-01T17:51:24.8249474Z 
2024-11-01T17:51:24.8249644Z     Args:
2024-11-01T17:51:24.8250209Z         dataset: Dataset used for sampling.
2024-11-01T17:51:24.8251336Z         num_replicas (int, optional): Number of processes participating in
2024-11-01T17:51:24.8252877Z             distributed training. By default, :attr:`world_size` is retrieved from the
2024-11-01T17:51:24.8254184Z             current distributed group.
2024-11-01T17:51:24.8255305Z         rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2024-11-01T17:51:24.8256716Z             By default, :attr:`rank` is retrieved from the current distributed
2024-11-01T17:51:24.8257682Z             group.
2024-11-01T17:51:24.8258596Z         shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2024-11-01T17:51:24.8259712Z             indices.
2024-11-01T17:51:24.8260566Z         seed (int, optional): random seed used to shuffle the sampler if
2024-11-01T17:51:24.8261962Z             :attr:`shuffle=True`. This number should be identical across all
2024-11-01T17:51:24.8263218Z             processes in the distributed group. Default: ``0``.
2024-11-01T17:51:24.8264560Z         drop_last (bool, optional): if ``True``, then the sampler will drop the
2024-11-01T17:51:24.8265997Z             tail of the data to make it evenly divisible across the number of
2024-11-01T17:51:24.8267872Z             replicas. If ``False``, the sampler will add extra indices to make
2024-11-01T17:51:24.8269285Z             the data evenly divisible across the replicas. Default: ``False``.
2024-11-01T17:51:24.8270180Z 
2024-11-01T17:51:24.8270392Z     .. warning::
2024-11-01T17:51:24.8271204Z         In distributed mode, calling the :meth:`set_epoch` method at
2024-11-01T17:51:24.8272578Z         the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2024-11-01T17:51:24.8274365Z         is necessary to make shuffling work properly across multiple epochs. Otherwise,
2024-11-01T17:51:24.8275684Z         the same ordering will be always used.
2024-11-01T17:51:24.8276281Z 
2024-11-01T17:51:24.8276498Z     Example::
2024-11-01T17:51:24.8276799Z 
2024-11-01T17:51:24.8277035Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:24.8278086Z         >>> sampler = DistributedSampler(dataset) if is_distributed else None
2024-11-01T17:51:24.8279455Z         >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2024-11-01T17:51:24.8280465Z         ...                     sampler=sampler)
2024-11-01T17:51:24.8281344Z         >>> for epoch in range(start_epoch, n_epochs):
2024-11-01T17:51:24.8282143Z         ...     if is_distributed:
2024-11-01T17:51:24.8282853Z         ...         sampler.set_epoch(epoch)
2024-11-01T17:51:24.8283637Z         ...     train(loader)
2024-11-01T17:51:24.8284263Z     
2024-11-01T17:51:24.8285546Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:24.8286500Z 
2024-11-01T17:51:25.0433833Z gathering tests
2024-11-01T17:51:25.0449824Z running 704 test(s)
2024-11-01T17:51:25.0469495Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0, line 1026 <- wrt source file
2024-11-01T17:51:25.0479154Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::typename:0
2024-11-01T17:51:25.0481834Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0, line 1062 <- wrt source file
2024-11-01T17:51:25.0486180Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::is_tensor:0
2024-11-01T17:51:25.0488401Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0, line 1131 <- wrt source file
2024-11-01T17:51:25.0490159Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_device:0
2024-11-01T17:51:25.0492021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1180 <- wrt source file
2024-11-01T17:51:25.0494192Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_tensor_type:0
2024-11-01T17:51:25.0496228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0, line 1217 <- wrt source file
2024-11-01T17:51:25.0498053Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::set_default_dtype:0
2024-11-01T17:51:25.0500354Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1372 <- wrt source file
2024-11-01T17:51:25.0503043Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::use_deterministic_algorithms:0
2024-11-01T17:51:25.0505032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0, line 2464 <- wrt source file
2024-11-01T17:51:25.0506901Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::compile:0
2024-11-01T17:51:25.0508695Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2720 <- wrt source file
2024-11-01T17:51:25.0510940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0
2024-11-01T17:51:25.0512909Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file
2024-11-01T17:51:25.0514917Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::Generator:0
2024-11-01T17:51:25.0516871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file
2024-11-01T17:51:25.0518814Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_C.cpython-312-x86_64-linux-gnu.so::_LinAlgError:0
2024-11-01T17:51:25.0520613Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file
2024-11-01T17:51:25.0522237Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::custom_op:0
2024-11-01T17:51:25.0523840Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file
2024-11-01T17:51:25.0525412Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl:0
2024-11-01T17:51:25.0527200Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file
2024-11-01T17:51:25.1220937Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_custom_ops.py::impl_abstract:0
2024-11-01T17:51:25.1222717Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file
2024-11-01T17:51:25.1224543Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_namedtensor_internals.py::update_names:0
2024-11-01T17:51:25.1226311Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 648 <- wrt source file
2024-11-01T17:51:25.1230331Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_hook:0
2024-11-01T17:51:25.1232223Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 705 <- wrt source file
2024-11-01T17:51:25.1251122Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0
2024-11-01T17:51:25.1252990Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1334 <- wrt source file
2024-11-01T17:51:25.1376290Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.refine_names:0
2024-11-01T17:51:25.1379855Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1379 <- wrt source file
2024-11-01T17:51:25.1386735Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.align_to:0
2024-11-01T17:51:25.1388433Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0, line 1452 <- wrt source file
2024-11-01T17:51:25.1395229Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.rename:0
2024-11-01T17:51:25.1396940Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1482 <- wrt source file
2024-11-01T17:51:25.1402117Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0
2024-11-01T17:51:25.1404032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0, line 1505 <- wrt source file
2024-11-01T17:51:25.1406889Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py::Tensor.dim_order:0
2024-11-01T17:51:25.1408604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file
2024-11-01T17:51:25.1431168Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor_str.py::set_printoptions:0
2024-11-01T17:51:25.1432903Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0, line 63 <- wrt source file
2024-11-01T17:51:25.1438682Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_tensors:0
2024-11-01T17:51:25.1440431Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0, line 91 <- wrt source file
2024-11-01T17:51:25.1442812Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::broadcast_shapes:0
2024-11-01T17:51:25.1444475Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0, line 178 <- wrt source file
2024-11-01T17:51:25.1456213Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::split:0
2024-11-01T17:51:25.1457831Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0, line 292 <- wrt source file
2024-11-01T17:51:25.1519485Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::einsum:0
2024-11-01T17:51:25.1521246Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1019 <- wrt source file
2024-11-01T17:51:25.1533342Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_unique_consecutive_impl:0
2024-11-01T17:51:25.1535099Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0, line 1294 <- wrt source file
2024-11-01T17:51:25.1547726Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::tensordot:0
2024-11-01T17:51:25.1550966Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0, line 1378 <- wrt source file
2024-11-01T17:51:25.1559002Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cartesian_prod:0
2024-11-01T17:51:25.1562158Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0, line 1412 <- wrt source file
2024-11-01T17:51:25.1572165Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::block_diag:0
2024-11-01T17:51:25.1575032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0, line 1463 <- wrt source file
2024-11-01T17:51:25.1590887Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::cdist:0
2024-11-01T17:51:25.1593820Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0, line 1504 <- wrt source file
2024-11-01T17:51:25.1611585Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_1d:0
2024-11-01T17:51:25.1614548Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0, line 1540 <- wrt source file
2024-11-01T17:51:25.1632803Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_2d:0
2024-11-01T17:51:25.1635912Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0, line 1578 <- wrt source file
2024-11-01T17:51:25.1658856Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::atleast_3d:0
2024-11-01T17:51:25.1661745Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0, line 1751 <- wrt source file
2024-11-01T17:51:25.1695976Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::norm:0
2024-11-01T17:51:25.1698876Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0, line 1918 <- wrt source file
2024-11-01T17:51:25.1728002Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::unravel_index:0
2024-11-01T17:51:25.1731318Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0, line 2018 <- wrt source file
2024-11-01T17:51:25.1734272Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::chain_matmul:0
2024-11-01T17:51:25.1737011Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0, line 2118 <- wrt source file
2024-11-01T17:51:25.1741073Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py::_lu_impl:0
2024-11-01T17:51:25.1744010Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0, line 468 <- wrt source file
2024-11-01T17:51:25.1746462Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::list:0
2024-11-01T17:51:25.1748922Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0, line 528 <- wrt source file
2024-11-01T17:51:25.1751380Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::help:0
2024-11-01T17:51:25.1754061Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file
2024-11-01T17:51:25.1756584Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py::_load_local:0
2024-11-01T17:51:25.1759601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0, line 142 <- wrt source file
2024-11-01T17:51:25.1762575Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.define:0
2024-11-01T17:51:25.1765601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 242 <- wrt source file
2024-11-01T17:51:25.1769165Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library._impl_with_aoti_compile:0
2024-11-01T17:51:25.1772241Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0, line 297 <- wrt source file
2024-11-01T17:51:25.1775130Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::Library.impl:0
2024-11-01T17:51:25.1777718Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0, line 490 <- wrt source file
2024-11-01T17:51:25.1785411Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::define:0
2024-11-01T17:51:25.1788256Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0, line 557 <- wrt source file
2024-11-01T17:51:25.1798070Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::impl:0
2024-11-01T17:51:25.1801171Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0, line 681 <- wrt source file
2024-11-01T17:51:25.1804199Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_kernel:0
2024-11-01T17:51:25.1807550Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0, line 998 <- wrt source file
2024-11-01T17:51:25.1964120Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_torch_dispatch:0
2024-11-01T17:51:25.1967059Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0, line 1087 <- wrt source file
2024-11-01T17:51:25.2159963Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py::register_vmap:0
2024-11-01T17:51:25.2163021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0, line 111 <- wrt source file
2024-11-01T17:51:25.2169390Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_ignored_functions:0
2024-11-01T17:51:25.2172219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0, line 416 <- wrt source file
2024-11-01T17:51:25.2212636Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::get_testing_overrides:0
2024-11-01T17:51:25.2215988Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0, line 1569 <- wrt source file
2024-11-01T17:51:25.2219105Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::wrap_torch_function:0
2024-11-01T17:51:25.2222277Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0, line 1704 <- wrt source file
2024-11-01T17:51:25.2225430Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::handle_torch_function:0
2024-11-01T17:51:25.2228470Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1952 <- wrt source file
2024-11-01T17:51:25.2260569Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_method_or_property:0
2024-11-01T17:51:25.2263555Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0, line 1971 <- wrt source file
2024-11-01T17:51:25.2271152Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/overrides.py::is_tensor_like:0
2024-11-01T17:51:25.2274536Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file
2024-11-01T17:51:25.2277503Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/quasirandom.py::SobolEngine:0
2024-11-01T17:51:25.2280559Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0, line 276 <- wrt source file
2024-11-01T17:51:25.2283774Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::add_safe_globals:0
2024-11-01T17:51:25.2286893Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0, line 301 <- wrt source file
2024-11-01T17:51:25.2290048Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::safe_globals:0
2024-11-01T17:51:25.2293733Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0, line 333 <- wrt source file
2024-11-01T17:51:25.2296862Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::skip_data:0
2024-11-01T17:51:25.2300120Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0, line 405 <- wrt source file
2024-11-01T17:51:25.2303325Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::register_package:0
2024-11-01T17:51:25.2306259Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0, line 868 <- wrt source file
2024-11-01T17:51:25.2309444Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py::save:0
2024-11-01T17:51:25.2312477Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0, line 18 <- wrt source file
2024-11-01T17:51:25.2315665Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/torch_version.py::TorchVersion:0
2024-11-01T17:51:25.2318684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 230 <- wrt source file
2024-11-01T17:51:25.2321945Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_mode_options:0
2024-11-01T17:51:25.2325553Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0, line 262 <- wrt source file
2024-11-01T17:51:25.2328551Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/__init__.py::list_options:0
2024-11-01T17:51:25.2331817Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1746 <- wrt source file
2024-11-01T17:51:25.2335066Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0
2024-11-01T17:51:25.2338448Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 45 <- wrt source file
2024-11-01T17:51:25.2341833Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py::current_accelerator:0
2024-11-01T17:51:25.2345102Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 105 <- wrt source file
2024-11-01T17:51:25.2348395Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::allow_in_graph:0
2024-11-01T17:51:25.2352114Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 159 <- wrt source file
2024-11-01T17:51:25.2811460Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::substitute_in_graph:0
2024-11-01T17:51:25.2814727Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 324 <- wrt source file
2024-11-01T17:51:25.2817959Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::wrap_numpy:0
2024-11-01T17:51:25.2821268Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0, line 355 <- wrt source file
2024-11-01T17:51:25.2824512Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_compiling:0
2024-11-01T17:51:25.2828375Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 376 <- wrt source file
2024-11-01T17:51:25.2831632Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0
2024-11-01T17:51:25.2834931Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0, line 409 <- wrt source file
2024-11-01T17:51:25.2837874Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::save:0
2024-11-01T17:51:25.2840975Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0, line 493 <- wrt source file
2024-11-01T17:51:25.2843918Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::load:0
2024-11-01T17:51:25.2847191Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0, line 591 <- wrt source file
2024-11-01T17:51:25.2850611Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/__init__.py::register_dataclass:0
2024-11-01T17:51:25.2854122Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 196 <- wrt source file
2024-11-01T17:51:25.2857343Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.add_done_callback:0
2024-11-01T17:51:25.2860998Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 258 <- wrt source file
2024-11-01T17:51:25.2863823Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::Future.set_exception:0
2024-11-01T17:51:25.2866665Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0, line 289 <- wrt source file
2024-11-01T17:51:25.2869435Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py::collect_all:0
2024-11-01T17:51:25.2872135Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0, line 146 <- wrt source file
2024-11-01T17:51:25.2874794Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/__init__.py::annotate:0
2024-11-01T17:51:25.2877443Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file
2024-11-01T17:51:25.2880391Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0
2024-11-01T17:51:25.2883403Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 57 <- wrt source file
2024-11-01T17:51:25.2890571Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::as_nested_tensor:0
2024-11-01T17:51:25.2893641Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0, line 216 <- wrt source file
2024-11-01T17:51:25.2896883Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor:0
2024-11-01T17:51:25.2899897Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0, line 278 <- wrt source file
2024-11-01T17:51:25.2953666Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::narrow:0
2024-11-01T17:51:25.2957016Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 362 <- wrt source file
2024-11-01T17:51:25.2977179Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0
2024-11-01T17:51:25.2980247Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0, line 426 <- wrt source file
2024-11-01T17:51:25.2998247Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py::masked_select:0
2024-11-01T17:51:25.3001301Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file
2024-11-01T17:51:25.3018343Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0
2024-11-01T17:51:25.3021453Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file
2024-11-01T17:51:25.3085266Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0
2024-11-01T17:51:25.3088717Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 248 <- wrt source file
2024-11-01T17:51:25.3091990Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0
2024-11-01T17:51:25.3096177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 205 <- wrt source file
2024-11-01T17:51:25.3099271Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0
2024-11-01T17:51:25.3102052Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 844 <- wrt source file
2024-11-01T17:51:25.3450581Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py::aot_function:0
2024-11-01T17:51:25.3453780Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0, line 324 <- wrt source file
2024-11-01T17:51:25.3456512Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py::grad:0
2024-11-01T17:51:25.3459635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file
2024-11-01T17:51:25.3463040Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0
2024-11-01T17:51:25.3466607Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 271 <- wrt source file
2024-11-01T17:51:25.3502146Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::vjp:0
2024-11-01T17:51:25.3505508Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 510 <- wrt source file
2024-11-01T17:51:25.3560520Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacrev:0
2024-11-01T17:51:25.3563967Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1064 <- wrt source file
2024-11-01T17:51:25.4569289Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jvp:0
2024-11-01T17:51:25.4572362Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1219 <- wrt source file
2024-11-01T17:51:25.4637314Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0
2024-11-01T17:51:25.4640024Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1384 <- wrt source file
2024-11-01T17:51:25.4657950Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::hessian:0
2024-11-01T17:51:25.4660719Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1548 <- wrt source file
2024-11-01T17:51:25.4663479Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::functionalize:0
2024-11-01T17:51:25.4666262Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1748 <- wrt source file
2024-11-01T17:51:25.4852067Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/eager_transforms.py::linearize:0
2024-11-01T17:51:25.4854845Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file
2024-11-01T17:51:25.4860603Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/functional_call.py::functional_call:0
2024-11-01T17:51:25.4863800Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file
2024-11-01T17:51:25.4866883Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/fx_minifier.py::minifier:0
2024-11-01T17:51:25.4879484Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 111 <- wrt source file
2024-11-01T17:51:25.4882876Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0
2024-11-01T17:51:25.4886066Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 119 <- wrt source file
2024-11-01T17:51:25.4889042Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0
2024-11-01T17:51:25.4892115Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 210 <- wrt source file
2024-11-01T17:51:25.4895214Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0
2024-11-01T17:51:25.4898070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 110 <- wrt source file
2024-11-01T17:51:25.4900564Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/cond.py::cond:0
2024-11-01T17:51:25.4903098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 95 <- wrt source file
2024-11-01T17:51:25.4905606Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/scan.py::scan:0
2024-11-01T17:51:25.4908533Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0, line 95 <- wrt source file
2024-11-01T17:51:25.4911612Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_higher_order_ops/while_loop.py::while_loop:0
2024-11-01T17:51:25.4914631Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1326 <- wrt source file
2024-11-01T17:51:25.4917694Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0
2024-11-01T17:51:25.4920527Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0, line 71 <- wrt source file
2024-11-01T17:51:25.5218058Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::custom_op:0
2024-11-01T17:51:25.5220266Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 200 <- wrt source file
2024-11-01T17:51:25.5302396Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0
2024-11-01T17:51:25.5304448Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 269 <- wrt source file
2024-11-01T17:51:25.5306405Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0
2024-11-01T17:51:25.5309056Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 375 <- wrt source file
2024-11-01T17:51:25.5382680Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0
2024-11-01T17:51:25.5384700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 498 <- wrt source file
2024-11-01T17:51:25.5539912Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0
2024-11-01T17:51:25.5541923Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 672 <- wrt source file
2024-11-01T17:51:25.5693160Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0
2024-11-01T17:51:25.5695208Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 197 <- wrt source file
2024-11-01T17:51:25.5697199Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0
2024-11-01T17:51:25.5699235Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 161 <- wrt source file
2024-11-01T17:51:25.5765451Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0
2024-11-01T17:51:25.5767355Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 45 <- wrt source file
2024-11-01T17:51:25.5771550Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/infer_schema.py::infer_schema:0
2024-11-01T17:51:25.5773849Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0, line 421 <- wrt source file
2024-11-01T17:51:25.5775541Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_logging/_internal.py::set_logs:0
2024-11-01T17:51:25.5777595Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file
2024-11-01T17:51:25.5816161Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_equal:0
2024-11-01T17:51:25.5818034Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 305 <- wrt source file
2024-11-01T17:51:25.5819893Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0
2024-11-01T17:51:25.5821748Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 996 <- wrt source file
2024-11-01T17:51:25.5870870Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0
2024-11-01T17:51:25.5872889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1061 <- wrt source file
2024-11-01T17:51:25.5874969Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0
2024-11-01T17:51:25.5876898Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1282 <- wrt source file
2024-11-01T17:51:25.5891058Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0
2024-11-01T17:51:25.5893056Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1348 <- wrt source file
2024-11-01T17:51:25.5895420Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0
2024-11-01T17:51:25.5897418Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1411 <- wrt source file
2024-11-01T17:51:25.5901264Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0
2024-11-01T17:51:25.5903123Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1456 <- wrt source file
2024-11-01T17:51:25.5904905Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0
2024-11-01T17:51:25.5907326Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1566 <- wrt source file
2024-11-01T17:51:25.5910092Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py::assert_warns:0
2024-11-01T17:51:25.5913361Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 85 <- wrt source file
2024-11-01T17:51:25.5915944Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_prims/context.py::TorchRefsMode:0
2024-11-01T17:51:25.5917697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 60 <- wrt source file
2024-11-01T17:51:25.5919399Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/amp/grad_scaler.py::GradScaler:0
2024-11-01T17:51:25.5921332Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file
2024-11-01T17:51:25.5923425Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0
2024-11-01T17:51:25.5925930Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file
2024-11-01T17:51:25.5928266Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0
2024-11-01T17:51:25.5930553Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file
2024-11-01T17:51:25.5932751Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0
2024-11-01T17:51:25.5935012Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file
2024-11-01T17:51:25.5937292Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0
2024-11-01T17:51:25.5939548Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file
2024-11-01T17:51:25.5941863Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0
2024-11-01T17:51:25.5943933Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 25 <- wrt source file
2024-11-01T17:51:25.5958161Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0
2024-11-01T17:51:25.5960139Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 318 <- wrt source file
2024-11-01T17:51:25.5987775Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0
2024-11-01T17:51:25.5990336Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file
2024-11-01T17:51:25.5993108Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0
2024-11-01T17:51:25.5995067Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file
2024-11-01T17:51:25.5996908Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0
2024-11-01T17:51:25.5999306Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file
2024-11-01T17:51:25.6001117Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0
2024-11-01T17:51:25.6003635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file
2024-11-01T17:51:25.6006474Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0
2024-11-01T17:51:25.6009626Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file
2024-11-01T17:51:25.6012757Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0
2024-11-01T17:51:25.6015520Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file
2024-11-01T17:51:25.6017733Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0
2024-11-01T17:51:25.6020580Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file
2024-11-01T17:51:25.6023106Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0
2024-11-01T17:51:25.6025474Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 208 <- wrt source file
2024-11-01T17:51:25.6027471Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0
2024-11-01T17:51:25.6029749Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 294 <- wrt source file
2024-11-01T17:51:25.6032926Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0
2024-11-01T17:51:25.6036219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 376 <- wrt source file
2024-11-01T17:51:25.6039606Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0
2024-11-01T17:51:25.6042836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 458 <- wrt source file
2024-11-01T17:51:25.6046023Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0
2024-11-01T17:51:25.6049168Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file
2024-11-01T17:51:25.6052171Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0
2024-11-01T17:51:25.6055171Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file
2024-11-01T17:51:25.6058060Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0
2024-11-01T17:51:25.6060969Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file
2024-11-01T17:51:25.6063881Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0
2024-11-01T17:51:25.6066872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file
2024-11-01T17:51:25.6069808Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0
2024-11-01T17:51:25.6072842Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file
2024-11-01T17:51:25.6075901Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0
2024-11-01T17:51:25.6078915Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file
2024-11-01T17:51:25.6082008Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0
2024-11-01T17:51:25.6084953Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file
2024-11-01T17:51:25.6087835Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0
2024-11-01T17:51:25.6090684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 506 <- wrt source file
2024-11-01T17:51:25.6093419Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0
2024-11-01T17:51:25.6096192Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file
2024-11-01T17:51:25.6098931Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0
2024-11-01T17:51:25.6101813Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 891 <- wrt source file
2024-11-01T17:51:25.6104863Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0
2024-11-01T17:51:25.6108095Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1013 <- wrt source file
2024-11-01T17:51:25.6111063Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0
2024-11-01T17:51:25.6114132Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1139 <- wrt source file
2024-11-01T17:51:25.6117096Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0
2024-11-01T17:51:25.6120117Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file
2024-11-01T17:51:25.6123149Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0
2024-11-01T17:51:25.6126249Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 276 <- wrt source file
2024-11-01T17:51:25.6129343Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0
2024-11-01T17:51:25.6132593Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 24 <- wrt source file
2024-11-01T17:51:25.6135866Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0
2024-11-01T17:51:25.6139134Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 177 <- wrt source file
2024-11-01T17:51:25.6142362Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0
2024-11-01T17:51:25.6145413Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file
2024-11-01T17:51:25.6148410Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0
2024-11-01T17:51:25.6151717Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file
2024-11-01T17:51:25.6155561Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0
2024-11-01T17:51:25.6159381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file
2024-11-01T17:51:25.6163215Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0
2024-11-01T17:51:25.6166955Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file
2024-11-01T17:51:25.6170510Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0
2024-11-01T17:51:25.6173976Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file
2024-11-01T17:51:25.6176989Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0
2024-11-01T17:51:25.6180076Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file
2024-11-01T17:51:25.6183212Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0
2024-11-01T17:51:25.6186265Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 178 <- wrt source file
2024-11-01T17:51:25.6189112Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0
2024-11-01T17:51:25.6192125Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file
2024-11-01T17:51:25.6195560Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0
2024-11-01T17:51:25.6198652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file
2024-11-01T17:51:25.6201791Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0
2024-11-01T17:51:25.6204934Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file
2024-11-01T17:51:25.6208282Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0
2024-11-01T17:51:25.6211471Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file
2024-11-01T17:51:25.6214658Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0
2024-11-01T17:51:25.6217875Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 93 <- wrt source file
2024-11-01T17:51:25.6220610Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_args:0
2024-11-01T17:51:25.6223510Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 115 <- wrt source file
2024-11-01T17:51:25.6226431Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0
2024-11-01T17:51:25.6229275Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file
2024-11-01T17:51:25.6231968Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0
2024-11-01T17:51:25.6234891Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file
2024-11-01T17:51:25.6237702Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0
2024-11-01T17:51:25.6240585Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file
2024-11-01T17:51:25.6243557Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0
2024-11-01T17:51:25.6246438Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 595 <- wrt source file
2024-11-01T17:51:25.6249255Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0
2024-11-01T17:51:25.6252196Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 654 <- wrt source file
2024-11-01T17:51:25.6255244Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0
2024-11-01T17:51:25.6258447Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 706 <- wrt source file
2024-11-01T17:51:25.6261696Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0
2024-11-01T17:51:25.6264807Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 49 <- wrt source file
2024-11-01T17:51:25.6267679Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0
2024-11-01T17:51:25.6270635Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 123 <- wrt source file
2024-11-01T17:51:25.6273593Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0
2024-11-01T17:51:25.6276653Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 215 <- wrt source file
2024-11-01T17:51:25.6279527Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0
2024-11-01T17:51:25.6282391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file
2024-11-01T17:51:25.6285320Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0
2024-11-01T17:51:25.6288142Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 517 <- wrt source file
2024-11-01T17:51:25.6290982Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0
2024-11-01T17:51:25.6293852Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 539 <- wrt source file
2024-11-01T17:51:25.6296755Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0
2024-11-01T17:51:25.6299671Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 553 <- wrt source file
2024-11-01T17:51:25.6302500Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0
2024-11-01T17:51:25.6305371Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 575 <- wrt source file
2024-11-01T17:51:25.6309232Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0
2024-11-01T17:51:25.6312071Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 702 <- wrt source file
2024-11-01T17:51:25.6314931Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0
2024-11-01T17:51:25.6318021Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file
2024-11-01T17:51:25.6321307Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0
2024-11-01T17:51:25.6324554Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file
2024-11-01T17:51:25.6327710Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0
2024-11-01T17:51:25.6330953Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file
2024-11-01T17:51:25.6334146Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0
2024-11-01T17:51:25.6337450Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 459 <- wrt source file
2024-11-01T17:51:25.6340837Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0
2024-11-01T17:51:25.6343921Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file
2024-11-01T17:51:25.6346635Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0
2024-11-01T17:51:25.6349316Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 83 <- wrt source file
2024-11-01T17:51:25.6352095Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::make_dual:0
2024-11-01T17:51:25.6354822Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 153 <- wrt source file
2024-11-01T17:51:25.6357448Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::unpack_dual:0
2024-11-01T17:51:25.6360091Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 189 <- wrt source file
2024-11-01T17:51:25.6362676Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/forward_ad.py::dual_level:0
2024-11-01T17:51:25.6365503Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 66 <- wrt source file
2024-11-01T17:51:25.6368479Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0
2024-11-01T17:51:25.6371494Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 109 <- wrt source file
2024-11-01T17:51:25.6374436Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0
2024-11-01T17:51:25.6377548Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 160 <- wrt source file
2024-11-01T17:51:25.6380373Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0
2024-11-01T17:51:25.6383333Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 207 <- wrt source file
2024-11-01T17:51:25.6386385Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0
2024-11-01T17:51:25.6389467Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 236 <- wrt source file
2024-11-01T17:51:25.6392639Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0
2024-11-01T17:51:25.6395562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file
2024-11-01T17:51:25.6398070Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/function.py::Function:0
2024-11-01T17:51:25.6400606Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0, line 294 <- wrt source file
2024-11-01T17:51:25.6403071Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vjp:0
2024-11-01T17:51:25.6405557Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0, line 396 <- wrt source file
2024-11-01T17:51:25.6408263Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jvp:0
2024-11-01T17:51:25.6410824Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0, line 631 <- wrt source file
2024-11-01T17:51:25.6413371Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::jacobian:0
2024-11-01T17:51:25.6415962Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0, line 885 <- wrt source file
2024-11-01T17:51:25.6418669Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hessian:0
2024-11-01T17:51:25.6421236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0, line 1001 <- wrt source file
2024-11-01T17:51:25.6423692Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::vhp:0
2024-11-01T17:51:25.6426230Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0, line 1100 <- wrt source file
2024-11-01T17:51:25.6428663Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/functional.py::hvp:0
2024-11-01T17:51:25.6431161Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file
2024-11-01T17:51:25.6433664Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::no_grad:0
2024-11-01T17:51:25.6436358Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file
2024-11-01T17:51:25.6438938Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::enable_grad:0
2024-11-01T17:51:25.6441775Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file
2024-11-01T17:51:25.6444456Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0
2024-11-01T17:51:25.6447167Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 232 <- wrt source file
2024-11-01T17:51:25.6449830Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/grad_mode.py::inference_mode:0
2024-11-01T17:51:25.6452408Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0, line 62 <- wrt source file
2024-11-01T17:51:25.6454873Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.name:0
2024-11-01T17:51:25.6457501Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 119 <- wrt source file
2024-11-01T17:51:25.6460199Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_hook:0
2024-11-01T17:51:25.6462927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 156 <- wrt source file
2024-11-01T17:51:25.6465657Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::Node.register_prehook:0
2024-11-01T17:51:25.6468392Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 280 <- wrt source file
2024-11-01T17:51:25.6471147Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0
2024-11-01T17:51:25.6473778Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 345 <- wrt source file
2024-11-01T17:51:25.6476413Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::save_on_cpu:0
2024-11-01T17:51:25.6479116Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 402 <- wrt source file
2024-11-01T17:51:25.6482027Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0
2024-11-01T17:51:25.6484836Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 479 <- wrt source file
2024-11-01T17:51:25.6487582Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0
2024-11-01T17:51:25.6490460Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 735 <- wrt source file
2024-11-01T17:51:25.6493328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0
2024-11-01T17:51:25.6496069Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0, line 177 <- wrt source file
2024-11-01T17:51:25.6498553Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::profile:0
2024-11-01T17:51:25.6501164Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0, line 714 <- wrt source file
2024-11-01T17:51:25.6503808Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::record_function:0
2024-11-01T17:51:25.6506735Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0, line 848 <- wrt source file
2024-11-01T17:51:25.6509347Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_itt:0
2024-11-01T17:51:25.6512009Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 921 <- wrt source file
2024-11-01T17:51:25.6514681Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/autograd/profiler.py::emit_nvtx:0
2024-11-01T17:51:25.6517271Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file
2024-11-01T17:51:25.6519810Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0
2024-11-01T17:51:25.6522374Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file
2024-11-01T17:51:25.6524892Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1
2024-11-01T17:51:25.6527475Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file
2024-11-01T17:51:25.6530019Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2
2024-11-01T17:51:25.6532709Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file
2024-11-01T17:51:25.6535507Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0
2024-11-01T17:51:25.6538157Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file
2024-11-01T17:51:25.6540550Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/profiler.py::profile:0
2024-11-01T17:51:25.6543125Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 411 <- wrt source file
2024-11-01T17:51:25.6545969Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0
2024-11-01T17:51:25.6548852Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 880 <- wrt source file
2024-11-01T17:51:25.6551843Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0
2024-11-01T17:51:25.6554854Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 962 <- wrt source file
2024-11-01T17:51:25.6557647Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0
2024-11-01T17:51:25.6560592Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2446 <- wrt source file
2024-11-01T17:51:25.6563568Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0
2024-11-01T17:51:25.6566565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0, line 2538 <- wrt source file
2024-11-01T17:51:25.6569490Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::batch_isend_irecv:0
2024-11-01T17:51:25.6572490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0, line 2647 <- wrt source file
2024-11-01T17:51:25.6575283Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_reduce:0
2024-11-01T17:51:25.6578170Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 2891 <- wrt source file
2024-11-01T17:51:25.6581095Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0
2024-11-01T17:51:25.6584050Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3091 <- wrt source file
2024-11-01T17:51:25.6586943Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0
2024-11-01T17:51:25.6589871Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3184 <- wrt source file
2024-11-01T17:51:25.6592761Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0
2024-11-01T17:51:25.6595827Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3286 <- wrt source file
2024-11-01T17:51:25.6598837Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0
2024-11-01T17:51:25.6601868Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3395 <- wrt source file
2024-11-01T17:51:25.6604842Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0
2024-11-01T17:51:25.6607959Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0, line 3490 <- wrt source file
2024-11-01T17:51:25.6610759Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather:0
2024-11-01T17:51:25.6613833Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3572 <- wrt source file
2024-11-01T17:51:25.6616844Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0
2024-11-01T17:51:25.6619889Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0, line 3697 <- wrt source file
2024-11-01T17:51:25.6622888Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_gather_coalesced:0
2024-11-01T17:51:25.6625799Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::gather:0, line 3790 <- wrt source file
2024-11-01T17:51:25.6628477Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::gather:0
2024-11-01T17:51:25.6631247Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0, line 3868 <- wrt source file
2024-11-01T17:51:25.6634058Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::scatter:0
2024-11-01T17:51:25.6637064Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 4005 <- wrt source file
2024-11-01T17:51:25.6640065Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0
2024-11-01T17:51:25.6643051Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all_single:0, line 4132 <- wrt source file
2024-11-01T17:51:25.6645976Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all_single:0
2024-11-01T17:51:25.6648856Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all:0, line 4244 <- wrt source file
2024-11-01T17:51:25.6651660Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::all_to_all:0
2024-11-01T17:51:25.6654585Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4416 <- wrt source file
2024-11-01T17:51:25.6657511Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0
2024-11-01T17:51:25.6660452Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0, line 4992 <- wrt source file
2024-11-01T17:51:25.6663322Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0
2024-11-01T17:51:25.6666363Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0, line 5094 <- wrt source file
2024-11-01T17:51:25.6669473Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py::new_subgroups_by_enumeration:0
2024-11-01T17:51:25.6672270Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/run.py::__doc__:0, line 87 <- wrt source file
2024-11-01T17:51:25.6674763Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/run.py::__doc__:0
2024-11-01T17:51:25.6677391Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0, line 39 <- wrt source file
2024-11-01T17:51:25.6680199Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/autograd/__init__.py::context:0
2024-11-01T17:51:25.6683143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 48 <- wrt source file
2024-11-01T17:51:25.6686252Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0
2024-11-01T17:51:25.6689240Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0, line 46 <- wrt source file
2024-11-01T17:51:25.6692049Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/contract.py::contract:0
2024-11-01T17:51:25.6694948Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 188 <- wrt source file
2024-11-01T17:51:25.6697976Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_composable/replicate.py::replicate:0
2024-11-01T17:51:25.6701128Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0, line 30 <- wrt source file
2024-11-01T17:51:25.6704582Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_optim/__init__.py::named_params_with_sharded_tensor:0
2024-11-01T17:51:25.6708115Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0, line 457 <- wrt source file
2024-11-01T17:51:25.6711355Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0
2024-11-01T17:51:25.6714676Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file
2024-11-01T17:51:25.6717891Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0
2024-11-01T17:51:25.6721013Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0, line 54 <- wrt source file
2024-11-01T17:51:25.6723931Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0
2024-11-01T17:51:25.6726789Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/join.py::Join:0, line 141 <- wrt source file
2024-11-01T17:51:25.6729465Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/join.py::Join:0
2024-11-01T17:51:25.6732488Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0, line 107 <- wrt source file
2024-11-01T17:51:25.6735849Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py::register_ddp_comm_hook:0
2024-11-01T17:51:25.6739222Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0, line 23 <- wrt source file
2024-11-01T17:51:25.6742470Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0
2024-11-01T17:51:25.6745973Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0, line 49 <- wrt source file
2024-11-01T17:51:25.6749312Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::allreduce_hook:0
2024-11-01T17:51:25.6752730Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0, line 104 <- wrt source file
2024-11-01T17:51:25.6756231Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_hook:0
2024-11-01T17:51:25.6759691Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0, line 125 <- wrt source file
2024-11-01T17:51:25.6763099Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_hook:0
2024-11-01T17:51:25.6766558Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0, line 143 <- wrt source file
2024-11-01T17:51:25.6769962Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::fp16_compress_wrapper:0
2024-11-01T17:51:25.6773603Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0, line 182 <- wrt source file
2024-11-01T17:51:25.6777055Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py::bf16_compress_wrapper:0
2024-11-01T17:51:25.6780587Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0, line 705 <- wrt source file
2024-11-01T17:51:25.6784050Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py::batched_powerSGD_hook:0
2024-11-01T17:51:25.6787666Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0, line 64 <- wrt source file
2024-11-01T17:51:25.6791371Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_pertensor_hook:0
2024-11-01T17:51:25.6795205Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0, line 145 <- wrt source file
2024-11-01T17:51:25.6798909Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py::quantization_perchannel_hook:0
2024-11-01T17:51:25.6802314Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0, line 1078 <- wrt source file
2024-11-01T17:51:25.6805304Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::get_state_dict:0
2024-11-01T17:51:25.6808602Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0, line 1319 <- wrt source file
2024-11-01T17:51:25.6811754Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_model_state_dict:0
2024-11-01T17:51:25.6815151Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0, line 1378 <- wrt source file
2024-11-01T17:51:25.6818373Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict.py::_patch_optimizer_state_dict:0
2024-11-01T17:51:25.6821738Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0, line 225 <- wrt source file
2024-11-01T17:51:25.6825055Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/api.py::RendezvousHandler.shutdown:0
2024-11-01T17:51:25.6828302Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0, line 141 <- wrt source file
2024-11-01T17:51:25.6831437Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/utils/distributed.py::get_free_port:0
2024-11-01T17:51:25.6834459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::StateDictType:0, line 260 <- wrt source file
2024-11-01T17:51:25.6837163Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::StateDictType:0
2024-11-01T17:51:25.6840101Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0, line 301 <- wrt source file
2024-11-01T17:51:25.6842914Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py::FullStateDictConfig:0
2024-11-01T17:51:25.6846114Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0, line 140 <- wrt source file
2024-11-01T17:51:25.6849551Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel:0
2024-11-01T17:51:25.6853274Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0, line 1503 <- wrt source file
2024-11-01T17:51:25.6857238Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.shard_full_optim_state_dict:0
2024-11-01T17:51:25.6861225Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0, line 1623 <- wrt source file
2024-11-01T17:51:25.6865247Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.scatter_full_optim_state_dict:0
2024-11-01T17:51:25.6869197Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0, line 1708 <- wrt source file
2024-11-01T17:51:25.6873007Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py::FullyShardedDataParallel.rekey_optim_state_dict:0
2024-11-01T17:51:25.6876593Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0, line 52 <- wrt source file
2024-11-01T17:51:25.6879702Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0
2024-11-01T17:51:25.6882791Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 236 <- wrt source file
2024-11-01T17:51:25.6885484Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0
2024-11-01T17:51:25.6888319Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file
2024-11-01T17:51:25.6891178Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0
2024-11-01T17:51:25.6894362Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0, line 42 <- wrt source file
2024-11-01T17:51:25.6897807Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_apply_optimizer_in_backward:0
2024-11-01T17:51:25.6901293Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0, line 113 <- wrt source file
2024-11-01T17:51:25.6904720Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py::_get_in_backward_optimizers:0
2024-11-01T17:51:25.6908421Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0, line 53 <- wrt source file
2024-11-01T17:51:25.6911454Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/named_optimizer.py::_NamedOptimizer:0
2024-11-01T17:51:25.6914567Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0, line 38 <- wrt source file
2024-11-01T17:51:25.6917577Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/utils.py::register_functional_optim:0
2024-11-01T17:51:25.6920527Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0, line 334 <- wrt source file
2024-11-01T17:51:25.6923318Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/_IR.py::pipe_split:0
2024-11-01T17:51:25.6926402Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0, line 82 <- wrt source file
2024-11-01T17:51:25.6929690Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0
2024-11-01T17:51:25.6933022Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0, line 101 <- wrt source file
2024-11-01T17:51:25.6936387Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0
2024-11-01T17:51:25.6939321Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 175 <- wrt source file
2024-11-01T17:51:25.6941898Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::_wait_all:0
2024-11-01T17:51:25.6944491Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 346 <- wrt source file
2024-11-01T17:51:25.6947015Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::shutdown:0
2024-11-01T17:51:25.6949744Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::remote:0, line 605 <- wrt source file
2024-11-01T17:51:25.6952246Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::remote:0
2024-11-01T17:51:25.6954899Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_sync:0, line 785 <- wrt source file
2024-11-01T17:51:25.6957450Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_sync:0
2024-11-01T17:51:25.6960041Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_async:0, line 877 <- wrt source file
2024-11-01T17:51:25.6962599Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/api.py::rpc_async:0
2024-11-01T17:51:25.6965676Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0, line 61 <- wrt source file
2024-11-01T17:51:25.6969118Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/server_process_global_profiler.py::_server_process_global_profile:0
2024-11-01T17:51:25.6972548Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0, line 232 <- wrt source file
2024-11-01T17:51:25.6976231Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_random.py::OffsetBasedRNGTracker._set_pre_op_offset:0
2024-11-01T17:51:25.6980356Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0, line 235 <- wrt source file
2024-11-01T17:51:25.6984270Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/_ops/_common_rules.py::pointwise_rule:0
2024-11-01T17:51:25.6988105Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0, line 56 <- wrt source file
2024-11-01T17:51:25.6992092Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/api.py::parallelize_module:0
2024-11-01T17:51:25.6996233Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0, line 88 <- wrt source file
2024-11-01T17:51:25.7000280Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/ddp.py::_pre_dp_module_transform:0
2024-11-01T17:51:25.7004289Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0, line 55 <- wrt source file
2024-11-01T17:51:25.7008505Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0
2024-11-01T17:51:25.7011575Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0, line 62 <- wrt source file
2024-11-01T17:51:25.7014661Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0
2024-11-01T17:51:25.7017781Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0, line 180 <- wrt source file
2024-11-01T17:51:25.7020844Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0
2024-11-01T17:51:25.7024308Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0, line 308 <- wrt source file
2024-11-01T17:51:25.7027402Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0
2024-11-01T17:51:25.7030318Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 29 <- wrt source file
2024-11-01T17:51:25.7033028Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/bernoulli.py::Bernoulli:0
2024-11-01T17:51:25.7035713Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0, line 20 <- wrt source file
2024-11-01T17:51:25.7038145Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/beta.py::Beta:0
2024-11-01T17:51:25.7040760Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0, line 28 <- wrt source file
2024-11-01T17:51:25.7043367Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/binomial.py::Binomial:0
2024-11-01T17:51:25.7046092Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0, line 40 <- wrt source file
2024-11-01T17:51:25.7049004Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/categorical.py::Categorical:0
2024-11-01T17:51:25.7051689Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file
2024-11-01T17:51:25.7054228Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/cauchy.py::Cauchy:0
2024-11-01T17:51:25.7056766Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0, line 15 <- wrt source file
2024-11-01T17:51:25.7059216Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/chi2.py::Chi2:0
2024-11-01T17:51:25.7061884Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0, line 160 <- wrt source file
2024-11-01T17:51:25.7064668Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::is_dependent:0
2024-11-01T17:51:25.7067560Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 181 <- wrt source file
2024-11-01T17:51:25.7070463Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/constraints.py::_DependentProperty:0
2024-11-01T17:51:25.7073543Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 34 <- wrt source file
2024-11-01T17:51:25.7076799Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0
2024-11-01T17:51:25.7079741Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 39 <- wrt source file
2024-11-01T17:51:25.7082408Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/dirichlet.py::Dirichlet:0
2024-11-01T17:51:25.7085160Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0, line 19 <- wrt source file
2024-11-01T17:51:25.7087914Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/exponential.py::Exponential:0
2024-11-01T17:51:25.7090945Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file
2024-11-01T17:51:25.7093855Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0
2024-11-01T17:51:25.7096607Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0, line 23 <- wrt source file
2024-11-01T17:51:25.7099127Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gamma.py::Gamma:0
2024-11-01T17:51:25.7101753Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0, line 34 <- wrt source file
2024-11-01T17:51:25.7104419Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/geometric.py::Geometric:0
2024-11-01T17:51:25.7107277Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 21 <- wrt source file
2024-11-01T17:51:25.7109827Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/gumbel.py::Gumbel:0
2024-11-01T17:51:25.7112493Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 23 <- wrt source file
2024-11-01T17:51:25.7115486Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0
2024-11-01T17:51:25.7118239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 23 <- wrt source file
2024-11-01T17:51:25.7120935Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/half_normal.py::HalfNormal:0
2024-11-01T17:51:25.7123716Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0, line 23 <- wrt source file
2024-11-01T17:51:25.7126507Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/independent.py::Independent:0
2024-11-01T17:51:25.7129321Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 21 <- wrt source file
2024-11-01T17:51:25.7132155Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0
2024-11-01T17:51:25.7134984Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 28 <- wrt source file
2024-11-01T17:51:25.7137734Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0
2024-11-01T17:51:25.7140457Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0, line 19 <- wrt source file
2024-11-01T17:51:25.7143037Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/laplace.py::Laplace:0
2024-11-01T17:51:25.7145731Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file
2024-11-01T17:51:25.7148488Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0
2024-11-01T17:51:25.7151246Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 20 <- wrt source file
2024-11-01T17:51:25.7153998Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/log_normal.py::LogNormal:0
2024-11-01T17:51:25.7156993Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 25 <- wrt source file
2024-11-01T17:51:25.7159907Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0
2024-11-01T17:51:25.7163080Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0, line 61 <- wrt source file
2024-11-01T17:51:25.7166466Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/lowrank_multivariate_normal.py::LowRankMultivariateNormal:0
2024-11-01T17:51:25.7169573Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 36 <- wrt source file
2024-11-01T17:51:25.7172342Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multinomial.py::Multinomial:0
2024-11-01T17:51:25.7175340Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 101 <- wrt source file
2024-11-01T17:51:25.7178437Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0
2024-11-01T17:51:25.7181354Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0, line 21 <- wrt source file
2024-11-01T17:51:25.7183881Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/normal.py::Normal:0
2024-11-01T17:51:25.7186720Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 31 <- wrt source file
2024-11-01T17:51:25.7189754Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0
2024-11-01T17:51:25.7192583Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0, line 17 <- wrt source file
2024-11-01T17:51:25.7195214Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/pareto.py::Pareto:0
2024-11-01T17:51:25.7197833Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0, line 23 <- wrt source file
2024-11-01T17:51:25.7200420Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/poisson.py::Poisson:0
2024-11-01T17:51:25.7203078Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0, line 21 <- wrt source file
2024-11-01T17:51:25.7205712Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/studentT.py::StudentT:0
2024-11-01T17:51:25.7208694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1033 <- wrt source file
2024-11-01T17:51:25.7211480Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CatTransform:0
2024-11-01T17:51:25.7214321Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1139 <- wrt source file
2024-11-01T17:51:25.7217150Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::StackTransform:0
2024-11-01T17:51:25.7220209Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1213 <- wrt source file
2024-11-01T17:51:25.7223583Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0
2024-11-01T17:51:25.7226496Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file
2024-11-01T17:51:25.7229090Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/uniform.py::Uniform:0
2024-11-01T17:51:25.7231732Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0, line 107 <- wrt source file
2024-11-01T17:51:25.7234414Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/utils.py::clamp_probs:0
2024-11-01T17:51:25.7237079Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0, line 115 <- wrt source file
2024-11-01T17:51:25.7239703Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/von_mises.py::VonMises:0
2024-11-01T17:51:25.7242329Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0, line 19 <- wrt source file
2024-11-01T17:51:25.7245058Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/weibull.py::Weibull:0
2024-11-01T17:51:25.7247816Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0, line 40 <- wrt source file
2024-11-01T17:51:25.7250393Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/wishart.py::Wishart:0
2024-11-01T17:51:25.7253098Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 602 <- wrt source file
2024-11-01T17:51:25.7255887Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0
2024-11-01T17:51:25.7258489Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0, line 105 <- wrt source file
2024-11-01T17:51:25.7260842Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::_snake_case:0
2024-11-01T17:51:25.7263405Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1826 <- wrt source file
2024-11-01T17:51:25.7266045Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0
2024-11-01T17:51:25.7268690Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1900 <- wrt source file
2024-11-01T17:51:25.7271291Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/graph.py::Graph.on_generate_code:0
2024-11-01T17:51:25.7273966Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Interpreter:0, line 44 <- wrt source file
2024-11-01T17:51:25.7276471Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Interpreter:0
2024-11-01T17:51:25.7279016Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Transformer:0, line 452 <- wrt source file
2024-11-01T17:51:25.7281533Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/interpreter.py::Transformer:0
2024-11-01T17:51:25.7284190Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0, line 135 <- wrt source file
2024-11-01T17:51:25.7286865Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0
2024-11-01T17:51:25.7289621Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0, line 12 <- wrt source file
2024-11-01T17:51:25.7292133Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::TensorType:0
2024-11-01T17:51:25.7294667Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0, line 65 <- wrt source file
2024-11-01T17:51:25.7297216Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_consistent:0
2024-11-01T17:51:25.7299796Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- wrt source file
2024-11-01T17:51:25.7302342Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/tensor_type.py::is_more_precise:0
2024-11-01T17:51:25.7305234Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0, line 96 <- wrt source file
2024-11-01T17:51:25.7308506Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0
2024-11-01T17:51:25.7311534Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/core.py::reify:0, line 58 <- wrt source file
2024-11-01T17:51:25.7314576Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/core.py::reify:0
2024-11-01T17:51:25.7317565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0, line 48 <- wrt source file
2024-11-01T17:51:25.7320618Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/match.py::VarDispatcher:0
2024-11-01T17:51:25.7323652Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0, line 11 <- wrt source file
2024-11-01T17:51:25.7326552Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unifiable:0
2024-11-01T17:51:25.7329538Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0, line 37 <- wrt source file
2024-11-01T17:51:25.7332510Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::reify_object:0
2024-11-01T17:51:25.7335513Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0, line 93 <- wrt source file
2024-11-01T17:51:25.7338458Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/more.py::unify_object:0
2024-11-01T17:51:25.7341546Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0, line 37 <- wrt source file
2024-11-01T17:51:25.7344675Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge:0
2024-11-01T17:51:25.7347884Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0, line 64 <- wrt source file
2024-11-01T17:51:25.7351096Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::merge_with:0
2024-11-01T17:51:25.7354390Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0, line 90 <- wrt source file
2024-11-01T17:51:25.7357700Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valmap:0
2024-11-01T17:51:25.7360927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0, line 106 <- wrt source file
2024-11-01T17:51:25.7377701Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keymap:0
2024-11-01T17:51:25.7381246Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0, line 122 <- wrt source file
2024-11-01T17:51:25.7384436Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0
2024-11-01T17:51:25.7387694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0, line 138 <- wrt source file
2024-11-01T17:51:25.7390918Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0
2024-11-01T17:51:25.7394326Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0, line 158 <- wrt source file
2024-11-01T17:51:25.7397837Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0
2024-11-01T17:51:25.7401101Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0, line 178 <- wrt source file
2024-11-01T17:51:25.7404376Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::itemfilter:0
2024-11-01T17:51:25.7407819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0, line 204 <- wrt source file
2024-11-01T17:51:25.7410967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::assoc:0
2024-11-01T17:51:25.7414161Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0, line 221 <- wrt source file
2024-11-01T17:51:25.7417328Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0
2024-11-01T17:51:25.7420504Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 416 <- wrt source file
2024-11-01T17:51:25.7423634Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0
2024-11-01T17:51:25.7426731Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 15 <- wrt source file
2024-11-01T17:51:25.7429772Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0
2024-11-01T17:51:25.7432779Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 42 <- wrt source file
2024-11-01T17:51:25.7435794Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0
2024-11-01T17:51:25.7439121Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 70 <- wrt source file
2024-11-01T17:51:25.7442102Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0
2024-11-01T17:51:25.7445063Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 95 <- wrt source file
2024-11-01T17:51:25.7447933Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/utils.py::freeze:0
2024-11-01T17:51:25.7450928Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 67 <- wrt source file
2024-11-01T17:51:25.7453941Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/variable.py::variables:0
2024-11-01T17:51:25.7457190Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 21 <- wrt source file
2024-11-01T17:51:25.7460506Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0
2024-11-01T17:51:25.7464088Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 113 <- wrt source file
2024-11-01T17:51:25.7467599Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0
2024-11-01T17:51:25.7471252Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 138 <- wrt source file
2024-11-01T17:51:25.7475046Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0
2024-11-01T17:51:25.7478745Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 191 <- wrt source file
2024-11-01T17:51:25.7482352Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0
2024-11-01T17:51:25.7486020Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file
2024-11-01T17:51:25.7489726Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0
2024-11-01T17:51:25.7493398Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file
2024-11-01T17:51:25.7496945Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0
2024-11-01T17:51:25.7500460Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 18 <- wrt source file
2024-11-01T17:51:25.7503878Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0
2024-11-01T17:51:25.7507507Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0, line 41 <- wrt source file
2024-11-01T17:51:25.7511009Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::_toposort:0
2024-11-01T17:51:25.7514489Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 68 <- wrt source file
2024-11-01T17:51:25.7517883Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0
2024-11-01T17:51:25.7521282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file
2024-11-01T17:51:25.7524576Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0
2024-11-01T17:51:25.7527941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file
2024-11-01T17:51:25.7531280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0
2024-11-01T17:51:25.7534712Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file
2024-11-01T17:51:25.7538280Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0
2024-11-01T17:51:25.7541751Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0, line 83 <- wrt source file
2024-11-01T17:51:25.7545161Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::Variadic:0
2024-11-01T17:51:25.7548381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 122 <- wrt source file
2024-11-01T17:51:25.7551396Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0
2024-11-01T17:51:25.7554321Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 92 <- wrt source file
2024-11-01T17:51:25.7556981Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0
2024-11-01T17:51:25.7559660Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0, line 85 <- wrt source file
2024-11-01T17:51:25.7562325Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/split_module.py::split_module:0
2024-11-01T17:51:25.7565561Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 53 <- wrt source file
2024-11-01T17:51:25.7569182Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0
2024-11-01T17:51:25.7572445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file
2024-11-01T17:51:25.7575303Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0
2024-11-01T17:51:25.7578281Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file
2024-11-01T17:51:25.7581126Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0
2024-11-01T17:51:25.7584143Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file
2024-11-01T17:51:25.7587144Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0
2024-11-01T17:51:25.7590068Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file
2024-11-01T17:51:25.7592842Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0
2024-11-01T17:51:25.7595591Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0, line 1521 <- wrt source file
2024-11-01T17:51:25.7597998Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/_ops.py::logaddexp:0
2024-11-01T17:51:25.7600673Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file
2024-11-01T17:51:25.7603942Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0
2024-11-01T17:51:25.7608044Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 467 <- wrt source file
2024-11-01T17:51:25.7611845Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0
2024-11-01T17:51:25.7615666Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 586 <- wrt source file
2024-11-01T17:51:25.8216414Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0
2024-11-01T17:51:25.8240538Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2181 <- wrt source file
2024-11-01T17:51:25.8249132Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::gumbel_softmax:0
2024-11-01T17:51:25.8251782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file
2024-11-01T17:51:25.8259748Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding:0
2024-11-01T17:51:25.8262403Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file
2024-11-01T17:51:25.8274120Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::embedding_bag:0
2024-11-01T17:51:25.8276752Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0, line 3059 <- wrt source file
2024-11-01T17:51:25.8292750Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::ctc_loss:0
2024-11-01T17:51:25.8295228Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0, line 3136 <- wrt source file
2024-11-01T17:51:25.8299658Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::nll_loss:0
2024-11-01T17:51:25.8302538Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0, line 3461 <- wrt source file
2024-11-01T17:51:25.8309188Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::cross_entropy:0
2024-11-01T17:51:25.8311867Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3533 <- wrt source file
2024-11-01T17:51:25.8316341Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy:0
2024-11-01T17:51:25.8319165Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3610 <- wrt source file
2024-11-01T17:51:25.8323149Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0
2024-11-01T17:51:25.8325857Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0, line 5173 <- wrt source file
2024-11-01T17:51:25.8333230Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py::pad:0
2024-11-01T17:51:25.8335617Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file
2024-11-01T17:51:25.8342558Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_input:0
2024-11-01T17:51:25.8345697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file
2024-11-01T17:51:25.8348427Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv1d_weight:0
2024-11-01T17:51:25.8351031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file
2024-11-01T17:51:25.8355949Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_input:0
2024-11-01T17:51:25.8358539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file
2024-11-01T17:51:25.8361063Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv2d_weight:0
2024-11-01T17:51:25.8363578Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file
2024-11-01T17:51:25.8396933Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_input:0
2024-11-01T17:51:25.8399700Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file
2024-11-01T17:51:25.8447563Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/grad.py::conv3d_weight:0
2024-11-01T17:51:25.8450138Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file
2024-11-01T17:51:25.8452638Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::calculate_gain:0
2024-11-01T17:51:25.8455127Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file
2024-11-01T17:51:25.8457489Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::uniform_:0
2024-11-01T17:51:25.8459903Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file
2024-11-01T17:51:25.8462233Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::normal_:0
2024-11-01T17:51:25.8465041Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file
2024-11-01T17:51:25.8478978Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::trunc_normal_:0
2024-11-01T17:51:25.8480819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file
2024-11-01T17:51:25.8482886Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::constant_:0
2024-11-01T17:51:25.8484697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file
2024-11-01T17:51:25.8486812Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::ones_:0
2024-11-01T17:51:25.8488724Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file
2024-11-01T17:51:25.8491562Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::zeros_:0
2024-11-01T17:51:25.8494177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file
2024-11-01T17:51:25.8496792Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::eye_:0
2024-11-01T17:51:25.8499610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file
2024-11-01T17:51:25.8502157Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::dirac_:0
2024-11-01T17:51:25.8504685Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file
2024-11-01T17:51:25.8507632Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_uniform_:0
2024-11-01T17:51:25.8510223Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file
2024-11-01T17:51:25.8512723Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::xavier_normal_:0
2024-11-01T17:51:25.8515385Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file
2024-11-01T17:51:25.8518338Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_uniform_:0
2024-11-01T17:51:25.8520930Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file
2024-11-01T17:51:25.8523435Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::kaiming_normal_:0
2024-11-01T17:51:25.8525945Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file
2024-11-01T17:51:25.8529969Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::orthogonal_:0
2024-11-01T17:51:25.8533203Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file
2024-11-01T17:51:25.8535585Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/init.py::sparse_:0
2024-11-01T17:51:25.8538145Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 100 <- wrt source file
2024-11-01T17:51:25.8540850Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0
2024-11-01T17:51:25.8543746Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file
2024-11-01T17:51:25.8546373Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/attention/bias.py::CausalBias:0
2024-11-01T17:51:25.8549084Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file
2024-11-01T17:51:25.8551780Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Threshold:0
2024-11-01T17:51:25.8554628Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file
2024-11-01T17:51:25.8557243Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU:0
2024-11-01T17:51:25.8559909Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file
2024-11-01T17:51:25.8562525Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::RReLU:0
2024-11-01T17:51:25.8565221Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file
2024-11-01T17:51:25.8568036Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardtanh:0
2024-11-01T17:51:25.8570722Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file
2024-11-01T17:51:25.8573553Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ReLU6:0
2024-11-01T17:51:25.8576264Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file
2024-11-01T17:51:25.8578939Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Sigmoid:0
2024-11-01T17:51:25.8581675Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file
2024-11-01T17:51:25.8584444Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0
2024-11-01T17:51:25.8587177Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file
2024-11-01T17:51:25.8589767Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanh:0
2024-11-01T17:51:25.8592412Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file
2024-11-01T17:51:25.8595111Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SiLU:0
2024-11-01T17:51:25.8597761Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file
2024-11-01T17:51:25.8600352Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Mish:0
2024-11-01T17:51:25.8603055Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file
2024-11-01T17:51:25.8605791Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardswish:0
2024-11-01T17:51:25.8608669Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file
2024-11-01T17:51:25.8611421Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::ELU:0
2024-11-01T17:51:25.8614061Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file
2024-11-01T17:51:25.8616676Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::CELU:0
2024-11-01T17:51:25.8619342Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file
2024-11-01T17:51:25.8621950Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::SELU:0
2024-11-01T17:51:25.8624580Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file
2024-11-01T17:51:25.8627155Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GLU:0
2024-11-01T17:51:25.8629788Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file
2024-11-01T17:51:25.8632401Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::GELU:0
2024-11-01T17:51:25.8635303Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file
2024-11-01T17:51:25.8638069Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Hardshrink:0
2024-11-01T17:51:25.8640855Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file
2024-11-01T17:51:25.8643580Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LeakyReLU:0
2024-11-01T17:51:25.8646368Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file
2024-11-01T17:51:25.8649102Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSigmoid:0
2024-11-01T17:51:25.8651879Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file
2024-11-01T17:51:25.8654539Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softplus:0
2024-11-01T17:51:25.8657292Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file
2024-11-01T17:51:25.8660032Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softshrink:0
2024-11-01T17:51:25.8662929Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1026 <- wrt source file
2024-11-01T17:51:25.8665869Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0
2024-11-01T17:51:25.8668697Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1489 <- wrt source file
2024-11-01T17:51:25.8671321Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::PReLU:0
2024-11-01T17:51:25.8674096Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1531 <- wrt source file
2024-11-01T17:51:25.8676970Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softsign:0
2024-11-01T17:51:25.8679734Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1554 <- wrt source file
2024-11-01T17:51:25.8682503Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Tanhshrink:0
2024-11-01T17:51:25.8685279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1589 <- wrt source file
2024-11-01T17:51:25.8687951Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmin:0
2024-11-01T17:51:25.8690649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1647 <- wrt source file
2024-11-01T17:51:25.8693317Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax:0
2024-11-01T17:51:25.8696065Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1688 <- wrt source file
2024-11-01T17:51:25.8698789Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::Softmax2d:0
2024-11-01T17:51:25.8701541Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1724 <- wrt source file
2024-11-01T17:51:25.8704415Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/activation.py::LogSoftmax:0
2024-11-01T17:51:25.8707376Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file
2024-11-01T17:51:25.8710106Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0
2024-11-01T17:51:25.8712877Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file
2024-11-01T17:51:25.9055337Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0
2024-11-01T17:51:25.9058116Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file
2024-11-01T17:51:26.1735935Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0
2024-11-01T17:51:26.1930381Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file
2024-11-01T17:51:26.1956368Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0
2024-11-01T17:51:26.1960207Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0, line 86 <- wrt source file
2024-11-01T17:51:26.1963495Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::Sequential:0
2024-11-01T17:51:26.1966503Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0, line 292 <- wrt source file
2024-11-01T17:51:26.1969172Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleList:0
2024-11-01T17:51:26.1971873Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 474 <- wrt source file
2024-11-01T17:51:26.1974508Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ModuleDict:0
2024-11-01T17:51:26.1977666Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0, line 606 <- wrt source file
2024-11-01T17:51:26.1980378Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterList:0
2024-11-01T17:51:26.1983112Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 758 <- wrt source file
2024-11-01T17:51:26.1985821Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/container.py::ParameterDict:0
2024-11-01T17:51:26.1988582Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file
2024-11-01T17:51:26.1991313Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0
2024-11-01T17:51:26.1994166Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file
2024-11-01T17:51:26.1996901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0
2024-11-01T17:51:26.1999554Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file
2024-11-01T17:51:26.2002205Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout:0
2024-11-01T17:51:26.2004762Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file
2024-11-01T17:51:26.2007527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout1d:0
2024-11-01T17:51:26.2010137Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file
2024-11-01T17:51:26.2025006Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout2d:0
2024-11-01T17:51:26.2027686Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file
2024-11-01T17:51:26.2107340Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::Dropout3d:0
2024-11-01T17:51:26.2110231Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file
2024-11-01T17:51:26.2113095Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0
2024-11-01T17:51:26.2115968Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file
2024-11-01T17:51:26.2194979Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0
2024-11-01T17:51:26.2197899Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file
2024-11-01T17:51:26.2202285Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py::Flatten:0
2024-11-01T17:51:26.2204794Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file
2024-11-01T17:51:26.2209312Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Fold:0
2024-11-01T17:51:26.2212162Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file
2024-11-01T17:51:26.2225336Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/fold.py::Unfold:0
2024-11-01T17:51:26.2228017Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file
2024-11-01T17:51:26.2240527Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0
2024-11-01T17:51:26.2243402Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file
2024-11-01T17:51:26.2443212Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0
2024-11-01T17:51:26.2446142Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file
2024-11-01T17:51:26.5111279Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0
2024-11-01T17:51:26.5295432Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file
2024-11-01T17:51:26.5298588Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0
2024-11-01T17:51:26.5301341Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file
2024-11-01T17:51:26.5305781Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Identity:0
2024-11-01T17:51:26.5308904Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file
2024-11-01T17:51:26.5315412Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Linear:0
2024-11-01T17:51:26.5318040Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file
2024-11-01T17:51:26.5336485Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/linear.py::Bilinear:0
2024-11-01T17:51:26.5339133Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file
2024-11-01T17:51:26.5345651Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::L1Loss:0
2024-11-01T17:51:26.5348193Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file
2024-11-01T17:51:26.5374710Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::NLLLoss:0
2024-11-01T17:51:26.5377500Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file
2024-11-01T17:51:26.5383940Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0
2024-11-01T17:51:26.5386794Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file
2024-11-01T17:51:26.5400650Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0
2024-11-01T17:51:26.5403463Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 517 <- wrt source file
2024-11-01T17:51:26.5410738Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::KLDivLoss:0
2024-11-01T17:51:26.5413402Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 595 <- wrt source file
2024-11-01T17:51:26.5418182Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MSELoss:0
2024-11-01T17:51:26.5420992Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 677 <- wrt source file
2024-11-01T17:51:26.5425394Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCELoss:0
2024-11-01T17:51:26.5428124Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 748 <- wrt source file
2024-11-01T17:51:26.5438168Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0
2024-11-01T17:51:26.5448708Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 941 <- wrt source file
2024-11-01T17:51:26.5451606Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0
2024-11-01T17:51:26.5454759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1261 <- wrt source file
2024-11-01T17:51:26.5457967Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0
2024-11-01T17:51:26.5460787Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1401 <- wrt source file
2024-11-01T17:51:26.5468343Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0
2024-11-01T17:51:26.5471159Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1466 <- wrt source file
2024-11-01T17:51:26.5476200Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0
2024-11-01T17:51:26.5478998Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1545 <- wrt source file
2024-11-01T17:51:26.5485901Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0
2024-11-01T17:51:26.5488690Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1645 <- wrt source file
2024-11-01T17:51:26.5498594Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0
2024-11-01T17:51:26.5501281Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1886 <- wrt source file
2024-11-01T17:51:26.5533178Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py::CTCLoss:0
2024-11-01T17:51:26.5535926Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 545 <- wrt source file
2024-11-01T17:51:26.5538756Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.register_buffer:0
2024-11-01T17:51:26.5541552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1005 <- wrt source file
2024-11-01T17:51:26.5550276Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.apply:0
2024-11-01T17:51:26.5552907Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0, line 1259 <- wrt source file
2024-11-01T17:51:26.5558146Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.to:0
2024-11-01T17:51:26.5560873Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2183 <- wrt source file
2024-11-01T17:51:26.5563608Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.state_dict:0
2024-11-01T17:51:26.5566349Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2625 <- wrt source file
2024-11-01T17:51:26.5569089Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.parameters:0
2024-11-01T17:51:26.5571920Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2653 <- wrt source file
2024-11-01T17:51:26.5574760Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_parameters:0
2024-11-01T17:51:26.5577781Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2680 <- wrt source file
2024-11-01T17:51:26.5580551Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.buffers:0
2024-11-01T17:51:26.5583298Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2707 <- wrt source file
2024-11-01T17:51:26.5586077Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_buffers:0
2024-11-01T17:51:26.5588872Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2738 <- wrt source file
2024-11-01T17:51:26.5591671Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_children:0
2024-11-01T17:51:26.5594544Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2762 <- wrt source file
2024-11-01T17:51:26.5597306Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.modules:0
2024-11-01T17:51:26.5600488Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2800 <- wrt source file
2024-11-01T17:51:26.5604281Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/module.py::Module.named_modules:0
2024-11-01T17:51:26.5608589Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file
2024-11-01T17:51:26.5619484Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0
2024-11-01T17:51:26.5622819Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file
2024-11-01T17:51:26.5628686Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::LayerNorm:0
2024-11-01T17:51:26.5631552Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file
2024-11-01T17:51:26.5637392Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::GroupNorm:0
2024-11-01T17:51:26.5640254Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 356 <- wrt source file
2024-11-01T17:51:26.5643579Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/normalization.py::RMSNorm:0
2024-11-01T17:51:26.5646304Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file
2024-11-01T17:51:26.5651171Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad1d:0
2024-11-01T17:51:26.5653884Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file
2024-11-01T17:51:26.5674678Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad2d:0
2024-11-01T17:51:26.5677448Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file
2024-11-01T17:51:27.2662012Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::CircularPad3d:0
2024-11-01T17:51:27.2968980Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file
2024-11-01T17:51:27.2978337Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0
2024-11-01T17:51:27.2981549Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file
2024-11-01T17:51:27.2987358Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0
2024-11-01T17:51:27.2990693Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file
2024-11-01T17:51:27.3015246Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0
2024-11-01T17:51:27.3018005Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file
2024-11-01T17:51:27.3022226Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0
2024-11-01T17:51:27.3024963Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file
2024-11-01T17:51:27.3028805Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0
2024-11-01T17:51:27.3031539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file
2024-11-01T17:51:27.3034330Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0
2024-11-01T17:51:27.3037073Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file
2024-11-01T17:51:27.3041840Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0
2024-11-01T17:51:27.3044609Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file
2024-11-01T17:51:27.3049047Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0
2024-11-01T17:51:27.3051870Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file
2024-11-01T17:51:27.8684453Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0
2024-11-01T17:51:27.9013219Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file
2024-11-01T17:51:27.9025085Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0
2024-11-01T17:51:27.9027727Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file
2024-11-01T17:51:27.9033431Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0
2024-11-01T17:51:27.9036814Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file
2024-11-01T17:51:27.9063785Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0
2024-11-01T17:51:27.9067488Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file
2024-11-01T17:51:27.9071694Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0
2024-11-01T17:51:27.9075393Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file
2024-11-01T17:51:27.9079944Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0
2024-11-01T17:51:27.9083376Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file
2024-11-01T17:51:27.9088959Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0
2024-11-01T17:51:27.9091600Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file
2024-11-01T17:51:27.9147622Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0
2024-11-01T17:51:27.9150684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file
2024-11-01T17:51:28.1578362Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0
2024-11-01T17:51:28.1641986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file
2024-11-01T17:51:28.1655374Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0
2024-11-01T17:51:28.1658616Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file
2024-11-01T17:51:28.2555854Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0
2024-11-01T17:51:28.2558653Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file
2024-11-01T17:51:28.2567916Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0
2024-11-01T17:51:28.2570943Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file
2024-11-01T17:51:28.2613588Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0
2024-11-01T17:51:28.2616195Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file
2024-11-01T17:51:28.4396764Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0
2024-11-01T17:51:28.4457941Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file
2024-11-01T17:51:28.4511209Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0
2024-11-01T17:51:28.4514599Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file
2024-11-01T17:51:28.5500327Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0
2024-11-01T17:51:28.5503539Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file
2024-11-01T17:51:28.5512621Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool1d:0
2024-11-01T17:51:28.5515869Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file
2024-11-01T17:51:28.5572719Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool2d:0
2024-11-01T17:51:28.5575927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file
2024-11-01T17:51:28.7852637Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::LPPool3d:0
2024-11-01T17:51:28.7913914Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file
2024-11-01T17:51:28.7920875Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0
2024-11-01T17:51:28.7923656Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file
2024-11-01T17:51:28.7930672Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0
2024-11-01T17:51:28.7933487Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file
2024-11-01T17:51:28.7962204Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0
2024-11-01T17:51:28.7965031Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file
2024-11-01T17:51:28.7967812Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0
2024-11-01T17:51:28.7970577Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file
2024-11-01T17:51:28.7976106Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0
2024-11-01T17:51:28.7979269Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file
2024-11-01T17:51:28.8000323Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0
2024-11-01T17:51:28.8002879Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0, line 589 <- wrt source file
2024-11-01T17:51:28.8013849Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNN:0
2024-11-01T17:51:28.8016330Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 946 <- wrt source file
2024-11-01T17:51:28.8402566Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTM:0
2024-11-01T17:51:28.8405089Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1283 <- wrt source file
2024-11-01T17:51:28.8421559Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRU:0
2024-11-01T17:51:28.8424141Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1534 <- wrt source file
2024-11-01T17:51:28.8434938Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::RNNCell:0
2024-11-01T17:51:28.8437508Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1656 <- wrt source file
2024-11-01T17:51:28.8446592Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::LSTMCell:0
2024-11-01T17:51:28.8449142Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1770 <- wrt source file
2024-11-01T17:51:28.8460211Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/rnn.py::GRUCell:0
2024-11-01T17:51:28.8462796Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file
2024-11-01T17:51:28.8475419Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding:0
2024-11-01T17:51:28.8478282Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file
2024-11-01T17:51:28.8481509Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0
2024-11-01T17:51:28.8484522Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file
2024-11-01T17:51:28.8489763Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0
2024-11-01T17:51:28.8492928Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 86 <- wrt source file
2024-11-01T17:51:29.4923745Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer:0
2024-11-01T17:51:29.4941459Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 254 <- wrt source file
2024-11-01T17:51:29.4944777Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0
2024-11-01T17:51:29.4948474Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 319 <- wrt source file
2024-11-01T17:51:29.5601558Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0
2024-11-01T17:51:29.5608313Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 532 <- wrt source file
2024-11-01T17:51:29.6919280Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0
2024-11-01T17:51:29.6929446Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 653 <- wrt source file
2024-11-01T17:51:29.7166625Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0
2024-11-01T17:51:29.7169750Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 957 <- wrt source file
2024-11-01T17:51:29.7551780Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0
2024-11-01T17:51:29.7554774Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file
2024-11-01T17:51:29.7580241Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::Upsample:0
2024-11-01T17:51:29.7583497Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file
2024-11-01T17:51:29.7596766Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0
2024-11-01T17:51:29.7600154Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file
2024-11-01T17:51:29.7609009Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0
2024-11-01T17:51:29.7612541Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 126 <- wrt source file
2024-11-01T17:51:29.7615831Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0
2024-11-01T17:51:29.7618938Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 619 <- wrt source file
2024-11-01T17:51:29.7622353Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0
2024-11-01T17:51:29.7626100Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1418 <- wrt source file
2024-11-01T17:51:29.7629803Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0
2024-11-01T17:51:29.7633822Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1981 <- wrt source file
2024-11-01T17:51:29.7638060Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0
2024-11-01T17:51:29.7642030Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 1991 <- wrt source file
2024-11-01T17:51:29.7646576Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1
2024-11-01T17:51:29.7650840Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2026 <- wrt source file
2024-11-01T17:51:29.7655474Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0
2024-11-01T17:51:29.7659542Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file
2024-11-01T17:51:29.7662773Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0
2024-11-01T17:51:29.7665773Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file
2024-11-01T17:51:29.7668679Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/init.py::skip_init:0
2024-11-01T17:51:29.7671610Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file
2024-11-01T17:51:29.7675064Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0
2024-11-01T17:51:29.7678212Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file
2024-11-01T17:51:29.7681536Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0
2024-11-01T17:51:29.7684844Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file
2024-11-01T17:51:29.7688103Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0
2024-11-01T17:51:29.7691604Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 506 <- wrt source file
2024-11-01T17:51:29.7695301Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0
2024-11-01T17:51:29.7698279Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0, line 845 <- wrt source file
2024-11-01T17:51:29.7700877Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::identity:0
2024-11-01T17:51:29.7703784Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 881 <- wrt source file
2024-11-01T17:51:29.7707374Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::random_unstructured:0
2024-11-01T17:51:29.7710341Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 924 <- wrt source file
2024-11-01T17:51:29.7713295Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::l1_unstructured:0
2024-11-01T17:51:29.7716506Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0, line 1191 <- wrt source file
2024-11-01T17:51:29.7719459Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::remove:0
2024-11-01T17:51:29.7722694Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1219 <- wrt source file
2024-11-01T17:51:29.7725761Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py::is_pruned:0
2024-11-01T17:51:29.7728927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 357 <- wrt source file
2024-11-01T17:51:29.7731856Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0
2024-11-01T17:51:29.7734768Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 435 <- wrt source file
2024-11-01T17:51:29.7737754Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pad_sequence:0
2024-11-01T17:51:29.7741004Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 493 <- wrt source file
2024-11-01T17:51:29.7746497Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0
2024-11-01T17:51:29.7749660Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 549 <- wrt source file
2024-11-01T17:51:29.7757391Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::pack_sequence:0
2024-11-01T17:51:29.7760445Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 577 <- wrt source file
2024-11-01T17:51:29.7778486Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0
2024-11-01T17:51:29.7781373Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file
2024-11-01T17:51:29.7788367Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0
2024-11-01T17:51:29.7791553Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file
2024-11-01T17:51:29.7798611Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0
2024-11-01T17:51:29.7802094Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 214 <- wrt source file
2024-11-01T17:51:29.7805463Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/stateless.py::functional_call:0
2024-11-01T17:51:29.7808759Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file
2024-11-01T17:51:29.7814676Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0
2024-11-01T17:51:29.7817903Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file
2024-11-01T17:51:29.7822520Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0
2024-11-01T17:51:29.7826093Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 317 <- wrt source file
2024-11-01T17:51:29.7829523Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0
2024-11-01T17:51:29.7834171Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/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
2024-11-01T17:51:29.7861861Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0
2024-11-01T17:51:29.7865033Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 309 <- wrt source file
2024-11-01T17:51:29.7867576Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0
2024-11-01T17:51:29.7870490Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 411 <- wrt source file
2024-11-01T17:51:29.7873235Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0
2024-11-01T17:51:29.7876032Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 511 <- wrt source file
2024-11-01T17:51:29.7878843Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::StepLR:0
2024-11-01T17:51:29.7881488Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 571 <- wrt source file
2024-11-01T17:51:29.7884624Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0
2024-11-01T17:51:29.7887298Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 636 <- wrt source file
2024-11-01T17:51:29.7889973Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0
2024-11-01T17:51:29.7901821Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 714 <- wrt source file
2024-11-01T17:51:29.7904598Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::LinearLR:0
2024-11-01T17:51:29.7907481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 847 <- wrt source file
2024-11-01T17:51:29.7910117Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0
2024-11-01T17:51:29.7912751Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 996 <- wrt source file
2024-11-01T17:51:29.7915468Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0
2024-11-01T17:51:29.7918344Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1152 <- wrt source file
2024-11-01T17:51:29.7921039Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0
2024-11-01T17:51:29.7923804Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1295 <- wrt source file
2024-11-01T17:51:29.7926565Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0
2024-11-01T17:51:29.7929239Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1543 <- wrt source file
2024-11-01T17:51:29.7931753Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0
2024-11-01T17:51:29.7934986Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1813 <- wrt source file
2024-11-01T17:51:29.7938050Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0
2024-11-01T17:51:29.7941185Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1829 <- wrt source file
2024-11-01T17:51:29.7944254Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1
2024-11-01T17:51:29.7947120Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1973 <- wrt source file
2024-11-01T17:51:29.7949697Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0
2024-11-01T17:51:29.7952255Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0, line 330 <- wrt source file
2024-11-01T17:51:29.7954824Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py::update_bn:0
2024-11-01T17:51:29.7957369Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0, line 21 <- wrt source file
2024-11-01T17:51:29.7960129Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/package/glob_group.py::GlobGroup:0
2024-11-01T17:51:29.7963016Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 279 <- wrt source file
2024-11-01T17:51:29.7966180Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0
2024-11-01T17:51:29.7969051Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0, line 580 <- wrt source file
2024-11-01T17:51:29.7971545Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/profiler/profiler.py::profile:0
2024-11-01T17:51:29.7974284Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 338 <- wrt source file
2024-11-01T17:51:29.7977208Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0
2024-11-01T17:51:29.7979973Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file
2024-11-01T17:51:29.7982500Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_creation.py::make_tensor:0
2024-11-01T17:51:29.7985226Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 596 <- wrt source file
2024-11-01T17:51:29.7988038Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::parametrize:0
2024-11-01T17:51:29.7990914Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 717 <- wrt source file
2024-11-01T17:51:29.7993760Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0
2024-11-01T17:51:29.7996708Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 806 <- wrt source file
2024-11-01T17:51:29.7999640Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0
2024-11-01T17:51:29.8002649Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4482 <- wrt source file
2024-11-01T17:51:29.8005777Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0
2024-11-01T17:51:29.8009178Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4496 <- wrt source file
2024-11-01T17:51:29.8012276Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0
2024-11-01T17:51:29.8015415Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4526 <- wrt source file
2024-11-01T17:51:29.8018466Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0
2024-11-01T17:51:29.8021455Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 192 <- wrt source file
2024-11-01T17:51:29.8024549Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0
2024-11-01T17:51:29.8027764Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 288 <- wrt source file
2024-11-01T17:51:29.8031171Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0
2024-11-01T17:51:29.8034733Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file
2024-11-01T17:51:29.8038269Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0
2024-11-01T17:51:29.8041298Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 257 <- wrt source file
2024-11-01T17:51:29.8043863Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0
2024-11-01T17:51:29.8046673Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 299 <- wrt source file
2024-11-01T17:51:29.8049278Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0
2024-11-01T17:51:29.8051860Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 329 <- wrt source file
2024-11-01T17:51:29.8054341Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0
2024-11-01T17:51:29.8056890Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 364 <- wrt source file
2024-11-01T17:51:29.8059392Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0
2024-11-01T17:51:29.8061992Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 399 <- wrt source file
2024-11-01T17:51:29.8064813Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0
2024-11-01T17:51:29.8067380Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 436 <- wrt source file
2024-11-01T17:51:29.8069852Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::tree_map:0
2024-11-01T17:51:29.8072453Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 812 <- wrt source file
2024-11-01T17:51:29.8075186Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0
2024-11-01T17:51:29.8077751Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0, line 933 <- wrt source file
2024-11-01T17:51:29.8080164Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_pytree.py::tree_map:0
2024-11-01T17:51:29.8082933Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file
2024-11-01T17:51:29.8085971Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0
2024-11-01T17:51:29.8089963Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 322 <- wrt source file
2024-11-01T17:51:29.8093274Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0
2024-11-01T17:51:29.8096436Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 354 <- wrt source file
2024-11-01T17:51:29.8099359Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0
2024-11-01T17:51:29.8102236Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0, line 548 <- wrt source file
2024-11-01T17:51:29.8105008Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0
2024-11-01T17:51:29.8108155Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 750 <- wrt source file
2024-11-01T17:51:29.8110968Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0
2024-11-01T17:51:29.8113639Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file
2024-11-01T17:51:29.8116199Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/dlpack.py::from_dlpack:0
2024-11-01T17:51:29.8118975Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 601 <- wrt source file
2024-11-01T17:51:29.8466324Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0
2024-11-01T17:51:29.8469274Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 98 <- wrt source file
2024-11-01T17:51:29.8471974Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::IterableDataset:0
2024-11-01T17:51:29.8474754Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 223 <- wrt source file
2024-11-01T17:51:29.8477689Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::StackDataset:0
2024-11-01T17:51:29.8480342Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0, line 445 <- wrt source file
2024-11-01T17:51:29.8482949Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/dataset.py::random_split:0
2024-11-01T17:51:29.8485565Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0, line 43 <- wrt source file
2024-11-01T17:51:29.8488070Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::Sampler:0
2024-11-01T17:51:29.8490793Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 241 <- wrt source file
2024-11-01T17:51:29.8493643Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0
2024-11-01T17:51:29.8496425Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 304 <- wrt source file
2024-11-01T17:51:29.8499067Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/sampler.py::BatchSampler:0
2024-11-01T17:51:29.8501962Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file
2024-11-01T17:51:29.8504762Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_convert:0
2024-11-01T17:51:29.8507727Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file
2024-11-01T17:51:29.8510376Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::collate:0
2024-11-01T17:51:29.8513131Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file
2024-11-01T17:51:29.8516012Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/_utils/collate.py::default_collate:0
2024-11-01T17:51:29.8518909Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0, line 96 <- wrt source file
2024-11-01T17:51:29.8521836Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0
2024-11-01T17:51:29.8524794Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 263 <- wrt source file
2024-11-01T17:51:29.8527698Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0
2024-11-01T17:51:29.8530795Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 51 <- wrt source file
2024-11-01T17:51:29.8533978Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0
2024-11-01T17:51:29.8537318Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 197 <- wrt source file
2024-11-01T17:51:29.8540530Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0
2024-11-01T17:51:29.8544045Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 87 <- wrt source file
2024-11-01T17:51:29.8547416Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0
2024-11-01T17:51:29.8550755Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 48 <- wrt source file
2024-11-01T17:51:29.8554111Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0
2024-11-01T17:51:29.8557408Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 98 <- wrt source file
2024-11-01T17:51:29.8560645Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0
2024-11-01T17:51:29.8563850Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 317 <- wrt source file
2024-11-01T17:51:29.8566954Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0
2024-11-01T17:51:29.8570420Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 403 <- wrt source file
2024-11-01T17:51:29.8573828Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0
2024-11-01T17:51:29.8577214Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 613 <- wrt source file
2024-11-01T17:51:29.8580577Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0
2024-11-01T17:51:29.8583927Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 681 <- wrt source file
2024-11-01T17:51:29.8587138Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0
2024-11-01T17:51:29.8590481Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file
2024-11-01T17:51:29.8593826Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0
2024-11-01T17:51:29.8597323Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 34 <- wrt source file
2024-11-01T17:51:29.8600683Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0
2024-11-01T17:51:29.8604033Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 62 <- wrt source file
2024-11-01T17:51:29.8607496Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0
2024-11-01T17:51:29.8610782Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 122 <- wrt source file
2024-11-01T17:51:29.8614249Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0
2024-11-01T17:51:29.8617542Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 189 <- wrt source file
2024-11-01T17:51:29.8620757Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0
2024-11-01T17:51:29.8624009Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 36 <- wrt source file
2024-11-01T17:51:29.8627231Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0
2024-11-01T17:51:29.8630638Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 24 <- wrt source file
2024-11-01T17:51:29.8634189Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0
2024-11-01T17:51:29.8637601Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file
2024-11-01T17:51:29.8641072Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0
2024-11-01T17:51:29.8644344Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file
2024-11-01T17:51:29.8647480Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0
2024-11-01T17:51:29.8650827Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 33 <- wrt source file
2024-11-01T17:51:29.8654170Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0
2024-11-01T17:51:29.8657494Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 28 <- wrt source file
2024-11-01T17:51:29.8662380Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0
2024-11-01T17:51:29.8665624Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 72 <- wrt source file
2024-11-01T17:51:29.8668833Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0
2024-11-01T17:51:29.8672070Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 28 <- wrt source file
2024-11-01T17:51:29.8675350Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0
2024-11-01T17:51:29.8678634Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file
2024-11-01T17:51:29.8681892Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0
2024-11-01T17:51:29.8685271Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 36 <- wrt source file
2024-11-01T17:51:29.8688394Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0
2024-11-01T17:51:29.8691576Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file
2024-11-01T17:51:29.8694657Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0
2024-11-01T17:51:29.8697673Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 437 <- wrt source file
2024-11-01T17:51:29.8700586Z * SUCCESS: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0
2024-11-01T17:51:29.8703562Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 533 <- wrt source file
2024-11-01T17:51:29.8706529Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0
2024-11-01T17:51:29.8709741Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file
2024-11-01T17:51:29.8712912Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0
2024-11-01T17:51:29.8716086Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file
2024-11-01T17:51:29.8719145Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0
2024-11-01T17:51:29.8722226Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file
2024-11-01T17:51:29.8725277Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0
2024-11-01T17:51:29.8728362Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file
2024-11-01T17:51:29.8731417Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0
2024-11-01T17:51:29.8734529Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file
2024-11-01T17:51:29.8737567Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0
2024-11-01T17:51:29.8740681Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file
2024-11-01T17:51:29.8743771Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0
2024-11-01T17:51:29.8746949Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file
2024-11-01T17:51:29.8750111Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0
2024-11-01T17:51:29.8753423Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file
2024-11-01T17:51:29.8756517Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0
2024-11-01T17:51:29.8759611Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file
2024-11-01T17:51:29.8762648Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0
2024-11-01T17:51:29.8765699Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file
2024-11-01T17:51:29.8768682Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0
2024-11-01T17:51:29.8771953Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file
2024-11-01T17:51:29.8775058Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0
2024-11-01T17:51:29.8778343Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file
2024-11-01T17:51:29.8781427Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0
2024-11-01T17:51:29.8784744Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file
2024-11-01T17:51:29.8788201Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0
2024-11-01T17:51:29.8791684Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file
2024-11-01T17:51:29.8795161Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0
2024-11-01T17:51:29.8798507Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file
2024-11-01T17:51:29.8801682Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0
2024-11-01T17:51:29.8804826Z * DOCTEST : /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file
2024-11-01T17:51:29.8807986Z * SKIPPED: /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0
2024-11-01T17:51:29.8809568Z ============
2024-11-01T17:51:29.8810094Z Finished doctests
2024-11-01T17:51:29.8810554Z 338 / 704 passed
2024-11-01T17:51:29.8811005Z 
2024-11-01T17:51:29.8811582Z === Found 103 parse-time warnings ===
2024-11-01T17:51:29.8812422Z --- Parse Warning: 1 / 103 ---
2024-11-01T17:51:29.8814997Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=431.
2024-11-01T17:51:29.8817680Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.8819218Z     Creates grids of coordinates specified by the 1D inputs in `attr`:tensors.
2024-11-01T17:51:29.8820139Z     
2024-11-01T17:51:29.8820741Z             This is helpful when you want to visualize data over some
2024-11-01T17:51:29.8821700Z             range of inputs. See below for a plotting example.
2024-11-01T17:51:29.8822435Z     
2024-11-01T17:51:29.8823120Z             Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as
2024-11-01T17:51:29.8824258Z             inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`,
2024-11-01T17:51:29.8825433Z             this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots
2024-11-01T17:51:29.8826573Z             G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where
2024-11-01T17:51:29.8827603Z             the output :math:`G_i` is constructed by expanding :math:`T_i`
2024-11-01T17:51:29.8828461Z             to the result shape.
2024-11-01T17:51:29.8829000Z     
2024-11-01T17:51:29.8829388Z             .. note::
2024-11-01T17:51:29.8830043Z                 0D inputs are treated equivalently to 1D inputs of a
2024-11-01T17:51:29.8830816Z                 single element.
2024-11-01T17:51:29.8831344Z     
2024-11-01T17:51:29.8831732Z             .. warning::
2024-11-01T17:51:29.8832444Z                 `torch.meshgrid(*tensors)` currently has the same behavior
2024-11-01T17:51:29.8833548Z                 as calling `numpy.meshgrid(*arrays, indexing='ij')`.
2024-11-01T17:51:29.8834385Z     
2024-11-01T17:51:29.8834935Z                 In the future `torch.meshgrid` will transition to
2024-11-01T17:51:29.8836051Z                 `indexing='xy'` as the default.
2024-11-01T17:51:29.8836697Z     
2024-11-01T17:51:29.8837292Z                 https://github.com/pytorch/pytorch/issues/50276 tracks
2024-11-01T17:51:29.8838424Z                 this issue with the goal of migrating to NumPy's behavior.
2024-11-01T17:51:29.8839204Z     
2024-11-01T17:51:29.8839596Z             .. seealso::
2024-11-01T17:51:29.8840079Z     
2024-11-01T17:51:29.8840651Z                 :func:`torch.cartesian_prod` has the same effect but it
2024-11-01T17:51:29.8841551Z                 collects the data in a tensor of vectors.
2024-11-01T17:51:29.8842218Z     
2024-11-01T17:51:29.8842589Z             Args:
2024-11-01T17:51:29.8843433Z                 tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be
2024-11-01T17:51:29.8844619Z                     treated as tensors of size :math:`(1,)` automatically
2024-11-01T17:51:29.8845372Z     
2024-11-01T17:51:29.8845953Z                 indexing: (str, optional): the indexing mode, either "xy"
2024-11-01T17:51:29.8846966Z                     or "ij", defaults to "ij". See warning for future changes.
2024-11-01T17:51:29.8847748Z     
2024-11-01T17:51:29.8848312Z                     If "xy" is selected, the first dimension corresponds
2024-11-01T17:51:29.8849266Z                     to the cardinality of the second input and the second
2024-11-01T17:51:29.8850255Z                     dimension corresponds to the cardinality of the first
2024-11-01T17:51:29.8851028Z                     input.
2024-11-01T17:51:29.8851508Z     
2024-11-01T17:51:29.8852056Z                     If "ij" is selected, the dimensions are in the same
2024-11-01T17:51:29.8852931Z                     order as the cardinality of the inputs.
2024-11-01T17:51:29.8853622Z     
2024-11-01T17:51:29.8853983Z             Returns:
2024-11-01T17:51:29.8854627Z                 seq (sequence of Tensors): If the input has :math:`N`
2024-11-01T17:51:29.8855696Z                 tensors of size :math:`S_0 \ldots S_{N-1}``, then the
2024-11-01T17:51:29.8856668Z                 output will also have :math:`N` tensors, where each tensor
2024-11-01T17:51:29.8857686Z                 is of shape :math:`(S_0, ..., S_{N-1})`.
2024-11-01T17:51:29.8858356Z     
2024-11-01T17:51:29.8858730Z             Example::
2024-11-01T17:51:29.8859187Z     
2024-11-01T17:51:29.8859636Z                 >>> x = torch.tensor([1, 2, 3])
2024-11-01T17:51:29.8860512Z                 >>> y = torch.tensor([4, 5, 6])
2024-11-01T17:51:29.8861174Z     
2024-11-01T17:51:29.8861941Z                 Observe the element-wise pairings across the grid, (1, 4),
2024-11-01T17:51:29.8862888Z                 (1, 5), ..., (3, 6). This is the same thing as the
2024-11-01T17:51:29.8863635Z                 cartesian product.
2024-11-01T17:51:29.8864510Z                 >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
2024-11-01T17:51:29.8865281Z                 >>> grid_x
2024-11-01T17:51:29.8865791Z                 tensor([[1, 1, 1],
2024-11-01T17:51:29.8866351Z                         [2, 2, 2],
2024-11-01T17:51:29.8866912Z                         [3, 3, 3]])
2024-11-01T17:51:29.8867467Z                 >>> grid_y
2024-11-01T17:51:29.8867979Z                 tensor([[4, 5, 6],
2024-11-01T17:51:29.8868543Z                         [4, 5, 6],
2024-11-01T17:51:29.8869081Z                         [4, 5, 6]])
2024-11-01T17:51:29.8869635Z     
2024-11-01T17:51:29.8870201Z                 This correspondence can be seen when these grids are
2024-11-01T17:51:29.8871000Z                 stacked properly.
2024-11-01T17:51:29.8871817Z                 >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))),
2024-11-01T17:51:29.8872768Z                 ...             torch.cartesian_prod(x, y))
2024-11-01T17:51:29.8873465Z                 True
2024-11-01T17:51:29.8874037Z     
2024-11-01T17:51:29.8874629Z                 `torch.meshgrid` is commonly used to produce a grid for
2024-11-01T17:51:29.8875422Z                 plotting.
2024-11-01T17:51:29.8876199Z                 >>> # xdoctest: +REQUIRES(module:matplotlib)
2024-11-01T17:51:29.8877002Z                 >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW)
2024-11-01T17:51:29.8877793Z                 >>> import matplotlib.pyplot as plt
2024-11-01T17:51:29.8878690Z                 >>> xs = torch.linspace(-5, 5, steps=100)
2024-11-01T17:51:29.8879569Z                 >>> ys = torch.linspace(-5, 5, steps=100)
2024-11-01T17:51:29.8880490Z                 >>> x, y = torch.meshgrid(xs, ys, indexing='xy')
2024-11-01T17:51:29.8881296Z                 >>> z = torch.sin(torch.sqrt(x * x + y * y))
2024-11-01T17:51:29.8882154Z                 >>> ax = plt.axes(projection='3d')
2024-11-01T17:51:29.8882980Z                 >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
2024-11-01T17:51:29.8883738Z                 >>> plt.show()
2024-11-01T17:51:29.8884250Z     
2024-11-01T17:51:29.8884715Z             .. image:: ../_static/img/meshgrid.png
2024-11-01T17:51:29.8885380Z                 :width: 512
2024-11-01T17:51:29.8885869Z     
2024-11-01T17:51:29.8886248Z             
2024-11-01T17:51:29.8887144Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.8888065Z     
2024-11-01T17:51:29.8888447Z       warnings.warn(msg)
2024-11-01T17:51:29.8888925Z     
2024-11-01T17:51:29.8889458Z --- Parse Warning: 2 / 103 ---
2024-11-01T17:51:29.8892054Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/functional.py line=820.
2024-11-01T17:51:29.8894724Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.8896481Z     unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> Tuple[Tensor, Tensor, Tensor]
2024-11-01T17:51:29.8897654Z     
2024-11-01T17:51:29.8898182Z         Returns the unique elements of the input tensor.
2024-11-01T17:51:29.8898903Z     
2024-11-01T17:51:29.8899767Z         .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that
2024-11-01T17:51:29.8901201Z             this function also eliminates non-consecutive duplicate values.
2024-11-01T17:51:29.8902045Z     
2024-11-01T17:51:29.8902744Z         .. note:: Currently in the CUDA implementation and the CPU implementation,
2024-11-01T17:51:29.8904280Z             `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument.
2024-11-01T17:51:29.8905823Z             Sorting could be slow, so if your input tensor is already sorted, it is recommended to use
2024-11-01T17:51:29.8907354Z             :func:`torch.unique_consecutive` which avoids the sorting.
2024-11-01T17:51:29.8908140Z     
2024-11-01T17:51:29.8908524Z         Args:
2024-11-01T17:51:29.8909002Z             input (Tensor): the input tensor
2024-11-01T17:51:29.8909948Z             sorted (bool): Whether to sort the unique elements in ascending order
2024-11-01T17:51:29.8910893Z                 before returning as output.
2024-11-01T17:51:29.8911806Z             return_inverse (bool): Whether to also return the indices for where
2024-11-01T17:51:29.8912983Z                 elements in the original input ended up in the returned unique list.
2024-11-01T17:51:29.8914281Z             return_counts (bool): Whether to also return the counts for each unique
2024-11-01T17:51:29.8915185Z                 element.
2024-11-01T17:51:29.8915967Z             dim (int, optional): the dimension to operate upon. If ``None``, the
2024-11-01T17:51:29.8917137Z                 unique of the flattened input is returned. Otherwise, each of the
2024-11-01T17:51:29.8918282Z                 tensors indexed by the given dimension is treated as one of the
2024-11-01T17:51:29.8919434Z                 elements to apply the unique operation upon. See examples for more
2024-11-01T17:51:29.8920365Z                 details. Default: ``None``
2024-11-01T17:51:29.8920952Z     
2024-11-01T17:51:29.8921322Z         Returns:
2024-11-01T17:51:29.8922422Z             (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing
2024-11-01T17:51:29.8923442Z     
2024-11-01T17:51:29.8924230Z                 - **output** (*Tensor*): the output list of unique scalar elements.
2024-11-01T17:51:29.8925289Z                 - **inverse_indices** (*Tensor*): (optional) if
2024-11-01T17:51:29.8926226Z                   :attr:`return_inverse` is True, there will be an additional
2024-11-01T17:51:29.8927294Z                   returned tensor (same shape as input) representing the indices
2024-11-01T17:51:29.8928410Z                   for where elements in the original input map to in the output;
2024-11-01T17:51:29.8929487Z                   otherwise, this function will only return a single tensor.
2024-11-01T17:51:29.8930485Z                 - **counts** (*Tensor*): (optional) if
2024-11-01T17:51:29.8931359Z                   :attr:`return_counts` is True, there will be an additional
2024-11-01T17:51:29.8932394Z                   returned tensor (same shape as output or output.size(dim),
2024-11-01T17:51:29.8933459Z                   if dim was specified) representing the number of occurrences
2024-11-01T17:51:29.8934373Z                   for each unique value or tensor.
2024-11-01T17:51:29.8935018Z     
2024-11-01T17:51:29.8935408Z         Example::
2024-11-01T17:51:29.8935826Z     
2024-11-01T17:51:29.8936484Z             >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
2024-11-01T17:51:29.8937354Z             >>> output
2024-11-01T17:51:29.8937835Z             tensor([1, 2, 3])
2024-11-01T17:51:29.8938344Z     
2024-11-01T17:51:29.8938810Z             >>> output, inverse_indices = torch.unique(
2024-11-01T17:51:29.8939857Z             ...     torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
2024-11-01T17:51:29.8940800Z             >>> output
2024-11-01T17:51:29.8941277Z             tensor([1, 2, 3])
2024-11-01T17:51:29.8941813Z             >>> inverse_indices
2024-11-01T17:51:29.8942363Z             tensor([0, 2, 1, 2])
2024-11-01T17:51:29.8942895Z     
2024-11-01T17:51:29.8943370Z             >>> output, inverse_indices = torch.unique(
2024-11-01T17:51:29.8944426Z             ...     torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
2024-11-01T17:51:29.8945369Z             >>> output
2024-11-01T17:51:29.8946026Z             tensor([1, 2, 3])
2024-11-01T17:51:29.8946553Z             >>> inverse_indices
2024-11-01T17:51:29.8947109Z             tensor([[0, 2],
2024-11-01T17:51:29.8947627Z                     [1, 2]])
2024-11-01T17:51:29.8948119Z     
2024-11-01T17:51:29.8948525Z             >>> a = torch.tensor([
2024-11-01T17:51:29.8949078Z             ...     [
2024-11-01T17:51:29.8949555Z             ...         [1, 1, 0, 0],
2024-11-01T17:51:29.8950137Z             ...         [1, 1, 0, 0],
2024-11-01T17:51:29.8950725Z             ...         [0, 0, 1, 1],
2024-11-01T17:51:29.8951284Z             ...     ],
2024-11-01T17:51:29.8951741Z             ...     [
2024-11-01T17:51:29.8952208Z             ...         [0, 0, 1, 1],
2024-11-01T17:51:29.8952791Z             ...         [0, 0, 1, 1],
2024-11-01T17:51:29.8953372Z             ...         [1, 1, 1, 1],
2024-11-01T17:51:29.8954036Z             ...     ],
2024-11-01T17:51:29.8954487Z             ...     [
2024-11-01T17:51:29.8954960Z             ...         [1, 1, 0, 0],
2024-11-01T17:51:29.8955556Z             ...         [1, 1, 0, 0],
2024-11-01T17:51:29.8956146Z             ...         [0, 0, 1, 1],
2024-11-01T17:51:29.8956708Z             ...     ],
2024-11-01T17:51:29.8957154Z             ... ])
2024-11-01T17:51:29.8957574Z     
2024-11-01T17:51:29.8958278Z             >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]`
2024-11-01T17:51:29.8959491Z             >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match
2024-11-01T17:51:29.8960511Z             >>> # each other, so one of them will be removed.
2024-11-01T17:51:29.8961373Z             >>> (a[0, :, :] == a[2, :, :]).all()
2024-11-01T17:51:29.8962006Z             tensor(True)
2024-11-01T17:51:29.8962597Z             >>> a_unique_dim0 = torch.unique(a, dim=0)
2024-11-01T17:51:29.8963288Z             >>> a_unique_dim0
2024-11-01T17:51:29.8963837Z             tensor([[[0, 0, 1, 1],
2024-11-01T17:51:29.8964410Z                      [0, 0, 1, 1],
2024-11-01T17:51:29.8964971Z                      [1, 1, 1, 1]],
2024-11-01T17:51:29.8965536Z                     [[1, 1, 0, 0],
2024-11-01T17:51:29.8966093Z                      [1, 1, 0, 0],
2024-11-01T17:51:29.8966652Z                      [0, 0, 1, 1]]])
2024-11-01T17:51:29.8967201Z     
2024-11-01T17:51:29.8968019Z             >>> # Notice which sub-tensors from `a` match with the sub-tensors from
2024-11-01T17:51:29.8968931Z             >>> # `a_unique_dim0`:
2024-11-01T17:51:29.8969601Z             >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all()
2024-11-01T17:51:29.8970309Z             tensor(True)
2024-11-01T17:51:29.8970908Z             >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all()
2024-11-01T17:51:29.8971605Z             tensor(True)
2024-11-01T17:51:29.8972067Z     
2024-11-01T17:51:29.8972728Z             >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are
2024-11-01T17:51:29.8973867Z             >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of
2024-11-01T17:51:29.8974743Z             >>> # them will be removed.
2024-11-01T17:51:29.8975402Z             >>> (a[:, 0, :] == a[:, 1, :]).all()
2024-11-01T17:51:29.8976032Z             tensor(True)
2024-11-01T17:51:29.8976558Z             >>> torch.unique(a, dim=1)
2024-11-01T17:51:29.8977174Z             tensor([[[0, 0, 1, 1],
2024-11-01T17:51:29.8977741Z                      [1, 1, 0, 0]],
2024-11-01T17:51:29.8978305Z                     [[1, 1, 1, 1],
2024-11-01T17:51:29.8978849Z                      [0, 0, 1, 1]],
2024-11-01T17:51:29.8979416Z                     [[0, 0, 1, 1],
2024-11-01T17:51:29.8979980Z                      [1, 1, 0, 0]]])
2024-11-01T17:51:29.8980529Z     
2024-11-01T17:51:29.8981212Z             >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared.
2024-11-01T17:51:29.8982323Z             >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and
2024-11-01T17:51:29.8983344Z             >>> # `a[:, :, 3]` match each other as well. So in this case, two of the
2024-11-01T17:51:29.8984442Z             >>> # sub-tensors will be removed.
2024-11-01T17:51:29.8985146Z             >>> (a[:, :, 0] == a[:, :, 1]).all()
2024-11-01T17:51:29.8985787Z             tensor(True)
2024-11-01T17:51:29.8986333Z             >>> (a[:, :, 2] == a[:, :, 3]).all()
2024-11-01T17:51:29.8986952Z             tensor(True)
2024-11-01T17:51:29.8987490Z             >>> torch.unique(a, dim=2)
2024-11-01T17:51:29.8988102Z             tensor([[[0, 1],
2024-11-01T17:51:29.8988620Z                      [0, 1],
2024-11-01T17:51:29.8989128Z                      [1, 0]],
2024-11-01T17:51:29.8989643Z                     [[1, 0],
2024-11-01T17:51:29.8990147Z                      [1, 0],
2024-11-01T17:51:29.8990650Z                      [1, 1]],
2024-11-01T17:51:29.8991165Z                     [[0, 1],
2024-11-01T17:51:29.8991662Z                      [0, 1],
2024-11-01T17:51:29.8992173Z                      [1, 0]]])
2024-11-01T17:51:29.8992672Z         
2024-11-01T17:51:29.8993551Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.8994568Z     
2024-11-01T17:51:29.8994967Z       warnings.warn(msg)
2024-11-01T17:51:29.8995453Z     
2024-11-01T17:51:29.8995988Z --- Parse Warning: 3 / 103 ---
2024-11-01T17:51:29.8998431Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=560.
2024-11-01T17:51:29.9001000Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9002036Z     
2024-11-01T17:51:29.9002592Z     Load a model from a github repo or a local directory.
2024-11-01T17:51:29.9003334Z     
2024-11-01T17:51:29.9004034Z     Note: Loading a model is the typical use case, but this can also be used to
2024-11-01T17:51:29.9005243Z     for loading other objects such as tokenizers, loss functions, etc.
2024-11-01T17:51:29.9006087Z     
2024-11-01T17:51:29.9007093Z     If ``source`` is 'github', ``repo_or_dir`` is expected to be
2024-11-01T17:51:29.9008077Z     of the form ``repo_owner/repo_name[:ref]`` with an optional
2024-11-01T17:51:29.9008874Z     ref (a tag or a branch).
2024-11-01T17:51:29.9009398Z     
2024-11-01T17:51:29.9010095Z     If ``source`` is 'local', ``repo_or_dir`` is expected to be a
2024-11-01T17:51:29.9010921Z     path to a local directory.
2024-11-01T17:51:29.9011457Z     
2024-11-01T17:51:29.9011813Z     Args:
2024-11-01T17:51:29.9012403Z         repo_or_dir (str): If ``source`` is 'github',
2024-11-01T17:51:29.9013550Z             this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with
2024-11-01T17:51:29.9015266Z             an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified,
2024-11-01T17:51:29.9016766Z             the default branch is assumed to be ``main`` if it exists, and otherwise ``master``.
2024-11-01T17:51:29.9018197Z             If ``source`` is 'local'  then it should be a path to a local directory.
2024-11-01T17:51:29.9019321Z         model (str): the name of a callable (entrypoint) defined in the
2024-11-01T17:51:29.9020281Z             repo/dir's ``hubconf.py``.
2024-11-01T17:51:29.9021128Z         *args (optional): the corresponding args for callable ``model``.
2024-11-01T17:51:29.9022282Z         source (str, optional): 'github' or 'local'. Specifies how
2024-11-01T17:51:29.9023375Z             ``repo_or_dir`` is to be interpreted. Default is 'github'.
2024-11-01T17:51:29.9024469Z         trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``.
2024-11-01T17:51:29.9025687Z             This parameter was introduced in v1.12 and helps ensuring that users
2024-11-01T17:51:29.9026686Z             only run code from repos that they trust.
2024-11-01T17:51:29.9027338Z     
2024-11-01T17:51:29.9028090Z             - If ``False``, a prompt will ask the user whether the repo should
2024-11-01T17:51:29.9028928Z               be trusted.
2024-11-01T17:51:29.9029972Z             - If ``True``, the repo will be added to the trusted list and loaded
2024-11-01T17:51:29.9030936Z               without requiring explicit confirmation.
2024-11-01T17:51:29.9031959Z             - If ``"check"``, the repo will be checked against the list of
2024-11-01T17:51:29.9033022Z               trusted repos in the cache. If it is not present in that list, the
2024-11-01T17:51:29.9034263Z               behaviour will fall back onto the ``trust_repo=False`` option.
2024-11-01T17:51:29.9035460Z             - If ``None``: this will raise a warning, inviting the user to set
2024-11-01T17:51:29.9036547Z               ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This
2024-11-01T17:51:29.9037661Z               is only present for backward compatibility and will be removed in
2024-11-01T17:51:29.9038528Z               v2.0.
2024-11-01T17:51:29.9038935Z     
2024-11-01T17:51:29.9039603Z             Default is ``None`` and will eventually change to ``"check"`` in v2.0.
2024-11-01T17:51:29.9040766Z         force_reload (bool, optional): whether to force a fresh download of
2024-11-01T17:51:29.9041871Z             the github repo unconditionally. Does not have any effect if
2024-11-01T17:51:29.9042898Z             ``source = 'local'``. Default is ``False``.
2024-11-01T17:51:29.9043842Z         verbose (bool, optional): If ``False``, mute messages about hitting
2024-11-01T17:51:29.9044995Z             local caches. Note that the message about first download cannot be
2024-11-01T17:51:29.9046174Z             muted. Does not have any effect if ``source = 'local'``.
2024-11-01T17:51:29.9047117Z             Default is ``True``.
2024-11-01T17:51:29.9048128Z         skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit
2024-11-01T17:51:29.9079074Z             specified by the ``github`` argument properly belongs to the repo owner. This will make
2024-11-01T17:51:29.9080820Z             requests to the GitHub API; you can specify a non-default GitHub token by setting the
2024-11-01T17:51:29.9082098Z             ``GITHUB_TOKEN`` environment variable. Default is ``False``.
2024-11-01T17:51:29.9083193Z         **kwargs (optional): the corresponding kwargs for callable ``model``.
2024-11-01T17:51:29.9084059Z     
2024-11-01T17:51:29.9084431Z     Returns:
2024-11-01T17:51:29.9085087Z         The output of the ``model`` callable when called with the given
2024-11-01T17:51:29.9085953Z         ``*args`` and ``**kwargs``.
2024-11-01T17:51:29.9086512Z     
2024-11-01T17:51:29.9086891Z     Example:
2024-11-01T17:51:29.9087443Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2024-11-01T17:51:29.9088154Z         >>> # from a github repo
2024-11-01T17:51:29.9088744Z         >>> repo = "pytorch/vision"
2024-11-01T17:51:29.9089367Z         >>> model = torch.hub.load(
2024-11-01T17:51:29.9090174Z         ...     repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1"
2024-11-01T17:51:29.9090971Z         ... )
2024-11-01T17:51:29.9091429Z         >>> # from a local directory
2024-11-01T17:51:29.9092106Z         >>> path = "/some/local/path/pytorch/vision"
2024-11-01T17:51:29.9092817Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:29.9093709Z         >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT")
2024-11-01T17:51:29.9094649Z     
2024-11-01T17:51:29.9095515Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9096422Z     
2024-11-01T17:51:29.9096815Z       warnings.warn(msg)
2024-11-01T17:51:29.9097295Z     
2024-11-01T17:51:29.9097841Z --- Parse Warning: 4 / 103 ---
2024-11-01T17:51:29.9100443Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=687.
2024-11-01T17:51:29.9103110Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9104404Z     Download object at the given URL to a local path.
2024-11-01T17:51:29.9105118Z     
2024-11-01T17:51:29.9105489Z         Args:
2024-11-01T17:51:29.9106011Z             url (str): URL of the object to download
2024-11-01T17:51:29.9107306Z             dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file``
2024-11-01T17:51:29.9108804Z             hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``.
2024-11-01T17:51:29.9109960Z                 Default: None
2024-11-01T17:51:29.9110855Z             progress (bool, optional): whether or not to display a progress bar to stderr
2024-11-01T17:51:29.9111826Z                 Default: True
2024-11-01T17:51:29.9112333Z     
2024-11-01T17:51:29.9112706Z         Example:
2024-11-01T17:51:29.9113256Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2024-11-01T17:51:29.9114098Z             >>> # xdoctest: +REQUIRES(POSIX)
2024-11-01T17:51:29.9114815Z             >>> torch.hub.download_url_to_file(
2024-11-01T17:51:29.9115930Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
2024-11-01T17:51:29.9116861Z             ...     "/tmp/temporary_file",
2024-11-01T17:51:29.9117460Z             ... )
2024-11-01T17:51:29.9117879Z     
2024-11-01T17:51:29.9118243Z         
2024-11-01T17:51:29.9119106Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9120031Z     
2024-11-01T17:51:29.9120410Z       warnings.warn(msg)
2024-11-01T17:51:29.9120891Z     
2024-11-01T17:51:29.9121432Z --- Parse Warning: 5 / 103 ---
2024-11-01T17:51:29.9124269Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/hub.py line=812.
2024-11-01T17:51:29.9127093Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9128206Z     Loads the Torch serialized object at the given URL.
2024-11-01T17:51:29.9128925Z     
2024-11-01T17:51:29.9129516Z         If downloaded file is a zip file, it will be automatically
2024-11-01T17:51:29.9130328Z         decompressed.
2024-11-01T17:51:29.9130784Z     
2024-11-01T17:51:29.9131578Z         If the object is already present in `model_dir`, it's deserialized and
2024-11-01T17:51:29.9132469Z         returned.
2024-11-01T17:51:29.9133201Z         The default value of ``model_dir`` is ``<hub_dir>/checkpoints`` where
2024-11-01T17:51:29.9134339Z         ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`.
2024-11-01T17:51:29.9135160Z     
2024-11-01T17:51:29.9135526Z         Args:
2024-11-01T17:51:29.9136043Z             url (str): URL of the object to download
2024-11-01T17:51:29.9136987Z             model_dir (str, optional): directory in which to save the object
2024-11-01T17:51:29.9138407Z             map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
2024-11-01T17:51:29.9139960Z             progress (bool, optional): whether or not to display a progress bar to stderr.
2024-11-01T17:51:29.9140943Z                 Default: True
2024-11-01T17:51:29.9142028Z             check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
2024-11-01T17:51:29.9143587Z                 ``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
2024-11-01T17:51:29.9144820Z                 digits of the SHA256 hash of the contents of the file. The hash is used to
2024-11-01T17:51:29.9146000Z                 ensure unique names and to verify the contents of the file.
2024-11-01T17:51:29.9146821Z                 Default: False
2024-11-01T17:51:29.9147924Z             file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set.
2024-11-01T17:51:29.9149597Z             weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects.
2024-11-01T17:51:29.9151232Z                 Recommended for untrusted sources. See :func:`~torch.load` for more details.
2024-11-01T17:51:29.9152174Z     
2024-11-01T17:51:29.9152544Z         Example:
2024-11-01T17:51:29.9153088Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB)
2024-11-01T17:51:29.9154045Z             >>> state_dict = torch.hub.load_state_dict_from_url(
2024-11-01T17:51:29.9155213Z             ...     "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth"
2024-11-01T17:51:29.9156077Z             ... )
2024-11-01T17:51:29.9156490Z     
2024-11-01T17:51:29.9156834Z         
2024-11-01T17:51:29.9157699Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9158608Z     
2024-11-01T17:51:29.9158985Z       warnings.warn(msg)
2024-11-01T17:51:29.9159456Z     
2024-11-01T17:51:29.9159970Z --- Parse Warning: 6 / 103 ---
2024-11-01T17:51:29.9162562Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=367.
2024-11-01T17:51:29.9165242Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:29.9166493Z     Registers the function implementation as the fallback for the given key.
2024-11-01T17:51:29.9167375Z     
2024-11-01T17:51:29.9168035Z             This function only works for a library with global namespace ("_").
2024-11-01T17:51:29.9168900Z     
2024-11-01T17:51:29.9169257Z             Args:
2024-11-01T17:51:29.9170327Z                 fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel`
2024-11-01T17:51:29.9171432Z                     to register a fallthrough.
2024-11-01T17:51:29.9172601Z                 dispatch_key: dispatch key that the input function should be registered for. By default, it uses
2024-11-01T17:51:29.9173895Z                               the dispatch key that the library was created with.
2024-11-01T17:51:29.9175287Z                 with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument
2024-11-01T17:51:29.9177056Z                              to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls.
2024-11-01T17:51:29.9178184Z     
2024-11-01T17:51:29.9178590Z             Example::
2024-11-01T17:51:29.9179106Z                 >>> my_lib = Library("_", "IMPL")
2024-11-01T17:51:29.9179871Z                 >>> def fallback_kernel(op, *args, **kwargs):
2024-11-01T17:51:29.9180693Z                 >>>     # Handle all autocast ops generically
2024-11-01T17:51:29.9181401Z                 >>>     # ...
2024-11-01T17:51:29.9182048Z                 >>> my_lib.fallback(fallback_kernel, "Autocast")
2024-11-01T17:51:29.9182760Z             
2024-11-01T17:51:29.9184591Z     Original Error: IndentationError('expected an indented block after function definition on line 2', ('<source_block>', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7))
2024-11-01T17:51:29.9186298Z     
2024-11-01T17:51:29.9186761Z     my_lib.fallback(fallback_kernel, "Autocast")
2024-11-01T17:51:29.9187415Z     ^
2024-11-01T17:51:29.9187817Z       warnings.warn(msg)
2024-11-01T17:51:29.9188288Z     
2024-11-01T17:51:29.9188824Z --- Parse Warning: 7 / 103 ---
2024-11-01T17:51:29.9191376Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=732.
2024-11-01T17:51:29.9194109Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:29.9195344Z     Register a FakeTensor implementation ("fake impl") for this operator.
2024-11-01T17:51:29.9196218Z     
2024-11-01T17:51:29.9196806Z         Also sometimes known as a "meta kernel", "abstract impl".
2024-11-01T17:51:29.9197669Z     
2024-11-01T17:51:29.9198371Z         An "FakeTensor implementation" specifies the behavior of this operator on
2024-11-01T17:51:29.9199621Z         Tensors that carry no data ("FakeTensor"). Given some input Tensors with
2024-11-01T17:51:29.9200851Z         certain properties (sizes/strides/storage_offset/device), it specifies
2024-11-01T17:51:29.9201888Z         what the properties of the output Tensors are.
2024-11-01T17:51:29.9202581Z     
2024-11-01T17:51:29.9203277Z         The FakeTensor implementation has the same signature as the operator.
2024-11-01T17:51:29.9204481Z         It is run for both FakeTensors and meta tensors. To write a FakeTensor
2024-11-01T17:51:29.9205650Z         implementation, assume that all Tensor inputs to the operator are
2024-11-01T17:51:29.9206998Z         regular CPU/CUDA/Meta tensors, but they do not have storage, and
2024-11-01T17:51:29.9208136Z         you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
2024-11-01T17:51:29.9209319Z         The FakeTensor implementation must consist of only PyTorch operations
2024-11-01T17:51:29.9210491Z         (and may not directly access the storage or data of any input or
2024-11-01T17:51:29.9211358Z         intermediate Tensors).
2024-11-01T17:51:29.9211888Z     
2024-11-01T17:51:29.9212427Z         This API may be used as a decorator (see examples).
2024-11-01T17:51:29.9213155Z     
2024-11-01T17:51:29.9213664Z         For a detailed guide on custom ops, please see
2024-11-01T17:51:29.9214691Z         https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
2024-11-01T17:51:29.9215548Z     
2024-11-01T17:51:29.9216084Z         Examples:
2024-11-01T17:51:29.9216544Z             >>> import torch
2024-11-01T17:51:29.9217088Z             >>> import numpy as np
2024-11-01T17:51:29.9217704Z             >>> from torch import Tensor
2024-11-01T17:51:29.9218291Z             >>>
2024-11-01T17:51:29.9219108Z             >>> # Example 1: an operator without data-dependent output shape
2024-11-01T17:51:29.9220240Z             >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=())
2024-11-01T17:51:29.9221536Z             >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor:
2024-11-01T17:51:29.9222632Z             >>>     raise NotImplementedError("Implementation goes here")
2024-11-01T17:51:29.9223423Z             >>>
2024-11-01T17:51:29.9224028Z             >>> @torch.library.register_fake("mylib::custom_linear")
2024-11-01T17:51:29.9224827Z             >>> def _(x, weight, bias):
2024-11-01T17:51:29.9225468Z             >>>     assert x.dim() == 2
2024-11-01T17:51:29.9226131Z             >>>     assert weight.dim() == 2
2024-11-01T17:51:29.9226810Z             >>>     assert bias.dim() == 1
2024-11-01T17:51:29.9227519Z             >>>     assert x.shape[1] == weight.shape[1]
2024-11-01T17:51:29.9228315Z             >>>     assert weight.shape[0] == bias.shape[0]
2024-11-01T17:51:29.9229097Z             >>>     assert x.device == weight.device
2024-11-01T17:51:29.9229747Z             >>>
2024-11-01T17:51:29.9230248Z             >>>     return (x @ weight.t()) + bias
2024-11-01T17:51:29.9230889Z             >>>
2024-11-01T17:51:29.9231504Z             >>> with torch._subclasses.fake_tensor.FakeTensorMode():
2024-11-01T17:51:29.9232328Z             >>>     x = torch.randn(2, 3)
2024-11-01T17:51:29.9232979Z             >>>     w = torch.randn(3, 3)
2024-11-01T17:51:29.9233618Z             >>>     b = torch.randn(3)
2024-11-01T17:51:29.9234420Z             >>>     y = torch.ops.mylib.custom_linear(x, w, b)
2024-11-01T17:51:29.9235112Z             >>>
2024-11-01T17:51:29.9235581Z             >>> assert y.shape == (2, 3)
2024-11-01T17:51:29.9236191Z             >>>
2024-11-01T17:51:29.9236949Z             >>> # Example 2: an operator with data-dependent output shape
2024-11-01T17:51:29.9238049Z             >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=())
2024-11-01T17:51:29.9239134Z             >>> def custom_nonzero(x: Tensor) -> Tensor:
2024-11-01T17:51:29.9240022Z             >>>     x_np = x.numpy(force=True)
2024-11-01T17:51:29.9240777Z             >>>     res = np.stack(np.nonzero(x_np), axis=1)
2024-11-01T17:51:29.9241589Z             >>>     return torch.tensor(res, device=x.device)
2024-11-01T17:51:29.9242285Z             >>>
2024-11-01T17:51:29.9242907Z             >>> @torch.library.register_fake("mylib::custom_nonzero")
2024-11-01T17:51:29.9243677Z             >>> def _(x):
2024-11-01T17:51:29.9244434Z             >>> # Number of nonzero-elements is data-dependent.
2024-11-01T17:51:29.9245337Z             >>> # Since we cannot peek at the data in an fake impl,
2024-11-01T17:51:29.9246295Z             >>> # we use the ctx object to construct a new symint that
2024-11-01T17:51:29.9247261Z             >>> # represents the data-dependent size.
2024-11-01T17:51:29.9248026Z             >>>     ctx = torch.library.get_ctx()
2024-11-01T17:51:29.9248737Z             >>>     nnz = ctx.new_dynamic_size()
2024-11-01T17:51:29.9249432Z             >>>     shape = [nnz, x.dim()]
2024-11-01T17:51:29.9250207Z             >>>     result = x.new_empty(shape, dtype=torch.int64)
2024-11-01T17:51:29.9250962Z             >>>     return result
2024-11-01T17:51:29.9251505Z             >>>
2024-11-01T17:51:29.9252129Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2024-11-01T17:51:29.9252930Z             >>>
2024-11-01T17:51:29.9253435Z             >>> x = torch.tensor([0, 1, 2, 3, 4, 0])
2024-11-01T17:51:29.9254454Z             >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x)
2024-11-01T17:51:29.9255447Z             >>> trace.print_readable()
2024-11-01T17:51:29.9256125Z             >>>
2024-11-01T17:51:29.9256844Z             >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x))
2024-11-01T17:51:29.9257717Z     
2024-11-01T17:51:29.9258084Z         
2024-11-01T17:51:29.9259652Z     Original Error: IndentationError('expected an indented block after function definition on line 37', ('<source_block>', 38, 1, '_._ = None\n', 38, 2))
2024-11-01T17:51:29.9261150Z     
2024-11-01T17:51:29.9261507Z     _._ = None
2024-11-01T17:51:29.9261913Z     ^
2024-11-01T17:51:29.9262317Z       warnings.warn(msg)
2024-11-01T17:51:29.9262802Z     
2024-11-01T17:51:29.9263341Z --- Parse Warning: 8 / 103 ---
2024-11-01T17:51:29.9265929Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=853.
2024-11-01T17:51:29.9268651Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9269738Z     Register a backward formula for this custom op.
2024-11-01T17:51:29.9270439Z     
2024-11-01T17:51:29.9271114Z         In order for an operator to work with autograd, you need to register
2024-11-01T17:51:29.9272015Z         a backward formula:
2024-11-01T17:51:29.9272824Z         1. You must tell us how to compute gradients during the backward pass
2024-11-01T17:51:29.9273805Z         by providing us a "backward" function.
2024-11-01T17:51:29.9274880Z         2. If you need any values from the forward to compute gradients, you can
2024-11-01T17:51:29.9275922Z         use `setup_context` to save values for backward.
2024-11-01T17:51:29.9276623Z     
2024-11-01T17:51:29.9277318Z         ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``:
2024-11-01T17:51:29.9278596Z         - ``grads`` is one or more gradients. The number of gradients matches
2024-11-01T17:51:29.9279546Z         the number of outputs of the operator.
2024-11-01T17:51:29.9280542Z         The ``ctx`` object is `the same ctx object <context_method_mixins>`_ used by
2024-11-01T17:51:29.9281795Z         :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the
2024-11-01T17:51:29.9282893Z         same as :meth:`torch.autograd.Function.backward`.
2024-11-01T17:51:29.9283625Z     
2024-11-01T17:51:29.9284258Z         ``setup_context(ctx, inputs, output)`` runs during the forward pass.
2024-11-01T17:51:29.9285551Z         Please save quantities needed for backward onto the ``ctx`` object via
2024-11-01T17:51:29.9286776Z         either :meth:`torch.autograd.function.FunctionCtx.save_for_backward`
2024-11-01T17:51:29.9287948Z         or assigning them as attributes of ``ctx``. If your custom op has
2024-11-01T17:51:29.9289202Z         kwarg-only arguments, we expect the signature of ``setup_context``
2024-11-01T17:51:29.9290324Z         to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``.
2024-11-01T17:51:29.9291133Z     
2024-11-01T17:51:29.9291823Z         Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is,
2024-11-01T17:51:29.9293043Z         they may not directly access :meth:`torch.Tensor.data_ptr` and they must
2024-11-01T17:51:29.9294445Z         not depend on or mutate global state. If you need a non-traceable backward,
2024-11-01T17:51:29.9295729Z         you can make it a separate custom_op that you call inside ``backward_fn``.
2024-11-01T17:51:29.9296648Z     
2024-11-01T17:51:29.9297010Z         Examples:
2024-11-01T17:51:29.9297463Z             >>> import torch
2024-11-01T17:51:29.9298009Z             >>> import numpy as np
2024-11-01T17:51:29.9298621Z             >>> from torch import Tensor
2024-11-01T17:51:29.9299222Z             >>>
2024-11-01T17:51:29.9299906Z             >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=())
2024-11-01T17:51:29.9300916Z             >>> def numpy_sin(x: Tensor) -> Tensor:
2024-11-01T17:51:29.9301628Z             >>>     x_np = x.cpu().numpy()
2024-11-01T17:51:29.9302382Z             >>>     y_np = np.sin(x_np)
2024-11-01T17:51:29.9303155Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2024-11-01T17:51:29.9303889Z             >>>
2024-11-01T17:51:29.9304562Z             >>> def setup_context(ctx, inputs, output) -> Tensor:
2024-11-01T17:51:29.9305306Z             >>>     x, = inputs
2024-11-01T17:51:29.9305909Z             >>>     ctx.save_for_backward(x)
2024-11-01T17:51:29.9306733Z             >>>
2024-11-01T17:51:29.9307212Z             >>> def backward(ctx, grad):
2024-11-01T17:51:29.9307851Z             >>>     x, = ctx.saved_tensors
2024-11-01T17:51:29.9308511Z             >>>     return grad * x.cos()
2024-11-01T17:51:29.9309113Z             >>>
2024-11-01T17:51:29.9309619Z             >>> torch.library.register_autograd(
2024-11-01T17:51:29.9310504Z             ...     "mylib::numpy_sin", backward, setup_context=setup_context
2024-11-01T17:51:29.9311281Z             ... )
2024-11-01T17:51:29.9311708Z             >>>
2024-11-01T17:51:29.9312236Z             >>> x = torch.randn(3, requires_grad=True)
2024-11-01T17:51:29.9312936Z             >>> y = numpy_sin(x)
2024-11-01T17:51:29.9313682Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2024-11-01T17:51:29.9314650Z             >>> assert torch.allclose(grad_x, x.cos())
2024-11-01T17:51:29.9315307Z             >>>
2024-11-01T17:51:29.9315924Z             >>> # Example with a keyword-only arg
2024-11-01T17:51:29.9316855Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2024-11-01T17:51:29.9317958Z             >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor:
2024-11-01T17:51:29.9318745Z             >>>     x_np = x.cpu().numpy()
2024-11-01T17:51:29.9319388Z             >>>     y_np = x_np * val
2024-11-01T17:51:29.9320139Z             >>>     return torch.from_numpy(y_np).to(device=x.device)
2024-11-01T17:51:29.9320878Z             >>>
2024-11-01T17:51:29.9321737Z             >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor:
2024-11-01T17:51:29.9322741Z             >>>     ctx.val = keyword_only_inputs["val"]
2024-11-01T17:51:29.9323417Z             >>>
2024-11-01T17:51:29.9323871Z             >>> def backward(ctx, grad):
2024-11-01T17:51:29.9324520Z             >>>     return grad * ctx.val
2024-11-01T17:51:29.9325122Z             >>>
2024-11-01T17:51:29.9325639Z             >>> torch.library.register_autograd(
2024-11-01T17:51:29.9326699Z             ...     "mylib::numpy_mul", backward, setup_context=setup_context
2024-11-01T17:51:29.9327467Z             ... )
2024-11-01T17:51:29.9327895Z             >>>
2024-11-01T17:51:29.9328415Z             >>> x = torch.randn(3, requires_grad=True)
2024-11-01T17:51:29.9329140Z             >>> y = numpy_mul(x, val=3.14)
2024-11-01T17:51:29.9329955Z             >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y))
2024-11-01T17:51:29.9330945Z             >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14))
2024-11-01T17:51:29.9331705Z     
2024-11-01T17:51:29.9332067Z         
2024-11-01T17:51:29.9332948Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9333870Z     
2024-11-01T17:51:29.9334262Z       warnings.warn(msg)
2024-11-01T17:51:29.9334729Z     
2024-11-01T17:51:29.9335272Z --- Parse Warning: 9 / 103 ---
2024-11-01T17:51:29.9337776Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py line=1261.
2024-11-01T17:51:29.9340422Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9341675Z     Given an operator and some sample arguments, tests if the operator is
2024-11-01T17:51:29.9342582Z         registered correctly.
2024-11-01T17:51:29.9343092Z     
2024-11-01T17:51:29.9343781Z         That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
2024-11-01T17:51:29.9345150Z         custom op, you specified metadata (e.g. mutability info) about the custom op
2024-11-01T17:51:29.9346432Z         and these APIs require that the functions you pass them satisfy certain
2024-11-01T17:51:29.9347667Z         properties (e.g. no data pointer access in the fake/meta/abstract kernel)
2024-11-01T17:51:29.9348719Z         ``opcheck`` tests these metadata and properties.
2024-11-01T17:51:29.9349416Z     
2024-11-01T17:51:29.9349857Z         Concretely, we test the following:
2024-11-01T17:51:29.9350483Z     
2024-11-01T17:51:29.9351168Z         - test_schema: If the schema matches the implementation of
2024-11-01T17:51:29.9352284Z           the operator. For example: if the schema specifies a Tensor is mutated,
2024-11-01T17:51:29.9353462Z           then we check the implementation mutates the Tensor. If the schema
2024-11-01T17:51:29.9354667Z           specifies that we return a new Tensor, then we check that the
2024-11-01T17:51:29.9355788Z           implementation returns a new Tensor (instead of an existing one or
2024-11-01T17:51:29.9356709Z           a view of an existing one).
2024-11-01T17:51:29.9357667Z         - test_autograd_registration: If the operator supports training
2024-11-01T17:51:29.9358754Z           (autograd): we check that its autograd formula is registered via
2024-11-01T17:51:29.9359858Z           torch.library.register_autograd or a manual registration to one
2024-11-01T17:51:29.9361123Z           or more DispatchKey::Autograd keys. Any other DispatchKey-based
2024-11-01T17:51:29.9362105Z           registrations may lead to undefined behavior.
2024-11-01T17:51:29.9363132Z         - test_faketensor: If the operator has a FakeTensor kernel
2024-11-01T17:51:29.9364147Z           (and if it is correct). The FakeTensor kernel is necessary (
2024-11-01T17:51:29.9365269Z           but not sufficient) for the operator to work with PyTorch compilation
2024-11-01T17:51:29.9366428Z           APIs (torch.compile/export/FX). We check that a FakeTensor kernel
2024-11-01T17:51:29.9367553Z           (also sometimes known as a meta kernel) was registered for the
2024-11-01T17:51:29.9368626Z           operator and that it is correct. This test takes the result of
2024-11-01T17:51:29.9369703Z           running the operator on real tensors and the result of running
2024-11-01T17:51:29.9370776Z           the operator on FakeTensors and checks that they have the same
2024-11-01T17:51:29.9371850Z           Tensor metadata (sizes/strides/dtype/device/etc).
2024-11-01T17:51:29.9372933Z         - test_aot_dispatch_dynamic: If the operator has correct behavior
2024-11-01T17:51:29.9373978Z           with PyTorch compilation APIs (torch.compile/export/FX).
2024-11-01T17:51:29.9375072Z           This checks that the outputs (and gradients, if applicable) are the
2024-11-01T17:51:29.9376205Z           same under eager-mode PyTorch and torch.compile.
2024-11-01T17:51:29.9377208Z           This test is a superset of ``test_faketensor`` and is an e2e test;
2024-11-01T17:51:29.9378258Z           other things it tests are that the operator supports
2024-11-01T17:51:29.9379272Z           functionalization and that the backward pass (if it exists) also
2024-11-01T17:51:29.9380254Z           supports FakeTensor and functionalization.
2024-11-01T17:51:29.9380931Z     
2024-11-01T17:51:29.9381558Z         For best results, please call ``opcheck`` multiple times with a
2024-11-01T17:51:29.9382592Z         representative set of inputs. If your operator supports
2024-11-01T17:51:29.9383718Z         autograd, please use ``opcheck`` with inputs with ``requires_grad = True``;
2024-11-01T17:51:29.9384944Z         if your operator supports multiple devices (e.g. CPU and CUDA), please
2024-11-01T17:51:29.9386009Z         use ``opcheck`` with inputs on all supported devices.
2024-11-01T17:51:29.9386732Z     
2024-11-01T17:51:29.9387100Z         Args:
2024-11-01T17:51:29.9387734Z             op: The operator. Must either be a function decorated with
2024-11-01T17:51:29.9388821Z                 :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket
2024-11-01T17:51:29.9390079Z                 found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo)
2024-11-01T17:51:29.9391031Z             args: The args to the operator
2024-11-01T17:51:29.9391737Z             kwargs: The kwargs to the operator
2024-11-01T17:51:29.9392600Z             test_utils: Tests that we should run. Default: all of them.
2024-11-01T17:51:29.9393532Z                 Example: ("test_schema", "test_faketensor")
2024-11-01T17:51:29.9394559Z             raise_exception: If we should raise an exception on the first
2024-11-01T17:51:29.9395574Z                 error. If False, we will return a dict with information
2024-11-01T17:51:29.9396427Z                 on if each test passed or not.
2024-11-01T17:51:29.9397060Z     
2024-11-01T17:51:29.9397448Z         .. warning::
2024-11-01T17:51:29.9397895Z     
2024-11-01T17:51:29.9398561Z             opcheck and :func:`torch.autograd.gradcheck` test different things;
2024-11-01T17:51:29.9399745Z             opcheck tests if your usage of torch.library APIs is correct while
2024-11-01T17:51:29.9400911Z             :func:`torch.autograd.gradcheck` tests if your autograd formula is
2024-11-01T17:51:29.9402065Z             mathematically correct. Use both to test custom ops that support
2024-11-01T17:51:29.9402954Z             gradient computation.
2024-11-01T17:51:29.9403508Z     
2024-11-01T17:51:29.9403875Z         Example:
2024-11-01T17:51:29.9404295Z     
2024-11-01T17:51:29.9404803Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:29.9405773Z             >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=())
2024-11-01T17:51:29.9407078Z             >>> def numpy_add(x: Tensor, y: float) -> Tensor:
2024-11-01T17:51:29.9407840Z             >>>     x_np = x.numpy(force=True)
2024-11-01T17:51:29.9408509Z             >>>     z_np = x_np + y
2024-11-01T17:51:29.9409210Z             >>>     return torch.from_numpy(z_np).to(x.device)
2024-11-01T17:51:29.9409916Z             >>>
2024-11-01T17:51:29.9410398Z             >>> @numpy_sin.register_fake
2024-11-01T17:51:29.9411006Z             >>> def _(x, y):
2024-11-01T17:51:29.9411594Z             >>>     return torch.empty_like(x)
2024-11-01T17:51:29.9412224Z             >>>
2024-11-01T17:51:29.9412749Z             >>> def setup_context(ctx, inputs, output):
2024-11-01T17:51:29.9413445Z             >>>     y, = inputs
2024-11-01T17:51:29.9414161Z             >>>     ctx.y = y
2024-11-01T17:51:29.9414657Z             >>>
2024-11-01T17:51:29.9415120Z             >>> def backward(ctx, grad):
2024-11-01T17:51:29.9415793Z             >>>     return grad * ctx.y, None
2024-11-01T17:51:29.9416421Z             >>>
2024-11-01T17:51:29.9417140Z             >>> numpy_sin.register_autograd(backward, setup_context=setup_context)
2024-11-01T17:51:29.9417995Z             >>>
2024-11-01T17:51:29.9418440Z             >>> sample_inputs = [
2024-11-01T17:51:29.9419048Z             >>>     (torch.randn(3), 3.14),
2024-11-01T17:51:29.9419918Z             >>>     (torch.randn(2, 3, device='cuda'), 2.718),
2024-11-01T17:51:29.9420761Z             >>>     (torch.randn(1, 10, requires_grad=True), 1.234),
2024-11-01T17:51:29.9421865Z             >>>     (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18),
2024-11-01T17:51:29.9422710Z             >>> ]
2024-11-01T17:51:29.9423134Z             >>>
2024-11-01T17:51:29.9423610Z             >>> for args in sample_inputs:
2024-11-01T17:51:29.9424345Z             >>>     torch.library.opcheck(foo, args)
2024-11-01T17:51:29.9425009Z     
2024-11-01T17:51:29.9425360Z         
2024-11-01T17:51:29.9426223Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9427139Z     
2024-11-01T17:51:29.9427528Z       warnings.warn(msg)
2024-11-01T17:51:29.9428005Z     
2024-11-01T17:51:29.9428531Z --- Parse Warning: 10 / 103 ---
2024-11-01T17:51:29.9431189Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/serialization.py line=1171.
2024-11-01T17:51:29.9433952Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9435451Z     load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args)
2024-11-01T17:51:29.9436557Z     
2024-11-01T17:51:29.9437154Z         Loads an object saved with :func:`torch.save` from a file.
2024-11-01T17:51:29.9437921Z     
2024-11-01T17:51:29.9438762Z         :func:`torch.load` uses Python's unpickling facilities but treats storages,
2024-11-01T17:51:29.9439989Z         which underlie tensors, specially. They are first deserialized on the
2024-11-01T17:51:29.9441206Z         CPU and are then moved to the device they were saved from. If this fails
2024-11-01T17:51:29.9442598Z         (e.g. because the run time system doesn't have certain devices), an exception
2024-11-01T17:51:29.9443894Z         is raised. However, storages can be dynamically remapped to an alternative
2024-11-01T17:51:29.9445002Z         set of devices using the :attr:`map_location` argument.
2024-11-01T17:51:29.9445745Z     
2024-11-01T17:51:29.9446500Z         If :attr:`map_location` is a callable, it will be called once for each serialized
2024-11-01T17:51:29.9447776Z         storage with two arguments: storage and location. The storage argument
2024-11-01T17:51:29.9449020Z         will be the initial deserialization of the storage, residing on the CPU.
2024-11-01T17:51:29.9450251Z         Each serialized storage has a location tag associated with it which
2024-11-01T17:51:29.9451407Z         identifies the device it was saved from, and this tag is the second
2024-11-01T17:51:29.9452793Z         argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'``
2024-11-01T17:51:29.9454244Z         for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors.
2024-11-01T17:51:29.9455446Z         :attr:`map_location` should return either ``None`` or a storage. If
2024-11-01T17:51:29.9456684Z         :attr:`map_location` returns a storage, it will be used as the final deserialized
2024-11-01T17:51:29.9458020Z         object, already moved to the right device. Otherwise, :func:`torch.load` will
2024-11-01T17:51:29.9459475Z         fall back to the default behavior, as if :attr:`map_location` wasn't specified.
2024-11-01T17:51:29.9460569Z     
2024-11-01T17:51:29.9461324Z         If :attr:`map_location` is a :class:`torch.device` object or a string containing
2024-11-01T17:51:29.9462638Z         a device tag, it indicates the location where all tensors should be loaded.
2024-11-01T17:51:29.9463559Z     
2024-11-01T17:51:29.9464343Z         Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags
2024-11-01T17:51:29.9465611Z         appearing in the file (keys), to ones that specify where to put the
2024-11-01T17:51:29.9466498Z         storages (values).
2024-11-01T17:51:29.9466996Z     
2024-11-01T17:51:29.9467676Z         User extensions can register their own location tags and tagging and
2024-11-01T17:51:29.9468932Z         deserialization methods using :func:`torch.serialization.register_package`.
2024-11-01T17:51:29.9469882Z     
2024-11-01T17:51:29.9470238Z         Args:
2024-11-01T17:51:29.9471385Z             f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`),
2024-11-01T17:51:29.9472748Z                 or a string or os.PathLike object containing a file name
2024-11-01T17:51:29.9474156Z             map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
2024-11-01T17:51:29.9475261Z                 locations
2024-11-01T17:51:29.9476070Z             pickle_module: module used for unpickling metadata and objects (has to
2024-11-01T17:51:29.9477153Z                 match the :attr:`pickle_module` used to serialize file)
2024-11-01T17:51:29.9478210Z             weights_only: Indicates whether unpickler should be restricted to
2024-11-01T17:51:29.9479331Z                 loading only tensors, primitive types, dictionaries
2024-11-01T17:51:29.9480427Z                 and any types added via :func:`torch.serialization.add_safe_globals`.
2024-11-01T17:51:29.9481908Z             mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory.
2024-11-01T17:51:29.9483636Z                 Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they
2024-11-01T17:51:29.9485378Z                 are moved to the location that they were tagged with when saving, or specified by ``map_location``. This
2024-11-01T17:51:29.9487369Z                 second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the
2024-11-01T17:51:29.9488927Z                 tensor storages from disk to CPU memory in the first step, ``f`` is mmaped.
2024-11-01T17:51:29.9490231Z             pickle_load_args: (Python 3 only) optional keyword arguments passed over to
2024-11-01T17:51:29.9491463Z                 :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g.,
2024-11-01T17:51:29.9492386Z                 :attr:`errors=...`.
2024-11-01T17:51:29.9492947Z     
2024-11-01T17:51:29.9493330Z         .. warning::
2024-11-01T17:51:29.9494083Z             :func:`torch.load()` unless `weights_only` parameter is set to `True`,
2024-11-01T17:51:29.9495236Z             uses ``pickle`` module implicitly, which is known to be insecure.
2024-11-01T17:51:29.9495827Z             It is possible to construct malicious pickle data which will execute arbitrary code
2024-11-01T17:51:29.9496350Z             during unpickling. Never load data that could have come from an untrusted
2024-11-01T17:51:29.9497013Z             source in an unsafe mode, or that could have been tampered with. **Only load data you trust**.
2024-11-01T17:51:29.9497174Z     
2024-11-01T17:51:29.9497338Z         .. note::
2024-11-01T17:51:29.9497952Z             When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors
2024-11-01T17:51:29.9498689Z             will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')``
2024-11-01T17:51:29.9499291Z             and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
2024-11-01T17:51:29.9499448Z     
2024-11-01T17:51:29.9499683Z         .. note::
2024-11-01T17:51:29.9500401Z             By default, we decode byte strings as ``utf-8``.  This is to avoid a common error
2024-11-01T17:51:29.9500961Z             case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``
2024-11-01T17:51:29.9501420Z             when loading files saved by Python 2 in Python 3.  If this default
2024-11-01T17:51:29.9501995Z             is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how
2024-11-01T17:51:29.9502662Z             these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them
2024-11-01T17:51:29.9503316Z             to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them
2024-11-01T17:51:29.9503825Z             as byte arrays which can be decoded later with ``byte_array.decode(...)``.
2024-11-01T17:51:29.9503986Z     
2024-11-01T17:51:29.9504144Z         Example:
2024-11-01T17:51:29.9504438Z             >>> # xdoctest: +SKIP("undefined filepaths")
2024-11-01T17:51:29.9504736Z             >>> torch.load("tensors.pt", weights_only=True)
2024-11-01T17:51:29.9504973Z             # Load all tensors onto the CPU
2024-11-01T17:51:29.9505500Z             >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True)
2024-11-01T17:51:29.9505812Z             # Load all tensors onto the CPU, using a function
2024-11-01T17:51:29.9506000Z             >>> torch.load(
2024-11-01T17:51:29.9506508Z             ...     "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True
2024-11-01T17:51:29.9506884Z             ... )
2024-11-01T17:51:29.9507104Z             # Load all tensors onto GPU 1
2024-11-01T17:51:29.9507445Z             >>> torch.load(
2024-11-01T17:51:29.9507625Z             ...     "tensors.pt",
2024-11-01T17:51:29.9507965Z             ...     map_location=lambda storage, loc: storage.cuda(1),
2024-11-01T17:51:29.9508171Z             ...     weights_only=True,
2024-11-01T17:51:29.9508499Z             ... )  # type: ignore[attr-defined]
2024-11-01T17:51:29.9508757Z             # Map tensors from GPU 1 to GPU 0
2024-11-01T17:51:29.9509281Z             >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True)
2024-11-01T17:51:29.9509543Z             # Load tensor from io.BytesIO object
2024-11-01T17:51:29.9510080Z             # Loading from a buffer setting weights_only=False, warning this can be unsafe
2024-11-01T17:51:29.9510325Z             >>> with open("tensor.pt", "rb") as f:
2024-11-01T17:51:29.9510571Z             ...     buffer = io.BytesIO(f.read())
2024-11-01T17:51:29.9510829Z             >>> torch.load(buffer, weights_only=False)
2024-11-01T17:51:29.9511280Z             # Load a module with 'ascii' encoding for unpickling
2024-11-01T17:51:29.9511810Z             # Loading from a module setting weights_only=False, warning this can be unsafe
2024-11-01T17:51:29.9512235Z             >>> torch.load("module.pt", encoding="ascii", weights_only=False)
2024-11-01T17:51:29.9512385Z         
2024-11-01T17:51:29.9513030Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9513189Z     
2024-11-01T17:51:29.9513368Z       warnings.warn(msg)
2024-11-01T17:51:29.9513526Z     
2024-11-01T17:51:29.9513939Z --- Parse Warning: 11 / 103 ---
2024-11-01T17:51:29.9516130Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=21.
2024-11-01T17:51:29.9516756Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:29.9517188Z     Check if there is an available :ref:`accelerator<accelerators>`.
2024-11-01T17:51:29.9517360Z     
2024-11-01T17:51:29.9517526Z         Returns:
2024-11-01T17:51:29.9518131Z             bool: A boolean indicating if there is an available :ref:`accelerator<accelerators>`.
2024-11-01T17:51:29.9518286Z     
2024-11-01T17:51:29.9518453Z         Example::
2024-11-01T17:51:29.9518715Z     
2024-11-01T17:51:29.9519280Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:29.9519446Z         
2024-11-01T17:51:29.9520828Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78))
2024-11-01T17:51:29.9520970Z     
2024-11-01T17:51:29.9521521Z     assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:29.9521716Z                                             ^
2024-11-01T17:51:29.9521917Z       warnings.warn(msg)
2024-11-01T17:51:29.9522066Z     
2024-11-01T17:51:29.9522625Z --- Parse Warning: 12 / 103 ---
2024-11-01T17:51:29.9525326Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/accelerator/__init__.py line=110.
2024-11-01T17:51:29.9528116Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:29.9529337Z     Wait for all kernels in all streams on the given device to complete.
2024-11-01T17:51:29.9530183Z     
2024-11-01T17:51:29.9530551Z         Args:
2024-11-01T17:51:29.9531497Z             device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
2024-11-01T17:51:29.9532912Z                 the current :ref:`accelerator<accelerators>` device type. If not given,
2024-11-01T17:51:29.9534148Z                 use :func:`torch.accelerator.current_device_idx` by default.
2024-11-01T17:51:29.9534980Z     
2024-11-01T17:51:29.9536092Z         .. note:: This function is a no-op if the current :ref:`accelerator<accelerators>` is not initialized.
2024-11-01T17:51:29.9537202Z     
2024-11-01T17:51:29.9537584Z         Example::
2024-11-01T17:51:29.9538002Z     
2024-11-01T17:51:29.9538511Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:29.9539617Z             >>> assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:29.9540733Z             >>> start_event = torch.Event(enable_timing=True)
2024-11-01T17:51:29.9541575Z             >>> end_event = torch.Event(enable_timing=True)
2024-11-01T17:51:29.9542315Z             >>> start_event.record()
2024-11-01T17:51:29.9543252Z             >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
2024-11-01T17:51:29.9544231Z             >>> sum = torch.sum(tensor)
2024-11-01T17:51:29.9544850Z             >>> end_event.record()
2024-11-01T17:51:29.9545517Z             >>> torch.accelerator.synchronize()
2024-11-01T17:51:29.9546373Z             >>> elapsed_time_ms = start_event.elapsed_time(end_event)
2024-11-01T17:51:29.9547130Z         
2024-11-01T17:51:29.9548745Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78))
2024-11-01T17:51:29.9550318Z     
2024-11-01T17:51:29.9551055Z     assert torch.accelerator.is_available() "No available accelerators detected."
2024-11-01T17:51:29.9552044Z                                             ^
2024-11-01T17:51:29.9552672Z       warnings.warn(msg)
2024-11-01T17:51:29.9553154Z     
2024-11-01T17:51:29.9553709Z --- Parse Warning: 13 / 103 ---
2024-11-01T17:51:29.9556382Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/cuda/__init__.py line=345.
2024-11-01T17:51:29.9559040Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:29.9560040Z     Retrieves the CUDA runtime API module.
2024-11-01T17:51:29.9560682Z     
2024-11-01T17:51:29.9561047Z     
2024-11-01T17:51:29.9561798Z         This function initializes the CUDA runtime environment if it is not already
2024-11-01T17:51:29.9563186Z         initialized and returns the CUDA runtime API module (_cudart). The CUDA
2024-11-01T17:51:29.9564403Z         runtime API module provides access to various CUDA runtime functions.
2024-11-01T17:51:29.9565276Z     
2024-11-01T17:51:29.9565639Z         Args:
2024-11-01T17:51:29.9566054Z             ``None``
2024-11-01T17:51:29.9566476Z     
2024-11-01T17:51:29.9566855Z         Returns:
2024-11-01T17:51:29.9567417Z             module: The CUDA runtime API module (_cudart).
2024-11-01T17:51:29.9568116Z     
2024-11-01T17:51:29.9568486Z         Raises:
2024-11-01T17:51:29.9569366Z             RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
2024-11-01T17:51:29.9570886Z             AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
2024-11-01T17:51:29.9594694Z     
2024-11-01T17:51:29.9595230Z         Example of CUDA operations with profiling:
2024-11-01T17:51:29.9595925Z             >>> import torch
2024-11-01T17:51:29.9596572Z             >>> from torch.cuda import cudart, check_error
2024-11-01T17:51:29.9597259Z             >>> import os
2024-11-01T17:51:29.9597722Z             >>>
2024-11-01T17:51:29.9598368Z             >>> os.environ['CUDA_PROFILE'] = '1'
2024-11-01T17:51:29.9598992Z             >>>
2024-11-01T17:51:29.9599518Z             >>> def perform_cuda_operations_with_streams():
2024-11-01T17:51:29.9600274Z             >>>     stream = torch.cuda.Stream()
2024-11-01T17:51:29.9600988Z             >>>     with torch.cuda.stream(stream):
2024-11-01T17:51:29.9601848Z             >>>         x = torch.randn(100, 100, device='cuda')
2024-11-01T17:51:29.9602937Z             >>>         y = torch.randn(100, 100, device='cuda')
2024-11-01T17:51:29.9603657Z             >>>         z = torch.mul(x, y)
2024-11-01T17:51:29.9604271Z             >>>     return z
2024-11-01T17:51:29.9604744Z             >>>
2024-11-01T17:51:29.9605202Z             >>> torch.cuda.synchronize()
2024-11-01T17:51:29.9605914Z             >>> print("====== Start nsys profiling ======")
2024-11-01T17:51:29.9606984Z             >>> check_error(cudart().cudaProfilerStart())
2024-11-01T17:51:29.9607804Z             >>> with torch.autograd.profiler.emit_nvtx():
2024-11-01T17:51:29.9608648Z             >>>     result = perform_cuda_operations_with_streams()
2024-11-01T17:51:29.9609470Z             >>>     print("CUDA operations completed.")
2024-11-01T17:51:29.9610313Z             >>> check_error(torch.cuda.cudart().cudaProfilerStop())
2024-11-01T17:51:29.9611162Z             >>> print("====== End nsys profiling ======")
2024-11-01T17:51:29.9611805Z     
2024-11-01T17:51:29.9612428Z         To run this example and save the profiling information, execute:
2024-11-01T17:51:29.9614017Z             >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2024-11-01T17:51:29.9615150Z     
2024-11-01T17:51:29.9615882Z         This command profiles the CUDA operations in the provided script and saves
2024-11-01T17:51:29.9617076Z         the profiling information to a file named `trace_name.prof`.
2024-11-01T17:51:29.9618345Z         The `--profile-from-start off` option ensures that profiling starts only
2024-11-01T17:51:29.9619426Z         after the `cudaProfilerStart` call in the script.
2024-11-01T17:51:29.9620585Z         The `--csv` and `--print-summary` options format the profiling output as a
2024-11-01T17:51:29.9621597Z         CSV file and print a summary, respectively.
2024-11-01T17:51:29.9622784Z         The `-o` option specifies the output file name, and the `-f` option forces the
2024-11-01T17:51:29.9623895Z         overwrite of the output file if it already exists.
2024-11-01T17:51:29.9624624Z         
2024-11-01T17:51:29.9626439Z     Original Error: SyntaxError('invalid syntax', ('<source_block>', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2))
2024-11-01T17:51:29.9628182Z     
2024-11-01T17:51:29.9629289Z     $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
2024-11-01T17:51:29.9630676Z     ^
2024-11-01T17:51:29.9631087Z       warnings.warn(msg)
2024-11-01T17:51:29.9631577Z     
2024-11-01T17:51:29.9632120Z --- Parse Warning: 14 / 103 ---
2024-11-01T17:51:29.9634980Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=101.
2024-11-01T17:51:29.9637790Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9638764Z     
2024-11-01T17:51:29.9639485Z     Append the given callback function to this ``Future``, which will be run
2024-11-01T17:51:29.9640704Z     when the ``Future`` is completed.  Multiple callbacks can be added to
2024-11-01T17:51:29.9641870Z     the same ``Future``, but the order in which they will be executed cannot
2024-11-01T17:51:29.9643001Z     be guaranteed (to enforce a certain order consider chaining:
2024-11-01T17:51:29.9644118Z     ``fut.then(cb1).then(cb2)``). The callback must take one argument, which
2024-11-01T17:51:29.9645314Z     is the reference to this ``Future``. The callback function can use the
2024-11-01T17:51:29.9646511Z     :meth:`value` method to get the value. Note that if this ``Future`` is
2024-11-01T17:51:29.9647700Z     already completed, the given callback will be run immediately inline.
2024-11-01T17:51:29.9648581Z     
2024-11-01T17:51:29.9649364Z     If the ``Future``'s value contains tensors that reside on GPUs, the
2024-11-01T17:51:29.9650717Z     callback might be invoked while the async kernels that are populating
2024-11-01T17:51:29.9652081Z     those tensors haven't yet finished executing on the device. However, the
2024-11-01T17:51:29.9653293Z     callback will be invoked with some dedicated streams set as current
2024-11-01T17:51:29.9654452Z     (fetched from a global pool) which will be synchronized with those
2024-11-01T17:51:29.9655629Z     kernels. Hence any operation performed by the callback on these tensors
2024-11-01T17:51:29.9656827Z     will be scheduled on the device after the kernels complete. In other
2024-11-01T17:51:29.9658096Z     words, as long as the callback doesn't switch streams, it can safely
2024-11-01T17:51:29.9659273Z     manipulate the result without any additional synchronization. This is
2024-11-01T17:51:29.9660438Z     similar to the non-blocking behavior of :meth:`wait`.
2024-11-01T17:51:29.9661168Z     
2024-11-01T17:51:29.9661852Z     Similarly, if the callback returns a value that contains tensors that
2024-11-01T17:51:29.9663027Z     reside on a GPU, it can do so even if the kernels that are producing
2024-11-01T17:51:29.9664215Z     these tensors are still running on the device, as long as the callback
2024-11-01T17:51:29.9665486Z     didn't change streams during its execution. If one wants to change
2024-11-01T17:51:29.9666759Z     streams, one must be careful to re-synchronize them with the original
2024-11-01T17:51:29.9667991Z     streams, that is, those that were current when the callback was invoked.
2024-11-01T17:51:29.9668862Z     
2024-11-01T17:51:29.9669240Z     Args:
2024-11-01T17:51:29.9669910Z         callback(``Callable``): a ``Callable`` that takes this ``Future`` as
2024-11-01T17:51:29.9670838Z                                 the only argument.
2024-11-01T17:51:29.9671482Z     
2024-11-01T17:51:29.9671846Z     Returns:
2024-11-01T17:51:29.9672466Z         A new ``Future`` object that holds the return value of the
2024-11-01T17:51:29.9673473Z         ``callback`` and will be marked as completed when the given
2024-11-01T17:51:29.9674402Z         ``callback`` finishes.
2024-11-01T17:51:29.9674938Z     
2024-11-01T17:51:29.9675555Z     .. note:: Note that if the callback function throws, either
2024-11-01T17:51:29.9676607Z         through the original future being completed with an exception and
2024-11-01T17:51:29.9677736Z         calling ``fut.wait()``, or through other code in the callback, the
2024-11-01T17:51:29.9679060Z         future returned by ``then`` will be marked appropriately with the
2024-11-01T17:51:29.9680153Z         encountered error. However, if this callback later completes
2024-11-01T17:51:29.9681262Z         additional futures, those futures are not marked as completed with
2024-11-01T17:51:29.9682445Z         an error and the user is responsible for handling completion/waiting
2024-11-01T17:51:29.9683376Z         on those futures independently.
2024-11-01T17:51:29.9683984Z     
2024-11-01T17:51:29.9684366Z     Example::
2024-11-01T17:51:29.9684942Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2024-11-01T17:51:29.9685691Z         >>> def callback(fut):
2024-11-01T17:51:29.9686348Z         ...     print(f"RPC return value is {fut.wait()}.")
2024-11-01T17:51:29.9687121Z         >>> fut = torch.futures.Future()
2024-11-01T17:51:29.9687944Z         >>> # The inserted callback will print the return value when
2024-11-01T17:51:29.9688833Z         >>> # receiving the response from "worker1"
2024-11-01T17:51:29.9689560Z         >>> cb_fut = fut.then(callback)
2024-11-01T17:51:29.9690212Z         >>> chain_cb_fut = cb_fut.then(
2024-11-01T17:51:29.9690957Z         ...     lambda x : print(f"Chained cb done. {x.wait()}")
2024-11-01T17:51:29.9691691Z         ... )
2024-11-01T17:51:29.9692128Z         >>> fut.set_result(5)
2024-11-01T17:51:29.9692683Z         RPC return value is 5.
2024-11-01T17:51:29.9693254Z         Chained cb done. None
2024-11-01T17:51:29.9693756Z     
2024-11-01T17:51:29.9694773Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9695705Z     
2024-11-01T17:51:29.9696106Z       warnings.warn(msg)
2024-11-01T17:51:29.9696594Z     
2024-11-01T17:51:29.9697131Z --- Parse Warning: 15 / 103 ---
2024-11-01T17:51:29.9699837Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/futures/__init__.py line=209.
2024-11-01T17:51:29.9702661Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9703610Z     
2024-11-01T17:51:29.9704282Z     Set the result for this ``Future``, which will mark this ``Future`` as
2024-11-01T17:51:29.9705459Z     completed and trigger all attached callbacks. Note that a ``Future``
2024-11-01T17:51:29.9706384Z     cannot be marked completed twice.
2024-11-01T17:51:29.9707203Z     
2024-11-01T17:51:29.9707900Z     If the result contains tensors that reside on GPUs, this method can be
2024-11-01T17:51:29.9709074Z     called even if the asynchronous kernels that are populating those
2024-11-01T17:51:29.9710370Z     tensors haven't yet completed running on the device, provided that the
2024-11-01T17:51:29.9711600Z     streams on which those kernels were enqueued are set as the current ones
2024-11-01T17:51:29.9712902Z     when this method is called. Put simply, it's safe to call this method
2024-11-01T17:51:29.9714151Z     immediately after launching those kernels, without any additional
2024-11-01T17:51:29.9715444Z     synchronization, as long as one doesn't change streams in between. This
2024-11-01T17:51:29.9716657Z     method will record events on all the relevant current streams and will
2024-11-01T17:51:29.9717825Z     use them to ensure proper scheduling for all the consumers of this
2024-11-01T17:51:29.9718686Z     ``Future``.
2024-11-01T17:51:29.9719089Z     
2024-11-01T17:51:29.9719458Z     Args:
2024-11-01T17:51:29.9720041Z         result (object): the result object of this ``Future``.
2024-11-01T17:51:29.9720785Z     
2024-11-01T17:51:29.9721168Z     Example::
2024-11-01T17:51:29.9721713Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES)
2024-11-01T17:51:29.9722460Z         >>> import threading
2024-11-01T17:51:29.9722996Z         >>> import time
2024-11-01T17:51:29.9723544Z         >>> def slow_set_future(fut, value):
2024-11-01T17:51:29.9724423Z         ...     time.sleep(0.5)
2024-11-01T17:51:29.9724991Z         ...     fut.set_result(value)
2024-11-01T17:51:29.9725641Z         >>> fut = torch.futures.Future()
2024-11-01T17:51:29.9726299Z         >>> t = threading.Thread(
2024-11-01T17:51:29.9726900Z         ...     target=slow_set_future,
2024-11-01T17:51:29.9727564Z         ...     args=(fut, torch.ones(2) * 3)
2024-11-01T17:51:29.9728172Z         ... )
2024-11-01T17:51:29.9728593Z         >>> t.start()
2024-11-01T17:51:29.9729082Z         >>> print(fut.wait())
2024-11-01T17:51:29.9729617Z         tensor([3., 3.])
2024-11-01T17:51:29.9730113Z         >>> t.join()
2024-11-01T17:51:29.9730541Z     
2024-11-01T17:51:29.9731461Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9732378Z     
2024-11-01T17:51:29.9732770Z       warnings.warn(msg)
2024-11-01T17:51:29.9733256Z     
2024-11-01T17:51:29.9733792Z --- Parse Warning: 16 / 103 ---
2024-11-01T17:51:29.9736378Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py line=202.
2024-11-01T17:51:29.9739061Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9740239Z     Return the sum of each row of the given sparse tensor.
2024-11-01T17:51:29.9741046Z     
2024-11-01T17:51:29.9741779Z         Returns the sum of each row of the sparse tensor :attr:`input` in the given
2024-11-01T17:51:29.9743092Z         dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions,
2024-11-01T17:51:29.9744222Z         reduce over all of them. When sum over all ``sparse_dim``, this method
2024-11-01T17:51:29.9745277Z         returns a dense tensor instead of a sparse tensor.
2024-11-01T17:51:29.9745998Z     
2024-11-01T17:51:29.9746775Z         All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output
2024-11-01T17:51:29.9748016Z         tensor having :attr:`dim` fewer dimensions than :attr:`input`.
2024-11-01T17:51:29.9748827Z     
2024-11-01T17:51:29.9749512Z         During backward, only gradients at ``nnz`` locations of :attr:`input`
2024-11-01T17:51:29.9750752Z         will propagate back. Note that the gradients of :attr:`input` is coalesced.
2024-11-01T17:51:29.9751688Z     
2024-11-01T17:51:29.9752069Z         Args:
2024-11-01T17:51:29.9752579Z             input (Tensor): the input sparse tensor
2024-11-01T17:51:29.9753721Z             dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce
2024-11-01T17:51:29.9754911Z                 over all dims.
2024-11-01T17:51:29.9755841Z             dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor.
2024-11-01T17:51:29.9756901Z                 Default: dtype of :attr:`input`.
2024-11-01T17:51:29.9757542Z     
2024-11-01T17:51:29.9757921Z         Example::
2024-11-01T17:51:29.9758352Z     
2024-11-01T17:51:29.9758738Z             >>> nnz = 3
2024-11-01T17:51:29.9759233Z             >>> dims = [5, 5, 2, 3]
2024-11-01T17:51:29.9759978Z             >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)),
2024-11-01T17:51:29.9760979Z                                torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz)
2024-11-01T17:51:29.9761892Z             >>> V = torch.randn(nnz, dims[2], dims[3])
2024-11-01T17:51:29.9762633Z             >>> size = torch.Size(dims)
2024-11-01T17:51:29.9763506Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:29.9764336Z             >>> S = torch.sparse_coo_tensor(I, V, size)
2024-11-01T17:51:29.9765025Z             >>> S
2024-11-01T17:51:29.9765523Z             tensor(indices=tensor([[2, 0, 3],
2024-11-01T17:51:29.9766252Z                                    [2, 4, 1]]),
2024-11-01T17:51:29.9767104Z                    values=tensor([[[-0.6438, -1.6467,  1.4004],
2024-11-01T17:51:29.9767989Z                                    [ 0.3411,  0.0918, -0.2312]],
2024-11-01T17:51:29.9768776Z     
2024-11-01T17:51:29.9769349Z                                   [[ 0.5348,  0.0634, -2.0494],
2024-11-01T17:51:29.9770195Z                                    [-0.7125, -1.0646,  2.1844]],
2024-11-01T17:51:29.9770846Z     
2024-11-01T17:51:29.9771416Z                                   [[ 0.1276,  0.1874, -0.6334],
2024-11-01T17:51:29.9772278Z                                    [-1.9682, -0.5340,  0.7483]]]),
2024-11-01T17:51:29.9773096Z                    size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo)
2024-11-01T17:51:29.9773799Z     
2024-11-01T17:51:29.9774447Z             # when sum over only part of sparse_dims, return a sparse tensor
2024-11-01T17:51:29.9775351Z             >>> torch.sparse.sum(S, [1, 3])
2024-11-01T17:51:29.9776059Z             tensor(indices=tensor([[0, 2, 3]]),
2024-11-01T17:51:29.9776891Z                    values=tensor([[-1.4512,  0.4073],
2024-11-01T17:51:29.9777710Z                                   [-0.8901,  0.2017],
2024-11-01T17:51:29.9778507Z                                   [-0.3183, -1.7539]]),
2024-11-01T17:51:29.9779276Z                    size=(5, 2), nnz=3, layout=torch.sparse_coo)
2024-11-01T17:51:29.9780033Z     
2024-11-01T17:51:29.9780457Z             # when sum over all sparse dim, return a dense tensor
2024-11-01T17:51:29.9781009Z             # with summed dims squeezed
2024-11-01T17:51:29.9781503Z             >>> torch.sparse.sum(S, [0, 1, 3])
2024-11-01T17:51:29.9782025Z             tensor([-2.6596, -1.1450])
2024-11-01T17:51:29.9782421Z         
2024-11-01T17:51:29.9783141Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9783765Z     
2024-11-01T17:51:29.9784021Z       warnings.warn(msg)
2024-11-01T17:51:29.9784352Z     
2024-11-01T17:51:29.9784738Z --- Parse Warning: 17 / 103 ---
2024-11-01T17:51:29.9786468Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_functorch/apis.py line=40.
2024-11-01T17:51:29.9788279Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9788920Z     
2024-11-01T17:51:29.9789384Z     vmap is the vectorizing map; ``vmap(func)`` returns a new function that
2024-11-01T17:51:29.9790181Z     maps ``func`` over some dimension of the inputs. Semantically, vmap
2024-11-01T17:51:29.9790977Z     pushes the map into PyTorch operations called by ``func``, effectively
2024-11-01T17:51:29.9791619Z     vectorizing those operations.
2024-11-01T17:51:29.9792018Z     
2024-11-01T17:51:29.9792502Z     vmap is useful for handling batch dimensions: one can write a function
2024-11-01T17:51:29.9793292Z     ``func`` that runs on examples and then lift it to a function that can
2024-11-01T17:51:29.9794221Z     take batches of examples with ``vmap(func)``. vmap can also be used to
2024-11-01T17:51:29.9794944Z     compute batched gradients when composed with autograd.
2024-11-01T17:51:29.9795448Z     
2024-11-01T17:51:29.9795722Z     .. note::
2024-11-01T17:51:29.9796167Z         :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for
2024-11-01T17:51:29.9796849Z         convenience. Use whichever one you'd like.
2024-11-01T17:51:29.9797314Z     
2024-11-01T17:51:29.9797565Z     Args:
2024-11-01T17:51:29.9798037Z         func (function): A Python function that takes one or more arguments.
2024-11-01T17:51:29.9798684Z             Must return one or more Tensors.
2024-11-01T17:51:29.9799308Z         in_dims (int or nested structure): Specifies which dimension of the
2024-11-01T17:51:29.9800041Z             inputs should be mapped over. ``in_dims`` should have a
2024-11-01T17:51:29.9800738Z             structure like the inputs. If the ``in_dim`` for a particular
2024-11-01T17:51:29.9801466Z             input is None, then that indicates there is no map dimension.
2024-11-01T17:51:29.9802037Z             Default: 0.
2024-11-01T17:51:29.9802551Z         out_dims (int or Tuple[int]): Specifies where the mapped dimension
2024-11-01T17:51:29.9803374Z             should appear in the outputs. If ``out_dims`` is a Tuple, then
2024-11-01T17:51:29.9804053Z             it should have one element per output. Default: 0.
2024-11-01T17:51:29.9804707Z         randomness (str): Specifies whether the randomness in this
2024-11-01T17:51:29.9805543Z             vmap should be the same or different across batches. If 'different',
2024-11-01T17:51:29.9806408Z             the randomness for each batch will be different. If 'same', the
2024-11-01T17:51:29.9807531Z             randomness will be the same across batches. If 'error', any calls to
2024-11-01T17:51:29.9808397Z             random functions will error. Default: 'error'. WARNING: this flag
2024-11-01T17:51:29.9809168Z             only applies to random PyTorch operations and does not apply to
2024-11-01T17:51:29.9809903Z             Python's random module or numpy randomness.
2024-11-01T17:51:29.9810631Z         chunk_size (None or int): If None (default), apply a single vmap over inputs.
2024-11-01T17:51:29.9811494Z             If not None, then compute the vmap :attr:`chunk_size` samples at a time.
2024-11-01T17:51:29.9812494Z             Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop.
2024-11-01T17:51:29.9813533Z             If you run into memory issues computing the vmap, please try a non-None chunk_size.
2024-11-01T17:51:29.9814204Z     
2024-11-01T17:51:29.9814461Z     Returns:
2024-11-01T17:51:29.9814917Z         Returns a new "batched" function. It takes the same inputs as
2024-11-01T17:51:29.9815779Z         ``func``, except each input has an extra dimension at the index
2024-11-01T17:51:29.9816499Z         specified by ``in_dims``. It takes returns the same outputs as
2024-11-01T17:51:29.9817208Z         ``func``, except each output has an extra dimension at the index
2024-11-01T17:51:29.9817827Z         specified by ``out_dims``.
2024-11-01T17:51:29.9818210Z     
2024-11-01T17:51:29.9818470Z     .. warning:
2024-11-01T17:51:29.9819045Z         :func:`vmap` works best with functional-style code. Please do not
2024-11-01T17:51:29.9819847Z         perform any side-effects in ``func``, with the exception of
2024-11-01T17:51:29.9820706Z         in-place PyTorch operations. Examples of side-effects include mutating
2024-11-01T17:51:29.9821542Z         Python data structures and assigning values to variables not captured
2024-11-01T17:51:29.9822154Z         in ``func``.
2024-11-01T17:51:29.9822458Z     
2024-11-01T17:51:29.9822974Z     One example of using :func:`vmap` is to compute batched dot products. PyTorch
2024-11-01T17:51:29.9823889Z     doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully
2024-11-01T17:51:29.9824776Z     rummaging through docs, use :func:`vmap` to construct a new function.
2024-11-01T17:51:29.9825359Z     
2024-11-01T17:51:29.9825793Z         >>> torch.dot                            # [D], [D] -> []
2024-11-01T17:51:29.9826553Z         >>> batched_dot = torch.func.vmap(torch.dot)  # [N, D], [N, D] -> [N]
2024-11-01T17:51:29.9827214Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2024-11-01T17:51:29.9827685Z         >>> batched_dot(x, y)
2024-11-01T17:51:29.9828041Z     
2024-11-01T17:51:29.9828537Z     :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler
2024-11-01T17:51:29.9829193Z     model authoring experience.
2024-11-01T17:51:29.9829571Z     
2024-11-01T17:51:29.9829858Z         >>> batch_size, feature_size = 3, 5
2024-11-01T17:51:29.9830429Z         >>> weights = torch.randn(feature_size, requires_grad=True)
2024-11-01T17:51:29.9830955Z         >>>
2024-11-01T17:51:29.9831258Z         >>> def model(feature_vec):
2024-11-01T17:51:29.9831747Z         >>>     # Very simple linear model with activation
2024-11-01T17:51:29.9832288Z         >>>     return feature_vec.dot(weights).relu()
2024-11-01T17:51:29.9832752Z         >>>
2024-11-01T17:51:29.9833136Z         >>> examples = torch.randn(batch_size, feature_size)
2024-11-01T17:51:29.9833806Z         >>> result = torch.vmap(model)(examples)
2024-11-01T17:51:29.9834355Z     
2024-11-01T17:51:29.9834870Z     :func:`vmap` can also help vectorize computations that were previously difficult
2024-11-01T17:51:29.9835855Z     or impossible to batch. One example is higher-order gradient computation.
2024-11-01T17:51:29.9836772Z     The PyTorch autograd engine computes vjps (vector-Jacobian products).
2024-11-01T17:51:29.9837684Z     Computing a full Jacobian matrix for some function f: R^N -> R^N usually
2024-11-01T17:51:29.9838559Z     requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`,
2024-11-01T17:51:29.9839452Z     we can vectorize the whole computation, computing the Jacobian in a single
2024-11-01T17:51:29.9840108Z     call to ``autograd.grad``.
2024-11-01T17:51:29.9840457Z     
2024-11-01T17:51:29.9840714Z         >>> # Setup
2024-11-01T17:51:29.9841049Z         >>> N = 5
2024-11-01T17:51:29.9841368Z         >>> f = lambda x: x ** 2
2024-11-01T17:51:29.9841877Z         >>> x = torch.randn(N, requires_grad=True)
2024-11-01T17:51:29.9842329Z         >>> y = f(x)
2024-11-01T17:51:29.9842659Z         >>> I_N = torch.eye(N)
2024-11-01T17:51:29.9843019Z         >>>
2024-11-01T17:51:29.9843320Z         >>> # Sequential approach
2024-11-01T17:51:29.9843913Z         >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0]
2024-11-01T17:51:29.9844549Z         >>>                  for v in I_N.unbind()]
2024-11-01T17:51:29.9845064Z         >>> jacobian = torch.stack(jacobian_rows)
2024-11-01T17:51:29.9845514Z         >>>
2024-11-01T17:51:29.9846015Z         >>> # vectorized gradient computation
2024-11-01T17:51:29.9846475Z         >>> def get_vjp(v):
2024-11-01T17:51:29.9846883Z         >>>     return torch.autograd.grad(y, x, v)
2024-11-01T17:51:29.9847413Z         >>> jacobian = torch.vmap(get_vjp)(I_N)
2024-11-01T17:51:29.9847852Z     
2024-11-01T17:51:29.9848403Z     :func:`vmap` can also be nested, producing an output with multiple batched dimensions
2024-11-01T17:51:29.9849073Z     
2024-11-01T17:51:29.9849496Z         >>> torch.dot                            # [D], [D] -> []
2024-11-01T17:51:29.9850401Z         >>> batched_dot = torch.vmap(torch.vmap(torch.dot))  # [N1, N0, D], [N1, N0, D] -> [N1, N0]
2024-11-01T17:51:29.9851195Z         >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5)
2024-11-01T17:51:29.9851759Z         >>> batched_dot(x, y) # tensor of size [2, 3]
2024-11-01T17:51:29.9852212Z     
2024-11-01T17:51:29.9852722Z     If the inputs are not batched along the first dimension, ``in_dims`` specifies
2024-11-01T17:51:29.9853467Z     the dimension that each inputs are batched along as
2024-11-01T17:51:29.9853956Z     
2024-11-01T17:51:29.9854390Z         >>> torch.dot                            # [N], [N] -> []
2024-11-01T17:51:29.9855187Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=1)  # [N, D], [N, D] -> [D]
2024-11-01T17:51:29.9855873Z         >>> x, y = torch.randn(2, 5), torch.randn(2, 5)
2024-11-01T17:51:29.9856607Z         >>> batched_dot(x, y)   # output is [5] instead of [2] if batched along the 0th dimension
2024-11-01T17:51:29.9857234Z     
2024-11-01T17:51:29.9857768Z     If there are multiple inputs each of which is batched along different dimensions,
2024-11-01T17:51:29.9858630Z     ``in_dims`` must be a tuple with the batch dimension for each input as
2024-11-01T17:51:29.9859210Z     
2024-11-01T17:51:29.9859646Z         >>> torch.dot                            # [D], [D] -> []
2024-11-01T17:51:29.9860476Z         >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None))  # [N, D], [D] -> [N]
2024-11-01T17:51:29.9861172Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2024-11-01T17:51:29.9861996Z         >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None
2024-11-01T17:51:29.9862648Z     
2024-11-01T17:51:29.9863165Z     If the input is a Python struct, ``in_dims`` must be a tuple containing a struct
2024-11-01T17:51:29.9863852Z     matching the shape of the input:
2024-11-01T17:51:29.9864332Z     
2024-11-01T17:51:29.9864759Z         >>> f = lambda dict: torch.dot(dict['x'], dict['y'])
2024-11-01T17:51:29.9865318Z         >>> x, y = torch.randn(2, 5), torch.randn(5)
2024-11-01T17:51:29.9865856Z         >>> input = {'x': x, 'y': y}
2024-11-01T17:51:29.9866488Z         >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},))
2024-11-01T17:51:29.9867051Z         >>> batched_dot(input)
2024-11-01T17:51:29.9867395Z     
2024-11-01T17:51:29.9867959Z     By default, the output is batched along the first dimension. However, it can be batched
2024-11-01T17:51:29.9868724Z     along any dimension by using ``out_dims``
2024-11-01T17:51:29.9869161Z     
2024-11-01T17:51:29.9869440Z         >>> f = lambda x: x ** 2
2024-11-01T17:51:29.9869828Z         >>> x = torch.randn(2, 5)
2024-11-01T17:51:29.9870289Z         >>> batched_pow = torch.vmap(f, out_dims=1)
2024-11-01T17:51:29.9870771Z         >>> batched_pow(x) # [5, 2]
2024-11-01T17:51:29.9871156Z     
2024-11-01T17:51:29.9871746Z     For any function that uses kwargs, the returned function will not batch the kwargs but will
2024-11-01T17:51:29.9872460Z     accept kwargs
2024-11-01T17:51:29.9872753Z     
2024-11-01T17:51:29.9873035Z         >>> x = torch.randn([2, 5])
2024-11-01T17:51:29.9873448Z         >>> def fn(x, scale=4.):
2024-11-01T17:51:29.9873974Z         >>>   return x * scale
2024-11-01T17:51:29.9874329Z         >>>
2024-11-01T17:51:29.9874644Z         >>> batched_pow = torch.vmap(fn)
2024-11-01T17:51:29.9875162Z         >>> assert torch.allclose(batched_pow(x), x * 4)
2024-11-01T17:51:29.9876024Z         >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5]
2024-11-01T17:51:29.9876642Z     
2024-11-01T17:51:29.9876915Z     .. note::
2024-11-01T17:51:29.9877493Z         vmap does not provide general autobatching or handle variable-length
2024-11-01T17:51:29.9878132Z         sequences out of the box.
2024-11-01T17:51:29.9878511Z     
2024-11-01T17:51:29.9879092Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9879727Z     
2024-11-01T17:51:29.9879984Z       warnings.warn(msg)
2024-11-01T17:51:29.9880316Z     
2024-11-01T17:51:29.9880695Z --- Parse Warning: 18 / 103 ---
2024-11-01T17:51:29.9882450Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=17.
2024-11-01T17:51:29.9884267Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9885174Z     Create a custom operator whose implementation is backed by 1+ triton kernels.
2024-11-01T17:51:29.9885800Z     
2024-11-01T17:51:29.9886305Z         Use this instead of :func:`torch.library.custom_op` when the implementation
2024-11-01T17:51:29.9887161Z         consists of 1+ triton kernels. :func:`torch.library.custom_op` treats
2024-11-01T17:51:29.9887887Z         custom operators as opaque (:func:`torch.compile` and
2024-11-01T17:51:29.9888647Z         :func:`torch.export.export` will never trace into them), but ``triton_op``
2024-11-01T17:51:29.9889480Z         makes the implementation visible to these subsystems, allowing them
2024-11-01T17:51:29.9890107Z         to optimize the triton kernel(s).
2024-11-01T17:51:29.9890526Z     
2024-11-01T17:51:29.9891038Z         Note that ``fn`` must only consist of calls to PyTorch-understood
2024-11-01T17:51:29.9891828Z         operators and triton kernels. Any triton kernels called inside ``fn``
2024-11-01T17:51:29.9892638Z         must be wrapped in a call to :func:`torch._library.capture_triton``.
2024-11-01T17:51:29.9893223Z     
2024-11-01T17:51:29.9893465Z         Args:
2024-11-01T17:51:29.9893984Z             name (str): A name for the custom op that looks like "{namespace}::{name}",
2024-11-01T17:51:29.9894921Z                 e.g. "mylib::my_linear". The name is used as the op's stable identifier
2024-11-01T17:51:29.9895739Z                 in PyTorch subsystems (e.g. torch.export, FX graphs).
2024-11-01T17:51:29.9896508Z                 To avoid name collisions, please use your project name as the namespace;
2024-11-01T17:51:29.9897348Z                 e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace.
2024-11-01T17:51:29.9898232Z             mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates.
2024-11-01T17:51:29.9899168Z                 This MUST be accurate, otherwise, the behavior is undefined. If "unknown",
2024-11-01T17:51:29.9900081Z                 it pessimistically assumes that all inputs to the operator are being mutated.
2024-11-01T17:51:29.9900907Z             schema (None | str): A schema string for the operator. If None
2024-11-01T17:51:29.9901748Z                 (recommended) we'll infer a schema for the operator from its type
2024-11-01T17:51:29.9902521Z                 annotations. We recommend letting us infer a schema unless you
2024-11-01T17:51:29.9903143Z                 have a specific reason not to.
2024-11-01T17:51:29.9903770Z                 Example: "(Tensor x, int y) -> (Tensor, Tensor)".
2024-11-01T17:51:29.9904257Z     
2024-11-01T17:51:29.9904523Z         Example::
2024-11-01T17:51:29.9904811Z     
2024-11-01T17:51:29.9905149Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:29.9905647Z             >>> import torch
2024-11-01T17:51:29.9906133Z             >>> from torch._library import triton_op, capture_triton
2024-11-01T17:51:29.9906909Z             >>>
2024-11-01T17:51:29.9907212Z             >>> import triton
2024-11-01T17:51:29.9907814Z             >>> from triton import language as tl
2024-11-01T17:51:29.9908246Z             >>>
2024-11-01T17:51:29.9908543Z             >>> @triton.jit
2024-11-01T17:51:29.9908908Z             >>> def add_kernel(
2024-11-01T17:51:29.9909287Z             >>>     in_ptr0,
2024-11-01T17:51:29.9909640Z             >>>     in_ptr1,
2024-11-01T17:51:29.9909977Z             >>>     out_ptr,
2024-11-01T17:51:29.9910339Z             >>>     n_elements,
2024-11-01T17:51:29.9910764Z             >>>     BLOCK_SIZE: "tl.constexpr",
2024-11-01T17:51:29.9911201Z             >>> ):
2024-11-01T17:51:29.9911557Z             >>>     pid = tl.program_id(axis=0)
2024-11-01T17:51:29.9912043Z             >>>     block_start = pid * BLOCK_SIZE
2024-11-01T17:51:29.9912608Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2024-11-01T17:51:29.9913163Z             >>>     mask = offsets < n_elements
2024-11-01T17:51:29.9913680Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2024-11-01T17:51:29.9914339Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2024-11-01T17:51:29.9914830Z             >>>     output = x + y
2024-11-01T17:51:29.9915309Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2024-11-01T17:51:29.9915807Z             >>>
2024-11-01T17:51:29.9916181Z             >>> @triton_op("mylib::add", mutates_args={})
2024-11-01T17:51:29.9916919Z             >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
2024-11-01T17:51:29.9917533Z             >>>     output = torch.empty_like(x)
2024-11-01T17:51:29.9918025Z             >>>     n_elements = output.numel()
2024-11-01T17:51:29.9918463Z             >>>
2024-11-01T17:51:29.9918774Z             >>>     def grid(meta):
2024-11-01T17:51:29.9919303Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2024-11-01T17:51:29.9919835Z             >>>
2024-11-01T17:51:29.9920325Z             >>>     # NB: we need to wrap the triton kernel in a call to capture_triton
2024-11-01T17:51:29.9921091Z             >>>     capture_triton(add_kernel)[grid](x, y, output, n_elements, 16)
2024-11-01T17:51:29.9921677Z             >>>     return output
2024-11-01T17:51:29.9922052Z             >>>
2024-11-01T17:51:29.9922359Z             >>> @torch.compile
2024-11-01T17:51:29.9922738Z             >>> def f(x, y):
2024-11-01T17:51:29.9923096Z             >>>     return add(x, y)
2024-11-01T17:51:29.9923588Z             >>>
2024-11-01T17:51:29.9923770Z             >>> x = torch.randn(3, device="cuda")
2024-11-01T17:51:29.9923932Z             >>> y = torch.randn(3, device="cuda")
2024-11-01T17:51:29.9924035Z             >>>
2024-11-01T17:51:29.9924164Z             >>> z = f(x, y)
2024-11-01T17:51:29.9924331Z             >>> assert torch.allclose(z, x + y)
2024-11-01T17:51:29.9924441Z     
2024-11-01T17:51:29.9924542Z         
2024-11-01T17:51:29.9924999Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9925094Z     
2024-11-01T17:51:29.9925215Z       warnings.warn(msg)
2024-11-01T17:51:29.9925330Z     
2024-11-01T17:51:29.9925560Z --- Parse Warning: 19 / 103 ---
2024-11-01T17:51:29.9927033Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=capture_triton in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_library/triton.py line=163.
2024-11-01T17:51:29.9927482Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9927770Z     Allows capture of a triton kernel into a graph via make_fx or
2024-11-01T17:51:29.9927976Z         non-strict export (coming soon).
2024-11-01T17:51:29.9928074Z     
2024-11-01T17:51:29.9928411Z         These technologies perform Dispatcher-based tracing (via
2024-11-01T17:51:29.9928691Z         ``__torch_dispatch__``) and cannot see calls to raw triton kernels.
2024-11-01T17:51:29.9929001Z         The ``capture_triton`` API returns a new callable that can actually
2024-11-01T17:51:29.9929196Z         be traced into a graph.
2024-11-01T17:51:29.9929307Z     
2024-11-01T17:51:29.9929418Z         Examples:
2024-11-01T17:51:29.9929514Z     
2024-11-01T17:51:29.9929657Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:29.9929777Z             >>> import torch
2024-11-01T17:51:29.9929909Z             >>> import triton
2024-11-01T17:51:29.9930079Z             >>> from triton import language as tl
2024-11-01T17:51:29.9930352Z             >>> from torch.fx.experimental.proxy_tensor import make_fx
2024-11-01T17:51:29.9930687Z             >>> from torch._higher_order_ops.triton_kernel_wrap import capture_triton
2024-11-01T17:51:29.9930790Z             >>>
2024-11-01T17:51:29.9930918Z             >>> @triton.jit
2024-11-01T17:51:29.9931040Z             >>> def add_kernel(
2024-11-01T17:51:29.9931166Z             >>>     in_ptr0,
2024-11-01T17:51:29.9931279Z             >>>     in_ptr1,
2024-11-01T17:51:29.9931388Z             >>>     out_ptr,
2024-11-01T17:51:29.9931516Z             >>>     n_elements,
2024-11-01T17:51:29.9931685Z             >>>     BLOCK_SIZE: "tl.constexpr",
2024-11-01T17:51:29.9931802Z             >>> ):
2024-11-01T17:51:29.9931962Z             >>>     pid = tl.program_id(axis=0)
2024-11-01T17:51:29.9932143Z             >>>     block_start = pid * BLOCK_SIZE
2024-11-01T17:51:29.9932365Z             >>>     offsets = block_start + tl.arange(0, BLOCK_SIZE)
2024-11-01T17:51:29.9932527Z             >>>     mask = offsets < n_elements
2024-11-01T17:51:29.9932732Z             >>>     x = tl.load(in_ptr0 + offsets, mask=mask)
2024-11-01T17:51:29.9932919Z             >>>     y = tl.load(in_ptr1 + offsets, mask=mask)
2024-11-01T17:51:29.9933059Z             >>>     output = x + y
2024-11-01T17:51:29.9933274Z             >>>     tl.store(out_ptr + offsets, output, mask=mask)
2024-11-01T17:51:29.9933392Z             >>>
2024-11-01T17:51:29.9933513Z             >>> def add(x, y):
2024-11-01T17:51:29.9933675Z             >>>     output = torch.empty_like(x)
2024-11-01T17:51:29.9933858Z             >>>     n_elements = output.numel()
2024-11-01T17:51:29.9933961Z             >>>
2024-11-01T17:51:29.9934104Z             >>>     def grid_fn(meta):
2024-11-01T17:51:29.9934351Z             >>>         return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
2024-11-01T17:51:29.9934454Z             >>>
2024-11-01T17:51:29.9934769Z             >>>     capture_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16)
2024-11-01T17:51:29.9934950Z             >>>     return output
2024-11-01T17:51:29.9935066Z             >>>
2024-11-01T17:51:29.9935229Z             >>> x = torch.randn(3, device="cuda")
2024-11-01T17:51:29.9935406Z             >>> y = torch.randn(3, device="cuda")
2024-11-01T17:51:29.9935542Z             >>> gm = make_fx(add)(x, y)
2024-11-01T17:51:29.9935663Z             >>> print(gm.code)
2024-11-01T17:51:29.9935836Z             >>> # def forward(self, x_1, y_1):
2024-11-01T17:51:29.9936183Z             >>> #     empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False)
2024-11-01T17:51:29.9936532Z             >>> #     triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation(
2024-11-01T17:51:29.9936725Z             >>> #         kernel_idx = 0, constant_args_idx = 0,
2024-11-01T17:51:29.9936907Z             >>> #         grid = [(1, 1, 1)], kwargs = {
2024-11-01T17:51:29.9937236Z             >>> #             'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like,
2024-11-01T17:51:29.9937495Z             >>> #             'n_elements': 3, 'BLOCK_SIZE': 16
2024-11-01T17:51:29.9937620Z             >>> #         })
2024-11-01T17:51:29.9937753Z             >>> #     return empty_like
2024-11-01T17:51:29.9937864Z     
2024-11-01T17:51:29.9937965Z         
2024-11-01T17:51:29.9938410Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9938505Z     
2024-11-01T17:51:29.9938628Z       warnings.warn(msg)
2024-11-01T17:51:29.9938739Z     
2024-11-01T17:51:29.9938968Z --- Parse Warning: 20 / 103 ---
2024-11-01T17:51:29.9940574Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=330.
2024-11-01T17:51:29.9941029Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9941141Z     
2024-11-01T17:51:29.9941444Z     Raises an AssertionError if two items are not equal up to desired
2024-11-01T17:51:29.9941554Z     precision.
2024-11-01T17:51:29.9941662Z     
2024-11-01T17:51:29.9941918Z     .. note:: It is recommended to use one of `assert_allclose`,
2024-11-01T17:51:29.9942183Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2024-11-01T17:51:29.9942458Z               instead of this function for more consistent floating point
2024-11-01T17:51:29.9942589Z               comparisons.
2024-11-01T17:51:29.9942683Z     
2024-11-01T17:51:29.9943004Z     The test verifies that the elements of `actual` and `desired` satisfy.
2024-11-01T17:51:29.9943116Z     
2024-11-01T17:51:29.9943419Z         ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
2024-11-01T17:51:29.9943530Z     
2024-11-01T17:51:29.9943873Z     That is a looser test than originally documented, but agrees with what the
2024-11-01T17:51:29.9944214Z     actual implementation in `assert_array_almost_equal` did up to rounding
2024-11-01T17:51:29.9944556Z     vagaries. An exception is raised at conflicting values. For ndarrays this
2024-11-01T17:51:29.9944711Z     delegates to assert_array_almost_equal
2024-11-01T17:51:29.9944822Z     
2024-11-01T17:51:29.9944930Z     Parameters
2024-11-01T17:51:29.9945076Z     ----------
2024-11-01T17:51:29.9945194Z     actual : array_like
2024-11-01T17:51:29.9945317Z         The object to check.
2024-11-01T17:51:29.9945448Z     desired : array_like
2024-11-01T17:51:29.9945568Z         The expected object.
2024-11-01T17:51:29.9945703Z     decimal : int, optional
2024-11-01T17:51:29.9945859Z         Desired precision, default is 7.
2024-11-01T17:51:29.9945996Z     err_msg : str, optional
2024-11-01T17:51:29.9946219Z         The error message to be printed in case of failure.
2024-11-01T17:51:29.9946345Z     verbose : bool, optional
2024-11-01T17:51:29.9946661Z         If True, the conflicting values are appended to the error message.
2024-11-01T17:51:29.9946758Z     
2024-11-01T17:51:29.9946939Z     Raises
2024-11-01T17:51:29.9947068Z     ------
2024-11-01T17:51:29.9947180Z     AssertionError
2024-11-01T17:51:29.9947478Z       If actual and desired are not equal up to specified precision.
2024-11-01T17:51:29.9947575Z     
2024-11-01T17:51:29.9947696Z     See Also
2024-11-01T17:51:29.9947825Z     --------
2024-11-01T17:51:29.9948168Z     assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:29.9948354Z                      relative and/or absolute precision.
2024-11-01T17:51:29.9948636Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:29.9948751Z     
2024-11-01T17:51:29.9948870Z     Examples
2024-11-01T17:51:29.9949011Z     --------
2024-11-01T17:51:29.9949239Z     >>> from torch._numpy.testing import assert_almost_equal
2024-11-01T17:51:29.9949427Z     >>> assert_almost_equal(2.3333333333333, 2.33333334)
2024-11-01T17:51:29.9949679Z     >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
2024-11-01T17:51:29.9949829Z     Traceback (most recent call last):
2024-11-01T17:51:29.9949945Z         ...
2024-11-01T17:51:29.9950061Z     AssertionError:
2024-11-01T17:51:29.9950234Z     Arrays are not almost equal to 10 decimals
2024-11-01T17:51:29.9950362Z      ACTUAL: 2.3333333333333
2024-11-01T17:51:29.9950475Z      DESIRED: 2.33333334
2024-11-01T17:51:29.9950583Z     
2024-11-01T17:51:29.9950790Z     >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
2024-11-01T17:51:29.9950987Z     ...                     np.array([1.0,2.33333334]), decimal=9)
2024-11-01T17:51:29.9951132Z     Traceback (most recent call last):
2024-11-01T17:51:29.9951315Z         ...
2024-11-01T17:51:29.9951445Z     AssertionError:
2024-11-01T17:51:29.9951617Z     Arrays are not almost equal to 9 decimals
2024-11-01T17:51:29.9951738Z     <BLANKLINE>
2024-11-01T17:51:29.9951874Z     Mismatched elements: 1 / 2 (50%)
2024-11-01T17:51:29.9952133Z     Max absolute difference: 6.666699636781459e-09
2024-11-01T17:51:29.9952378Z     Max relative difference: 2.8571569790287484e-09
2024-11-01T17:51:29.9952578Z      x: torch.ndarray([1.0000, 2.3333], dtype=float64)
2024-11-01T17:51:29.9952780Z      y: torch.ndarray([1.0000, 2.3333], dtype=float64)
2024-11-01T17:51:29.9952874Z     
2024-11-01T17:51:29.9952983Z     
2024-11-01T17:51:29.9953412Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9953508Z     
2024-11-01T17:51:29.9953639Z       warnings.warn(msg)
2024-11-01T17:51:29.9953736Z     
2024-11-01T17:51:29.9954109Z --- Parse Warning: 21 / 103 ---
2024-11-01T17:51:29.9955638Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=455.
2024-11-01T17:51:29.9956101Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9956201Z     
2024-11-01T17:51:29.9956521Z     Raises an AssertionError if two items are not equal up to significant
2024-11-01T17:51:29.9956637Z     digits.
2024-11-01T17:51:29.9956733Z     
2024-11-01T17:51:29.9957007Z     .. note:: It is recommended to use one of `assert_allclose`,
2024-11-01T17:51:29.9957262Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2024-11-01T17:51:29.9957549Z               instead of this function for more consistent floating point
2024-11-01T17:51:29.9957667Z               comparisons.
2024-11-01T17:51:29.9957764Z     
2024-11-01T17:51:29.9958039Z     Given two numbers, check that they are approximately equal.
2024-11-01T17:51:29.9958346Z     Approximately equal is defined as the number of significant digits
2024-11-01T17:51:29.9958477Z     that agree.
2024-11-01T17:51:29.9958577Z     
2024-11-01T17:51:29.9958703Z     Parameters
2024-11-01T17:51:29.9958838Z     ----------
2024-11-01T17:51:29.9958952Z     actual : scalar
2024-11-01T17:51:29.9959092Z         The object to check.
2024-11-01T17:51:29.9959353Z     desired : scalar
2024-11-01T17:51:29.9959490Z         The expected object.
2024-11-01T17:51:29.9959623Z     significant : int, optional
2024-11-01T17:51:29.9959775Z         Desired precision, default is 7.
2024-11-01T17:51:29.9959909Z     err_msg : str, optional
2024-11-01T17:51:29.9960128Z         The error message to be printed in case of failure.
2024-11-01T17:51:29.9960266Z     verbose : bool, optional
2024-11-01T17:51:29.9960563Z         If True, the conflicting values are appended to the error message.
2024-11-01T17:51:29.9960674Z     
2024-11-01T17:51:29.9960776Z     Raises
2024-11-01T17:51:29.9960902Z     ------
2024-11-01T17:51:29.9961034Z     AssertionError
2024-11-01T17:51:29.9961319Z       If actual and desired are not equal up to specified precision.
2024-11-01T17:51:29.9961426Z     
2024-11-01T17:51:29.9961525Z     See Also
2024-11-01T17:51:29.9961653Z     --------
2024-11-01T17:51:29.9961992Z     assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:29.9962178Z                      relative and/or absolute precision.
2024-11-01T17:51:29.9962472Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:29.9962566Z     
2024-11-01T17:51:29.9962740Z     Examples
2024-11-01T17:51:29.9962867Z     --------
2024-11-01T17:51:29.9963353Z     >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)  # doctest: +SKIP
2024-11-01T17:51:29.9963813Z     >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,  # doctest: +SKIP
2024-11-01T17:51:29.9963984Z     ...                                significant=8)
2024-11-01T17:51:29.9964501Z     >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,  # doctest: +SKIP
2024-11-01T17:51:29.9964668Z     ...                                significant=8)
2024-11-01T17:51:29.9964828Z     Traceback (most recent call last):
2024-11-01T17:51:29.9964929Z         ...
2024-11-01T17:51:29.9965045Z     AssertionError:
2024-11-01T17:51:29.9965240Z     Items are not equal to 8 significant digits:
2024-11-01T17:51:29.9965404Z      ACTUAL: 1.234567e-21
2024-11-01T17:51:29.9965576Z      DESIRED: 1.2345672e-21
2024-11-01T17:51:29.9965671Z     
2024-11-01T17:51:29.9965894Z     the evaluated condition that raises the exception is
2024-11-01T17:51:29.9966003Z     
2024-11-01T17:51:29.9966334Z     >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
2024-11-01T17:51:29.9966447Z     True
2024-11-01T17:51:29.9966541Z     
2024-11-01T17:51:29.9966648Z     
2024-11-01T17:51:29.9967075Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9967172Z     
2024-11-01T17:51:29.9967308Z       warnings.warn(msg)
2024-11-01T17:51:29.9967403Z     
2024-11-01T17:51:29.9967639Z --- Parse Warning: 22 / 103 ---
2024-11-01T17:51:29.9969142Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=734.
2024-11-01T17:51:29.9969600Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:29.9981196Z     
2024-11-01T17:51:29.9981670Z     Raises an AssertionError if two array_like objects are not equal.
2024-11-01T17:51:29.9981767Z     
2024-11-01T17:51:29.9982077Z     Given two array_like objects, check that the shape is equal and all
2024-11-01T17:51:29.9982412Z     elements of these objects are equal (but see the Notes for the special
2024-11-01T17:51:29.9982707Z     handling of a scalar). An exception is raised at shape mismatch or
2024-11-01T17:51:29.9983085Z     conflicting values. In contrast to the standard usage in numpy, NaNs
2024-11-01T17:51:29.9983401Z     are compared like numbers, no assertion is raised if both objects have
2024-11-01T17:51:29.9983547Z     NaNs in the same positions.
2024-11-01T17:51:29.9983640Z     
2024-11-01T17:51:29.9983968Z     The usual caution for verifying equality with floating point numbers is
2024-11-01T17:51:29.9984264Z     advised.
2024-11-01T17:51:29.9984359Z     
2024-11-01T17:51:29.9984482Z     Parameters
2024-11-01T17:51:29.9984686Z     ----------
2024-11-01T17:51:29.9984796Z     x : array_like
2024-11-01T17:51:29.9984946Z         The actual object to check.
2024-11-01T17:51:29.9985054Z     y : array_like
2024-11-01T17:51:29.9985210Z         The desired, expected object.
2024-11-01T17:51:29.9985332Z     err_msg : str, optional
2024-11-01T17:51:29.9985565Z         The error message to be printed in case of failure.
2024-11-01T17:51:29.9985693Z     verbose : bool, optional
2024-11-01T17:51:29.9985995Z         If True, the conflicting values are appended to the error message.
2024-11-01T17:51:29.9986133Z     strict : bool, optional
2024-11-01T17:51:29.9986429Z         If True, raise an AssertionError when either the shape or the data
2024-11-01T17:51:29.9986698Z         type of the array_like objects does not match. The special
2024-11-01T17:51:29.9986991Z         handling for scalars mentioned in the Notes section is disabled.
2024-11-01T17:51:29.9987105Z     
2024-11-01T17:51:29.9987207Z     Raises
2024-11-01T17:51:29.9987333Z     ------
2024-11-01T17:51:29.9987459Z     AssertionError
2024-11-01T17:51:29.9987648Z         If actual and desired objects are not equal.
2024-11-01T17:51:29.9987758Z     
2024-11-01T17:51:29.9987861Z     See Also
2024-11-01T17:51:29.9987988Z     --------
2024-11-01T17:51:29.9988331Z     assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:29.9988516Z                      relative and/or absolute precision.
2024-11-01T17:51:29.9988891Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:29.9988987Z     
2024-11-01T17:51:29.9989087Z     Notes
2024-11-01T17:51:29.9989228Z     -----
2024-11-01T17:51:29.9989522Z     When one of `x` and `y` is a scalar and the other is array_like, the
2024-11-01T17:51:29.9989858Z     function checks that each element of the array_like object is equal to
2024-11-01T17:51:29.9990189Z     the scalar. This behaviour can be disabled with the `strict` parameter.
2024-11-01T17:51:29.9990299Z     
2024-11-01T17:51:29.9990403Z     Examples
2024-11-01T17:51:29.9990528Z     --------
2024-11-01T17:51:29.9990732Z     The first assert does not raise an exception:
2024-11-01T17:51:29.9990826Z     
2024-11-01T17:51:29.9991055Z     >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
2024-11-01T17:51:29.9991241Z     ...                               [np.exp(0),2.33333, np.nan])
2024-11-01T17:51:29.9991347Z     
2024-11-01T17:51:29.9991684Z     Use `assert_allclose` or one of the nulp (number of floating point values)
2024-11-01T17:51:29.9991829Z     functions for these cases instead:
2024-11-01T17:51:29.9991935Z     
2024-11-01T17:51:29.9992136Z     >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
2024-11-01T17:51:29.9992337Z     ...                            [1, np.sqrt(np.pi)**2, np.nan],
2024-11-01T17:51:29.9992568Z     ...                            rtol=1e-10, atol=0)
2024-11-01T17:51:29.9992678Z     
2024-11-01T17:51:29.9992977Z     As mentioned in the Notes section, `assert_array_equal` has special
2024-11-01T17:51:29.9993301Z     handling for scalars. Here the test checks that each value in `x` is 3:
2024-11-01T17:51:29.9993408Z     
2024-11-01T17:51:29.9993552Z     >>> x = np.full((2, 5), fill_value=3)
2024-11-01T17:51:29.9993723Z     >>> np.testing.assert_array_equal(x, 3)
2024-11-01T17:51:29.9993818Z     
2024-11-01T17:51:29.9994291Z     Use `strict` to raise an AssertionError when comparing a scalar with an
2024-11-01T17:51:29.9994408Z     array:
2024-11-01T17:51:29.9994503Z     
2024-11-01T17:51:29.9994732Z     >>> np.testing.assert_array_equal(x, 3, strict=True)
2024-11-01T17:51:29.9994879Z     Traceback (most recent call last):
2024-11-01T17:51:29.9994995Z         ...
2024-11-01T17:51:29.9995111Z     AssertionError:
2024-11-01T17:51:29.9995232Z     Arrays are not equal
2024-11-01T17:51:29.9995353Z     <BLANKLINE>
2024-11-01T17:51:29.9995479Z     (shapes (2, 5), () mismatch)
2024-11-01T17:51:29.9995716Z      x: torch.ndarray([[3, 3, 3, 3, 3],
2024-11-01T17:51:29.9995831Z             [3, 3, 3, 3, 3]])
2024-11-01T17:51:29.9995949Z      y: torch.ndarray(3)
2024-11-01T17:51:29.9996059Z     
2024-11-01T17:51:29.9996366Z     The `strict` parameter also ensures that the array data types match:
2024-11-01T17:51:29.9996475Z     
2024-11-01T17:51:29.9996598Z     >>> x = np.array([2, 2, 2])
2024-11-01T17:51:29.9996793Z     >>> y = np.array([2., 2., 2.], dtype=np.float32)
2024-11-01T17:51:29.9997002Z     >>> np.testing.assert_array_equal(x, y, strict=True)
2024-11-01T17:51:29.9997153Z     Traceback (most recent call last):
2024-11-01T17:51:29.9997270Z         ...
2024-11-01T17:51:29.9997386Z     AssertionError:
2024-11-01T17:51:29.9997520Z     Arrays are not equal
2024-11-01T17:51:29.9997626Z     <BLANKLINE>
2024-11-01T17:51:29.9997829Z     (dtypes dtype("int64"), dtype("float32") mismatch)
2024-11-01T17:51:29.9997971Z      x: torch.ndarray([2, 2, 2])
2024-11-01T17:51:29.9998106Z      y: torch.ndarray([2., 2., 2.])
2024-11-01T17:51:29.9998215Z     
2024-11-01T17:51:29.9998659Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:29.9998765Z     
2024-11-01T17:51:29.9998884Z       warnings.warn(msg)
2024-11-01T17:51:29.9998978Z     
2024-11-01T17:51:29.9999224Z --- Parse Warning: 23 / 103 ---
2024-11-01T17:51:30.0000855Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=840.
2024-11-01T17:51:30.0001314Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0001411Z     
2024-11-01T17:51:30.0001728Z     Raises an AssertionError if two objects are not equal up to desired
2024-11-01T17:51:30.0001837Z     precision.
2024-11-01T17:51:30.0001932Z     
2024-11-01T17:51:30.0002213Z     .. note:: It is recommended to use one of `assert_allclose`,
2024-11-01T17:51:30.0002468Z               `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
2024-11-01T17:51:30.0002756Z               instead of this function for more consistent floating point
2024-11-01T17:51:30.0002874Z               comparisons.
2024-11-01T17:51:30.0002986Z     
2024-11-01T17:51:30.0003330Z     The test verifies identical shapes and that the elements of ``actual`` and
2024-11-01T17:51:30.0003449Z     ``desired`` satisfy.
2024-11-01T17:51:30.0003560Z     
2024-11-01T17:51:30.0003807Z         ``abs(desired-actual) < 1.5 * 10**(-decimal)``
2024-11-01T17:51:30.0003922Z     
2024-11-01T17:51:30.0004261Z     That is a looser test than originally documented, but agrees with what the
2024-11-01T17:51:30.0004604Z     actual implementation did up to rounding vagaries. An exception is raised
2024-11-01T17:51:30.0004956Z     at shape mismatch or conflicting values. In contrast to the standard usage
2024-11-01T17:51:30.0005280Z     in numpy, NaNs are compared like numbers, no assertion is raised if both
2024-11-01T17:51:30.0005468Z     objects have NaNs in the same positions.
2024-11-01T17:51:30.0005564Z     
2024-11-01T17:51:30.0005689Z     Parameters
2024-11-01T17:51:30.0005820Z     ----------
2024-11-01T17:51:30.0005931Z     x : array_like
2024-11-01T17:51:30.0006079Z         The actual object to check.
2024-11-01T17:51:30.0006187Z     y : array_like
2024-11-01T17:51:30.0006340Z         The desired, expected object.
2024-11-01T17:51:30.0006462Z     decimal : int, optional
2024-11-01T17:51:30.0006905Z         Desired precision, default is 6.
2024-11-01T17:51:30.0007032Z     err_msg : str, optional
2024-11-01T17:51:30.0007249Z       The error message to be printed in case of failure.
2024-11-01T17:51:30.0007389Z     verbose : bool, optional
2024-11-01T17:51:30.0007687Z         If True, the conflicting values are appended to the error message.
2024-11-01T17:51:30.0007800Z     
2024-11-01T17:51:30.0007900Z     Raises
2024-11-01T17:51:30.0008203Z     ------
2024-11-01T17:51:30.0008332Z     AssertionError
2024-11-01T17:51:30.0008614Z         If actual and desired are not equal up to specified precision.
2024-11-01T17:51:30.0008727Z     
2024-11-01T17:51:30.0008830Z     See Also
2024-11-01T17:51:30.0008972Z     --------
2024-11-01T17:51:30.0009302Z     assert_allclose: Compare two array_like objects for equality with desired
2024-11-01T17:51:30.0009487Z                      relative and/or absolute precision.
2024-11-01T17:51:30.0009786Z     assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
2024-11-01T17:51:30.0009882Z     
2024-11-01T17:51:30.0010002Z     Examples
2024-11-01T17:51:30.0010134Z     --------
2024-11-01T17:51:30.0010315Z     the first assert does not raise an exception
2024-11-01T17:51:30.0010420Z     
2024-11-01T17:51:30.0010664Z     >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
2024-11-01T17:51:30.0010851Z     ...                                      [1.0,2.333,np.nan])
2024-11-01T17:51:30.0010952Z     
2024-11-01T17:51:30.0011216Z     >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2024-11-01T17:51:30.0011409Z     ...                                      [1.0,2.33339,np.nan], decimal=5)
2024-11-01T17:51:30.0011555Z     Traceback (most recent call last):
2024-11-01T17:51:30.0011669Z         ...
2024-11-01T17:51:30.0011783Z     AssertionError:
2024-11-01T17:51:30.0011964Z     Arrays are not almost equal to 5 decimals
2024-11-01T17:51:30.0012073Z     <BLANKLINE>
2024-11-01T17:51:30.0012226Z     Mismatched elements: 1 / 3 (33.3%)
2024-11-01T17:51:30.0012469Z     Max absolute difference: 5.999999999994898e-05
2024-11-01T17:51:30.0012797Z     Max relative difference: 2.5713661239633743e-05
2024-11-01T17:51:30.0013044Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2024-11-01T17:51:30.0013269Z      y: torch.ndarray([1.0000, 2.3334,    nan], dtype=float64)
2024-11-01T17:51:30.0013378Z     
2024-11-01T17:51:30.0013631Z     >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
2024-11-01T17:51:30.0013821Z     ...                                      [1.0,2.33333, 5], decimal=5)
2024-11-01T17:51:30.0013980Z     Traceback (most recent call last):
2024-11-01T17:51:30.0014081Z         ...
2024-11-01T17:51:30.0014214Z     AssertionError:
2024-11-01T17:51:30.0014382Z     Arrays are not almost equal to 5 decimals
2024-11-01T17:51:30.0014504Z     <BLANKLINE>
2024-11-01T17:51:30.0014639Z     x and y nan location mismatch:
2024-11-01T17:51:30.0014867Z      x: torch.ndarray([1.0000, 2.3333,    nan], dtype=float64)
2024-11-01T17:51:30.0015104Z      y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
2024-11-01T17:51:30.0015204Z     
2024-11-01T17:51:30.0015314Z     
2024-11-01T17:51:30.0015747Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0015856Z     
2024-11-01T17:51:30.0015974Z       warnings.warn(msg)
2024-11-01T17:51:30.0016068Z     
2024-11-01T17:51:30.0016311Z --- Parse Warning: 24 / 103 ---
2024-11-01T17:51:30.0017873Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_numpy/testing/utils.py line=1790.
2024-11-01T17:51:30.0018329Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0018626Z     Context manager that resets warning registry for catching warnings
2024-11-01T17:51:30.0018734Z     
2024-11-01T17:51:30.0019083Z         Warnings can be slippery, because, whenever a warning is triggered, Python
2024-11-01T17:51:30.0019402Z         adds a ``__warningregistry__`` member to the *calling* module.  This makes
2024-11-01T17:51:30.0019755Z         it impossible to retrigger the warning in this module, whatever you put in
2024-11-01T17:51:30.0020102Z         the warnings filters.  This context manager accepts a sequence of `modules`
2024-11-01T17:51:30.0020308Z         as a keyword argument to its constructor and:
2024-11-01T17:51:30.0020499Z     
2024-11-01T17:51:30.0020838Z         * stores and removes any ``__warningregistry__`` entries in given `modules`
2024-11-01T17:51:30.0020945Z           on entry;
2024-11-01T17:51:30.0021207Z         * resets ``__warningregistry__`` to its previous state on exit.
2024-11-01T17:51:30.0021316Z     
2024-11-01T17:51:30.0021644Z         This makes it possible to trigger any warning afresh inside the context
2024-11-01T17:51:30.0021915Z         manager without disturbing the state of warnings outside.
2024-11-01T17:51:30.0022011Z     
2024-11-01T17:51:30.0022360Z         For compatibility with Python 3.0, please consider all arguments to be
2024-11-01T17:51:30.0022511Z         keyword-only.
2024-11-01T17:51:30.0022605Z     
2024-11-01T17:51:30.0022727Z         Parameters
2024-11-01T17:51:30.0022858Z         ----------
2024-11-01T17:51:30.0022997Z         record : bool, optional
2024-11-01T17:51:30.0023259Z             Specifies whether warnings should be captured by a custom
2024-11-01T17:51:30.0023608Z             implementation of ``warnings.showwarning()`` and be appended to a list
2024-11-01T17:51:30.0023910Z             returned by the context manager. Otherwise None is returned by the
2024-11-01T17:51:30.0024228Z             context manager. The objects appended to the list are arguments whose
2024-11-01T17:51:30.0024475Z             attributes mirror the arguments to ``showwarning()``.
2024-11-01T17:51:30.0024614Z         modules : sequence, optional
2024-11-01T17:51:30.0024954Z             Sequence of modules for which to reset warnings registry on entry and
2024-11-01T17:51:30.0025380Z             restore on exit. To work correctly, all 'ignore' filters should
2024-11-01T17:51:30.0025544Z             filter by one of these modules.
2024-11-01T17:51:30.0025639Z     
2024-11-01T17:51:30.0025744Z         Examples
2024-11-01T17:51:30.0025896Z         --------
2024-11-01T17:51:30.0026013Z         >>> import warnings
2024-11-01T17:51:30.0026301Z         >>> with np.testing.clear_and_catch_warnings(  # doctest: +SKIP
2024-11-01T17:51:30.0026479Z         ...         modules=[np.core.fromnumeric]):
2024-11-01T17:51:30.0026704Z         ...     warnings.simplefilter('always')
2024-11-01T17:51:30.0027105Z         ...     warnings.filterwarnings('ignore', module='np.core.fromnumeric')
2024-11-01T17:51:30.0027361Z         ...     # do something that raises a warning but ignore those in
2024-11-01T17:51:30.0027512Z         ...     # np.core.fromnumeric
2024-11-01T17:51:30.0027612Z         
2024-11-01T17:51:30.0028051Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0028152Z     
2024-11-01T17:51:30.0028272Z       warnings.warn(msg)
2024-11-01T17:51:30.0028381Z     
2024-11-01T17:51:30.0028610Z --- Parse Warning: 25 / 103 ---
2024-11-01T17:51:30.0030125Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/conv.py line=355.
2024-11-01T17:51:30.0030573Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0030885Z     Applies a 1D convolution over a quantized input signal composed of
2024-11-01T17:51:30.0031033Z         several quantized input planes.
2024-11-01T17:51:30.0031138Z     
2024-11-01T17:51:30.0031444Z         For details on input arguments, parameters, and implementation see
2024-11-01T17:51:30.0031575Z         :class:`~torch.nn.Conv1d`.
2024-11-01T17:51:30.0031680Z     
2024-11-01T17:51:30.0031797Z         .. note::
2024-11-01T17:51:30.0032102Z             Only `zeros` is supported for the :attr:`padding_mode` argument.
2024-11-01T17:51:30.0032197Z     
2024-11-01T17:51:30.0032302Z         .. note::
2024-11-01T17:51:30.0032568Z             Only `torch.quint8` is supported for the input data type.
2024-11-01T17:51:30.0032662Z     
2024-11-01T17:51:30.0032771Z     
2024-11-01T17:51:30.0032883Z         Attributes:
2024-11-01T17:51:30.0033268Z             weight (Tensor):     packed tensor derived from the learnable weight
2024-11-01T17:51:30.0033403Z                                  parameter.
2024-11-01T17:51:30.0033609Z             scale (Tensor):      scalar for the output scale
2024-11-01T17:51:30.0034008Z             zero_point (Tensor): scalar for the output zero point
2024-11-01T17:51:30.0034106Z     
2024-11-01T17:51:30.0034339Z         See :class:`~torch.nn.Conv1d` for other attributes.
2024-11-01T17:51:30.0034433Z     
2024-11-01T17:51:30.0034544Z         Examples::
2024-11-01T17:51:30.0034652Z     
2024-11-01T17:51:30.0034873Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
2024-11-01T17:51:30.0035088Z             >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
2024-11-01T17:51:30.0035246Z             >>> input = torch.randn(20, 16, 100)
2024-11-01T17:51:30.0035406Z             >>> # quantize input to quint8
2024-11-01T17:51:30.0035533Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0035843Z             >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
2024-11-01T17:51:30.0036052Z             ...                                     dtype=torch.quint8)
2024-11-01T17:51:30.0036180Z             >>> output = m(q_input)
2024-11-01T17:51:30.0036286Z     
2024-11-01T17:51:30.0036384Z         
2024-11-01T17:51:30.0036837Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0036931Z     
2024-11-01T17:51:30.0037049Z       warnings.warn(msg)
2024-11-01T17:51:30.0037158Z     
2024-11-01T17:51:30.0037387Z --- Parse Warning: 26 / 103 ---
2024-11-01T17:51:30.0038940Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11.
2024-11-01T17:51:30.0039386Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0039634Z     A quantized long short-term memory (LSTM).
2024-11-01T17:51:30.0039729Z     
2024-11-01T17:51:30.0040138Z         For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
2024-11-01T17:51:30.0040251Z     
2024-11-01T17:51:30.0040388Z         Attributes:
2024-11-01T17:51:30.0040579Z             layers : instances of the `_LSTMLayer`
2024-11-01T17:51:30.0040674Z     
2024-11-01T17:51:30.0040795Z         .. note::
2024-11-01T17:51:30.0041128Z             To access the weights and biases, you need to access them per layer.
2024-11-01T17:51:30.0041381Z             See examples in :class:`~torch.ao.nn.quantizable.LSTM`
2024-11-01T17:51:30.0041493Z     
2024-11-01T17:51:30.0041603Z         Examples::
2024-11-01T17:51:30.0041742Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0041883Z             >>> custom_module_config = {
2024-11-01T17:51:30.0042152Z             ...     'float_to_observed_custom_module_class': {
2024-11-01T17:51:30.0042344Z             ...         nn.LSTM: nn.quantizable.LSTM,
2024-11-01T17:51:30.0042457Z             ...     },
2024-11-01T17:51:30.0042757Z             ...     'observed_to_quantized_custom_module_class': {
2024-11-01T17:51:30.0042967Z             ...         nn.quantizable.LSTM: nn.quantized.LSTM,
2024-11-01T17:51:30.0043086Z             ...     }
2024-11-01T17:51:30.0043187Z             ... }
2024-11-01T17:51:30.0043498Z             >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
2024-11-01T17:51:30.0043813Z             >>> tq.convert(model, convert_custom_module_class=custom_module_config)
2024-11-01T17:51:30.0043911Z         
2024-11-01T17:51:30.0044357Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0044451Z     
2024-11-01T17:51:30.0044582Z       warnings.warn(msg)
2024-11-01T17:51:30.0044676Z     
2024-11-01T17:51:30.0044903Z --- Parse Warning: 27 / 103 ---
2024-11-01T17:51:30.0046644Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227.
2024-11-01T17:51:30.0047172Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0047427Z     Squashes the sparse masks into the appropriate tensors.
2024-11-01T17:51:30.0047526Z     
2024-11-01T17:51:30.0047850Z             If either the `params_to_keep` or `params_to_keep_per_layer` is set,
2024-11-01T17:51:30.0048124Z             the module will have a `sparse_params` dict attached to it.
2024-11-01T17:51:30.0048220Z     
2024-11-01T17:51:30.0048336Z             Args:
2024-11-01T17:51:30.0048611Z                 params_to_keep: List of keys to save in the module or a dict
2024-11-01T17:51:30.0048865Z                                 representing the modules and keys that will have
2024-11-01T17:51:30.0049043Z                                 sparsity parameters saved
2024-11-01T17:51:30.0049370Z                 params_to_keep_per_layer: Dict to specify the params that should be
2024-11-01T17:51:30.0049601Z                                 saved for specific layers. The keys in the dict
2024-11-01T17:51:30.0049850Z                                 should be the module fqn, while the values should
2024-11-01T17:51:30.0050100Z                                 be a list of strings with the names of the variables
2024-11-01T17:51:30.0050280Z                                 to save in the `sparse_params`
2024-11-01T17:51:30.0050390Z     
2024-11-01T17:51:30.0050569Z             Examples:
2024-11-01T17:51:30.0050783Z                 >>> # xdoctest: +SKIP("locals are undefined")
2024-11-01T17:51:30.0051009Z                 >>> # Don't save any sparse params
2024-11-01T17:51:30.0051168Z                 >>> sparsifier.squash_mask()
2024-11-01T17:51:30.0051453Z                 >>> hasattr(model.submodule1, 'sparse_params')
2024-11-01T17:51:30.0051565Z                 False
2024-11-01T17:51:30.0051675Z     
2024-11-01T17:51:30.0051843Z                 >>> # Keep sparse params per layer
2024-11-01T17:51:30.0052005Z                 >>> sparsifier.squash_mask(
2024-11-01T17:51:30.0052163Z                 ...     params_to_keep_per_layer={
2024-11-01T17:51:30.0052432Z                 ...         'submodule1.linear1': ('foo', 'bar'),
2024-11-01T17:51:30.0052699Z                 ...         'submodule2.linear42': ('baz',)
2024-11-01T17:51:30.0052809Z                 ...     })
2024-11-01T17:51:30.0053050Z                 >>> print(model.submodule1.linear1.sparse_params)
2024-11-01T17:51:30.0053239Z                 {'foo': 42, 'bar': 24}
2024-11-01T17:51:30.0053481Z                 >>> print(model.submodule2.linear42.sparse_params)
2024-11-01T17:51:30.0053636Z                 {'baz': 0.1}
2024-11-01T17:51:30.0053733Z     
2024-11-01T17:51:30.0053929Z                 >>> # Keep sparse params for all layers
2024-11-01T17:51:30.0054247Z                 >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar'))
2024-11-01T17:51:30.0054489Z                 >>> print(model.submodule1.linear1.sparse_params)
2024-11-01T17:51:30.0054666Z                 {'foo': 42, 'bar': 24}
2024-11-01T17:51:30.0054908Z                 >>> print(model.submodule2.linear42.sparse_params)
2024-11-01T17:51:30.0055082Z                 {'foo': 42, 'bar': 24}
2024-11-01T17:51:30.0055177Z     
2024-11-01T17:51:30.0055488Z                 >>> # Keep some sparse params for all layers, and specific ones for
2024-11-01T17:51:30.0055619Z                 >>> # some other layers
2024-11-01T17:51:30.0055785Z                 >>> sparsifier.squash_mask(
2024-11-01T17:51:30.0056014Z                 ...     params_to_keep=('foo', 'bar'),
2024-11-01T17:51:30.0056188Z                 ...     params_to_keep_per_layer={
2024-11-01T17:51:30.0056439Z                 ...         'submodule2.linear42': ('baz',)
2024-11-01T17:51:30.0056548Z                 ...     })
2024-11-01T17:51:30.0056792Z                 >>> print(model.submodule1.linear1.sparse_params)
2024-11-01T17:51:30.0057112Z                 {'foo': 42, 'bar': 24}
2024-11-01T17:51:30.0057355Z                 >>> print(model.submodule2.linear42.sparse_params)
2024-11-01T17:51:30.0057569Z                 {'foo': 42, 'bar': 24, 'baz': 0.1}
2024-11-01T17:51:30.0057684Z             
2024-11-01T17:51:30.0058114Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0058209Z     
2024-11-01T17:51:30.0058344Z       warnings.warn(msg)
2024-11-01T17:51:30.0058437Z     
2024-11-01T17:51:30.0058676Z --- Parse Warning: 28 / 103 ---
2024-11-01T17:51:30.0060355Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181.
2024-11-01T17:51:30.0060810Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0060910Z     
2024-11-01T17:51:30.0061269Z     Config object that specifies the supported data types passed as arguments to
2024-11-01T17:51:30.0061633Z     quantize ops in the reference model spec, for input and output activations,
2024-11-01T17:51:30.0061754Z     weights, and biases.
2024-11-01T17:51:30.0061860Z     
2024-11-01T17:51:30.0062082Z     For example, consider the following reference model:
2024-11-01T17:51:30.0062187Z     
2024-11-01T17:51:30.0062471Z       quant1 - [dequant1 - fp32_linear - quant2] - dequant2
2024-11-01T17:51:30.0062565Z     
2024-11-01T17:51:30.0062987Z     The pattern in the square brackets refers to the reference pattern of
2024-11-01T17:51:30.0063315Z     statically quantized linear. Setting the input dtype as `torch.quint8`
2024-11-01T17:51:30.0063663Z     in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
2024-11-01T17:51:30.0063995Z     to the first quantize op (quant1). Similarly, setting the output dtype as
2024-11-01T17:51:30.0064330Z     `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
2024-11-01T17:51:30.0064471Z     the second quantize op (quant2).
2024-11-01T17:51:30.0064564Z     
2024-11-01T17:51:30.0064902Z     Note that the dtype here does not refer to the interface dtypes of the
2024-11-01T17:51:30.0065206Z     op. For example, the "input dtype" here is not the dtype of the input
2024-11-01T17:51:30.0065532Z     tensor passed to the quantized linear op. Though it can still be the
2024-11-01T17:51:30.0065826Z     same as the interface dtype, this is not always the case, e.g. the
2024-11-01T17:51:30.0066154Z     interface dtype is fp32 in dynamic quantization but the "input dtype"
2024-11-01T17:51:30.0066468Z     specified in the DTypeConfig would still be quint8. The semantics of
2024-11-01T17:51:30.0066776Z     dtypes here are the same as the semantics of the dtypes specified in
2024-11-01T17:51:30.0066900Z     the observers.
2024-11-01T17:51:30.0066994Z     
2024-11-01T17:51:30.0067339Z     These dtypes are matched against the ones specified in the user's
2024-11-01T17:51:30.0067664Z     QConfig. If there is a match, and the QConfig satisfies the constraints
2024-11-01T17:51:30.0067997Z     specified in the DTypeConfig (if any), then we will quantize the given
2024-11-01T17:51:30.0068313Z     pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
2024-11-01T17:51:30.0068459Z     the pattern will not be quantized.
2024-11-01T17:51:30.0068567Z     
2024-11-01T17:51:30.0068689Z     Example usage::
2024-11-01T17:51:30.0068796Z     
2024-11-01T17:51:30.0068934Z         >>> # xdoctest: +SKIP(failing)
2024-11-01T17:51:30.0069087Z         >>> dtype_config1 = DTypeConfig(
2024-11-01T17:51:30.0069241Z         ...     input_dtype=torch.quint8,
2024-11-01T17:51:30.0069386Z         ...     output_dtype=torch.quint8,
2024-11-01T17:51:30.0069537Z         ...     weight_dtype=torch.qint8,
2024-11-01T17:51:30.0069674Z         ...     bias_dtype=torch.float)
2024-11-01T17:51:30.0069781Z     
2024-11-01T17:51:30.0069996Z         >>> dtype_config2 = DTypeConfig(
2024-11-01T17:51:30.0070178Z         ...     input_dtype=DTypeWithConstraints(
2024-11-01T17:51:30.0070320Z         ...         dtype=torch.quint8,
2024-11-01T17:51:30.0070465Z         ...         quant_min_lower_bound=0,
2024-11-01T17:51:30.0070631Z         ...         quant_max_upper_bound=255,
2024-11-01T17:51:30.0070734Z         ...     ),
2024-11-01T17:51:30.0070930Z         ...     output_dtype=DTypeWithConstraints(
2024-11-01T17:51:30.0071058Z         ...         dtype=torch.quint8,
2024-11-01T17:51:30.0071203Z         ...         quant_min_lower_bound=0,
2024-11-01T17:51:30.0071374Z         ...         quant_max_upper_bound=255,
2024-11-01T17:51:30.0071476Z         ...     ),
2024-11-01T17:51:30.0071666Z         ...     weight_dtype=DTypeWithConstraints(
2024-11-01T17:51:30.0071796Z         ...         dtype=torch.qint8,
2024-11-01T17:51:30.0072011Z         ...         quant_min_lower_bound=-128,
2024-11-01T17:51:30.0072174Z         ...         quant_max_upper_bound=127,
2024-11-01T17:51:30.0072279Z         ...     ),
2024-11-01T17:51:30.0072425Z         ...     bias_dtype=torch.float)
2024-11-01T17:51:30.0072519Z     
2024-11-01T17:51:30.0072669Z         >>> dtype_config1.input_dtype
2024-11-01T17:51:30.0072777Z         torch.quint8
2024-11-01T17:51:30.0072872Z     
2024-11-01T17:51:30.0073017Z         >>> dtype_config2.input_dtype
2024-11-01T17:51:30.0073129Z         torch.quint8
2024-11-01T17:51:30.0073242Z     
2024-11-01T17:51:30.0073431Z         >>> dtype_config2.input_dtype_with_constraints
2024-11-01T17:51:30.0074400Z         DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None)
2024-11-01T17:51:30.0074513Z     
2024-11-01T17:51:30.0074955Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0075067Z     
2024-11-01T17:51:30.0075186Z       warnings.warn(msg)
2024-11-01T17:51:30.0075294Z     
2024-11-01T17:51:30.0075531Z --- Parse Warning: 29 / 103 ---
2024-11-01T17:51:30.0077511Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301.
2024-11-01T17:51:30.0077971Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0078067Z     
2024-11-01T17:51:30.0078471Z     Takes in optional filter values and generates two tables with desired information.
2024-11-01T17:51:30.0078566Z     
2024-11-01T17:51:30.0078949Z     The generated tables are presented in both a list-of-lists format
2024-11-01T17:51:30.0079043Z     
2024-11-01T17:51:30.0079365Z     The reason for the two tables are that they handle different things:
2024-11-01T17:51:30.0079599Z     1.) the first table handles all tensor level information
2024-11-01T17:51:30.0079922Z     2.) the second table handles and displays all channel based information
2024-11-01T17:51:30.0080029Z     
2024-11-01T17:51:30.0080523Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2024-11-01T17:51:30.0081121Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2024-11-01T17:51:30.0081638Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2024-11-01T17:51:30.0081747Z     
2024-11-01T17:51:30.0081868Z     Tensor table columns:
2024-11-01T17:51:30.0082155Z         idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:30.0082447Z         ----  ---------  ---------   ---------   ---------        ---------
2024-11-01T17:51:30.0082542Z     
2024-11-01T17:51:30.0082733Z     Per-Channel table columns:
2024-11-01T17:51:30.0083057Z         idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:30.0083452Z         ----  ---------  -------  ---------   ---------   ---------        ---------
2024-11-01T17:51:30.0083546Z     
2024-11-01T17:51:30.0083647Z     Args:
2024-11-01T17:51:30.0084037Z         feature_filter (str, optional): Filters the features presented to only those that
2024-11-01T17:51:30.0084185Z             contain this filter substring
2024-11-01T17:51:30.0084437Z             Default = "", results in all the features being printed
2024-11-01T17:51:30.0084803Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:30.0085189Z             Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:30.0085286Z     
2024-11-01T17:51:30.0085435Z     Returns a dictionary with two keys:
2024-11-01T17:51:30.0085697Z         (Dict[str, Tuple[List, List]]) A dict containing two keys:
2024-11-01T17:51:30.0085866Z         "tensor_level_info", "channel_level_info"
2024-11-01T17:51:30.0086034Z             Each key maps to a tuple with:
2024-11-01T17:51:30.0086204Z                 A list of the headers of each table
2024-11-01T17:51:30.0086494Z                 A list of lists containing the table information row by row
2024-11-01T17:51:30.0086753Z                 The 0th index row will contain the headers of the columns
2024-11-01T17:51:30.0086934Z                 The rest of the rows will contain data
2024-11-01T17:51:30.0087043Z     
2024-11-01T17:51:30.0091297Z     Example Use:
2024-11-01T17:51:30.0091514Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0091843Z         >>> mod_report_visualizer.generate_filtered_tables(
2024-11-01T17:51:30.0092022Z         ...     feature_filter = "per_channel_min",
2024-11-01T17:51:30.0092191Z         ...     module_fqn_filter = "block1"
2024-11-01T17:51:30.0092588Z         ... ) # generates table with per_channel_min info for all modules in block 1 of the model
2024-11-01T17:51:30.0092698Z     
2024-11-01T17:51:30.0093191Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0093301Z     
2024-11-01T17:51:30.0093425Z       warnings.warn(msg)
2024-11-01T17:51:30.0093522Z     
2024-11-01T17:51:30.0093767Z --- Parse Warning: 30 / 103 ---
2024-11-01T17:51:30.0095772Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400.
2024-11-01T17:51:30.0096268Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0096377Z     
2024-11-01T17:51:30.0096771Z     Takes in optional filter values and prints out formatted tables of the information.
2024-11-01T17:51:30.0096866Z     
2024-11-01T17:51:30.0097398Z     The reason for the two tables printed out instead of one large one are that they handle different things:
2024-11-01T17:51:30.0097640Z     1.) the first table handles all tensor level information
2024-11-01T17:51:30.0097969Z     2.) the second table handles and displays all channel based information
2024-11-01T17:51:30.0098066Z     
2024-11-01T17:51:30.0098578Z     The reasoning for this is that having all the info in one table can make it ambiguous which collected
2024-11-01T17:51:30.0099171Z         statistics are global, and which are actually per-channel, so it's better to split it up into two
2024-11-01T17:51:30.0099709Z         tables. This also makes the information much easier to digest given the plethora of statistics collected
2024-11-01T17:51:30.0099806Z     
2024-11-01T17:51:30.0099928Z     Tensor table columns:
2024-11-01T17:51:30.0100221Z      idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:30.0100497Z     ----  ---------  ---------   ---------   ---------        ---------
2024-11-01T17:51:30.0100607Z     
2024-11-01T17:51:30.0100914Z     Per-Channel table columns:
2024-11-01T17:51:30.0101009Z     
2024-11-01T17:51:30.0101348Z      idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
2024-11-01T17:51:30.0101646Z     ----  ---------  -------  ---------   ---------   ---------        ---------
2024-11-01T17:51:30.0101756Z     
2024-11-01T17:51:30.0101857Z     Args:
2024-11-01T17:51:30.0102245Z         feature_filter (str, optional): Filters the features presented to only those that
2024-11-01T17:51:30.0102399Z             contain this filter substring
2024-11-01T17:51:30.0102639Z             Default = "", results in all the features being printed
2024-11-01T17:51:30.0103021Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:30.0103390Z             Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:30.0103500Z     
2024-11-01T17:51:30.0103610Z     Example Use:
2024-11-01T17:51:30.0103810Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0104040Z         >>> mod_report_visualizer.generate_table_visualization(
2024-11-01T17:51:30.0104216Z         ...     feature_filter = "per_channel_min",
2024-11-01T17:51:30.0104380Z         ...     module_fqn_filter = "block1"
2024-11-01T17:51:30.0104482Z         ... )
2024-11-01T17:51:30.0104767Z         >>> # prints out neatly formatted table with per_channel_min info
2024-11-01T17:51:30.0104951Z         >>> # for all modules in block 1 of the model
2024-11-01T17:51:30.0105162Z     
2024-11-01T17:51:30.0105658Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0105756Z     
2024-11-01T17:51:30.0105892Z       warnings.warn(msg)
2024-11-01T17:51:30.0105989Z     
2024-11-01T17:51:30.0106233Z --- Parse Warning: 31 / 103 ---
2024-11-01T17:51:30.0108489Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=565.
2024-11-01T17:51:30.0108955Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0109051Z     
2024-11-01T17:51:30.0109402Z     Takes in a feature and optional module_filter and plots of the desired data.
2024-11-01T17:51:30.0109512Z     
2024-11-01T17:51:30.0109915Z     For per channel features, it averages the value across the channels and plots a point
2024-11-01T17:51:30.0110352Z     per module. The reason for this is that for models with hundreds of channels, it can
2024-11-01T17:51:30.0110760Z     be hard to differentiate one channel line from another, and so the point of generating
2024-11-01T17:51:30.0111171Z     a single average point per module is to give a sense of general trends that encourage
2024-11-01T17:51:30.0111292Z     further deep dives.
2024-11-01T17:51:30.0111391Z     
2024-11-01T17:51:30.0111504Z     Note:
2024-11-01T17:51:30.0111900Z         Only features in the report that have tensor value data are plottable by this class
2024-11-01T17:51:30.0112146Z         When the tensor information is plotted, it will plot:
2024-11-01T17:51:30.0112342Z             idx as the x val, feature value as the y_val
2024-11-01T17:51:30.0112595Z         When the channel information is plotted, it will plot:
2024-11-01T17:51:30.0113012Z             the first idx of each module as the x val, feature value as the y_val [for each channel]
2024-11-01T17:51:30.0113378Z             The reason for this is that we want to be able to compare values across the
2024-11-01T17:51:30.0113734Z             channels for same layer, and it will be hard if values are staggered by idx
2024-11-01T17:51:30.0114120Z             This means each module is represented by only 1 x value
2024-11-01T17:51:30.0114236Z     Args:
2024-11-01T17:51:30.0114559Z         feature_filter (str): Filters the features presented to only those that
2024-11-01T17:51:30.0114899Z             contain this filter substring
2024-11-01T17:51:30.0115270Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:30.0115654Z             Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:30.0115750Z     
2024-11-01T17:51:30.0115861Z     Example Use:
2024-11-01T17:51:30.0116062Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0116293Z         >>> mod_report_visualizer.generate_plot_visualization(
2024-11-01T17:51:30.0116490Z         ...     feature_filter = "per_channel_min",
2024-11-01T17:51:30.0116640Z         ...     module_fqn_filter = "block1"
2024-11-01T17:51:30.0116756Z         ... )
2024-11-01T17:51:30.0117010Z         >>> # outputs line plot of per_channel_min information for all
2024-11-01T17:51:30.0117373Z         >>> # modules in block1 of model each channel gets it's own line,
2024-11-01T17:51:30.0117727Z         >>> # and it's plotted across the in-order modules on the x-axis
2024-11-01T17:51:30.0117823Z     
2024-11-01T17:51:30.0118271Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0118368Z     
2024-11-01T17:51:30.0118490Z       warnings.warn(msg)
2024-11-01T17:51:30.0118597Z     
2024-11-01T17:51:30.0118827Z --- Parse Warning: 32 / 103 ---
2024-11-01T17:51:30.0121068Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=645.
2024-11-01T17:51:30.0121599Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0121709Z     
2024-11-01T17:51:30.0122114Z     Takes in a feature and optional module_filter and plots the histogram of desired data.
2024-11-01T17:51:30.0122226Z     
2024-11-01T17:51:30.0122327Z     Note:
2024-11-01T17:51:30.0122731Z         Only features in the report that have tensor value data can be viewed as a histogram
2024-11-01T17:51:30.0123153Z         If you want to plot a histogram from all the channel values of a specific feature for
2024-11-01T17:51:30.0123525Z             a specific model, make sure to specify both the model and the feature properly
2024-11-01T17:51:30.0123916Z             in the filters and you should be able to see a distribution of the channel data
2024-11-01T17:51:30.0124015Z     
2024-11-01T17:51:30.0124132Z     Args:
2024-11-01T17:51:30.0124516Z         feature_filter (str, optional): Filters the features presented to only those that
2024-11-01T17:51:30.0124663Z             contain this filter substring
2024-11-01T17:51:30.0124916Z             Default = "", results in all the features being printed
2024-11-01T17:51:30.0125285Z         module_fqn_filter (str, optional): Only includes modules that contains this string
2024-11-01T17:51:30.0125667Z             Default = "", results in all the modules in the reports to be visible in the table
2024-11-01T17:51:30.0125993Z         num_bins (int, optional): The number of bins to create the histogram with
2024-11-01T17:51:30.0126283Z             Default = 10, the values will be split into 10 equal sized bins
2024-11-01T17:51:30.0126380Z     
2024-11-01T17:51:30.0126490Z     Example Use:
2024-11-01T17:51:30.0126628Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0127043Z         >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
2024-11-01T17:51:30.0127239Z         ...     feature_filter = "per_channel_min",
2024-11-01T17:51:30.0127390Z         ...     module_fqn_filter = "block1"
2024-11-01T17:51:30.0127504Z         ... )
2024-11-01T17:51:30.0127897Z         # outputs histogram of per_channel_min information for all modules in block1 of model
2024-11-01T17:51:30.0128264Z             information is gathered across all channels for all modules in block 1 for the
2024-11-01T17:51:30.0128650Z             per_channel_min and is displayed in a histogram of equally sized bins
2024-11-01T17:51:30.0128746Z     
2024-11-01T17:51:30.0129193Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0129288Z     
2024-11-01T17:51:30.0129422Z       warnings.warn(msg)
2024-11-01T17:51:30.0129517Z     
2024-11-01T17:51:30.0129747Z --- Parse Warning: 33 / 103 ---
2024-11-01T17:51:30.0131329Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/device_mesh.py line=660.
2024-11-01T17:51:30.0131759Z     Caused by: DoctestParseError('Failed to parse doctest in _package_groups')
2024-11-01T17:51:30.0131870Z     
2024-11-01T17:51:30.0132259Z     Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
2024-11-01T17:51:30.0132659Z     The submesh created consists of the dimensions and the communicators indicated by
2024-11-01T17:51:30.0132772Z     ``mesh_dim_names``
2024-11-01T17:51:30.0132868Z     
2024-11-01T17:51:30.0132983Z     Args:
2024-11-01T17:51:30.0133326Z         mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the
2024-11-01T17:51:30.0133605Z         mesh dimension of the DeviceMesh to create the submesh for.
2024-11-01T17:51:30.0133709Z     Returns:
2024-11-01T17:51:30.0133912Z         A :class:`DeviceMesh` object
2024-11-01T17:51:30.0134008Z     
2024-11-01T17:51:30.0134489Z     The following program runs on each process/rank in an SPMD manner in a world size of 8.
2024-11-01T17:51:30.0134624Z     In the first example:
2024-11-01T17:51:30.0135021Z         Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
2024-11-01T17:51:30.0135435Z         Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
2024-11-01T17:51:30.0135788Z         Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
2024-11-01T17:51:30.0136152Z         Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
2024-11-01T17:51:30.0136500Z         Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
2024-11-01T17:51:30.0136864Z         Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
2024-11-01T17:51:30.0136959Z     
2024-11-01T17:51:30.0137086Z     In the second example:
2024-11-01T17:51:30.0137539Z         Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
2024-11-01T17:51:30.0137973Z         Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
2024-11-01T17:51:30.0138411Z         Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
2024-11-01T17:51:30.0138834Z         Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
2024-11-01T17:51:30.0138947Z     
2024-11-01T17:51:30.0139087Z     Example::
2024-11-01T17:51:30.0139232Z         >>> # xdoctest: +SKIP("no rank")
2024-11-01T17:51:30.0139492Z         >>> from torch.distributed.device_mesh import DeviceMesh
2024-11-01T17:51:30.0139597Z         >>>
2024-11-01T17:51:30.0139899Z         >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
2024-11-01T17:51:30.0140181Z         >>> # of cross-host(dim 0), and within-host (dim 1).
2024-11-01T17:51:30.0140565Z         >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2024-11-01T17:51:30.0140697Z         >>> tp_mesh = mesh_2d["tp"]
2024-11-01T17:51:30.0140823Z         >>> dp_mesh = mesh_2d["dp"]
2024-11-01T17:51:30.0140938Z         >>>
2024-11-01T17:51:30.0141067Z         >>> # Initialize a 3D mesh.
2024-11-01T17:51:30.0141479Z         >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
2024-11-01T17:51:30.0141974Z         >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
2024-11-01T17:51:30.0142145Z         >>> dp_cp_mesh = mesh_3d["dp", "cp"]
2024-11-01T17:51:30.0142298Z         >>> cp_dp_mesh = mesh_3d["cp", "dp"]
2024-11-01T17:51:30.0142395Z     
2024-11-01T17:51:30.0143532Z     Original Error: SyntaxError('positional argument follows keyword argument', ('<source_block>', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83))
2024-11-01T17:51:30.0143633Z     
2024-11-01T17:51:30.0144002Z     mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
2024-11-01T17:51:30.0144187Z                                                                                      ^
2024-11-01T17:51:30.0144324Z       warnings.warn(msg)
2024-11-01T17:51:30.0144419Z     
2024-11-01T17:51:30.0144650Z --- Parse Warning: 34 / 103 ---
2024-11-01T17:51:30.0146220Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py line=2940.
2024-11-01T17:51:30.0146661Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0146767Z     
2024-11-01T17:51:30.0147075Z     Gathers picklable objects from the whole group in a single process.
2024-11-01T17:51:30.0147230Z     
2024-11-01T17:51:30.0147636Z     Similar to :func:`gather`, but Python objects can be passed in. Note that the
2024-11-01T17:51:30.0147861Z     object must be picklable in order to be gathered.
2024-11-01T17:51:30.0147959Z     
2024-11-01T17:51:30.0148059Z     Args:
2024-11-01T17:51:30.0148257Z         obj (Any): Input object. Must be picklable.
2024-11-01T17:51:30.0148558Z         object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
2024-11-01T17:51:30.0148844Z             should be correctly sized as the size of the group for this
2024-11-01T17:51:30.0149237Z             collective and will contain the output. Must be ``None`` on non-dst
2024-11-01T17:51:30.0149381Z             ranks. (default is ``None``)
2024-11-01T17:51:30.0149947Z         dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0)
2024-11-01T17:51:30.0150269Z         group: (ProcessGroup, optional): The process group to work on. If None,
2024-11-01T17:51:30.0150557Z             the default process group will be used. Default is ``None``.
2024-11-01T17:51:30.0150654Z     
2024-11-01T17:51:30.0150775Z     Returns:
2024-11-01T17:51:30.0151056Z         None. On the ``dst`` rank, ``object_gather_list`` will contain the
2024-11-01T17:51:30.0151208Z         output of the collective.
2024-11-01T17:51:30.0151304Z     
2024-11-01T17:51:30.0151628Z     .. note:: Note that this API differs slightly from the gather collective
2024-11-01T17:51:30.0151972Z         since it does not provide an async_op handle and thus will be a blocking
2024-11-01T17:51:30.0152077Z         call.
2024-11-01T17:51:30.0152187Z     
2024-11-01T17:51:30.0152603Z     .. note:: For NCCL-based processed groups, internal tensor representations
2024-11-01T17:51:30.0152935Z         of objects must be moved to the GPU device before communication takes
2024-11-01T17:51:30.0153144Z         place. In this case, the device used is given by
2024-11-01T17:51:30.0153536Z         ``torch.cuda.current_device()`` and it is the user's responsiblity to
2024-11-01T17:51:30.0154007Z         ensure that this is set so that each rank has an individual GPU, via
2024-11-01T17:51:30.0154145Z         ``torch.cuda.set_device()``.
2024-11-01T17:51:30.0154255Z     
2024-11-01T17:51:30.0154369Z     .. warning::
2024-11-01T17:51:30.0154670Z         :func:`gather_object` uses ``pickle`` module implicitly, which is
2024-11-01T17:51:30.0155000Z         known to be insecure. It is possible to construct malicious pickle data
2024-11-01T17:51:30.0155359Z         which will execute arbitrary code during unpickling. Only call this
2024-11-01T17:51:30.0155515Z         function with data you trust.
2024-11-01T17:51:30.0155610Z     
2024-11-01T17:51:30.0155732Z     .. warning::
2024-11-01T17:51:30.0156035Z         Calling :func:`gather_object` with GPU tensors is not well supported
2024-11-01T17:51:30.0156469Z         and inefficient as it incurs GPU -> CPU transfer since tensors would be
2024-11-01T17:51:30.0156705Z         pickled. Please consider using :func:`gather` instead.
2024-11-01T17:51:30.0156806Z     
2024-11-01T17:51:30.0156958Z     Example::
2024-11-01T17:51:30.0157159Z         >>> # xdoctest: +SKIP("need process group init")
2024-11-01T17:51:30.0157432Z         >>> # Note: Process group initialization omitted on each rank.
2024-11-01T17:51:30.0157597Z         >>> import torch.distributed as dist
2024-11-01T17:51:30.0157736Z         >>> # Assumes world_size of 3.
2024-11-01T17:51:30.0158005Z         >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
2024-11-01T17:51:30.0158182Z         >>> output = [None for _ in gather_objects]
2024-11-01T17:51:30.0158318Z         >>> dist.gather_object(
2024-11-01T17:51:30.0158482Z         ...     gather_objects[dist.get_rank()],
2024-11-01T17:51:30.0158686Z         ...     output if dist.get_rank() == 0 else None,
2024-11-01T17:51:30.0158794Z         ...     dst=0
2024-11-01T17:51:30.0158896Z         ... )
2024-11-01T17:51:30.0159022Z         >>> # On rank 0
2024-11-01T17:51:30.0159165Z         >>> output
2024-11-01T17:51:30.0159341Z         ['foo', 12, {1: 2}]
2024-11-01T17:51:30.0159435Z     
2024-11-01T17:51:30.0159934Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0160032Z     
2024-11-01T17:51:30.0160152Z       warnings.warn(msg)
2024-11-01T17:51:30.0160261Z     
2024-11-01T17:51:30.0160492Z --- Parse Warning: 35 / 103 ---
2024-11-01T17:51:30.0161905Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/launch.py line=2.
2024-11-01T17:51:30.0162353Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0162461Z     
2024-11-01T17:51:30.0162622Z     Module ``torch.distributed.launch``.
2024-11-01T17:51:30.0162717Z     
2024-11-01T17:51:30.0163086Z     ``torch.distributed.launch`` is a module that spawns up multiple distributed
2024-11-01T17:51:30.0163296Z     training processes on each of the training nodes.
2024-11-01T17:51:30.0163409Z     
2024-11-01T17:51:30.0163520Z     .. warning::
2024-11-01T17:51:30.0163613Z     
2024-11-01T17:51:30.0164100Z         This module is going to be deprecated in favor of :ref:`torchrun <launcher-api>`.
2024-11-01T17:51:30.0164195Z     
2024-11-01T17:51:30.0164646Z     The utility can be used for single-node distributed training, in which one or
2024-11-01T17:51:30.0164999Z     more processes per node will be spawned. The utility can be used for either
2024-11-01T17:51:30.0165332Z     CPU training or GPU training. If the utility is used for GPU training,
2024-11-01T17:51:30.0165688Z     each distributed process will be operating on a single GPU. This can achieve
2024-11-01T17:51:30.0166091Z     well-improved single-node training performance. It can also be used in
2024-11-01T17:51:30.0166564Z     multi-node distributed training, by spawning up multiple processes on each node
2024-11-01T17:51:30.0166968Z     for well-improved multi-node distributed training performance as well.
2024-11-01T17:51:30.0167313Z     This will especially be beneficial for systems with multiple Infiniband
2024-11-01T17:51:30.0167760Z     interfaces that have direct-GPU support, since all of them can be utilized for
2024-11-01T17:51:30.0167923Z     aggregated communication bandwidth.
2024-11-01T17:51:30.0168019Z     
2024-11-01T17:51:30.0168461Z     In both cases of single-node distributed training or multi-node distributed
2024-11-01T17:51:30.0168854Z     training, this utility will launch the given number of processes per node
2024-11-01T17:51:30.0169273Z     (``--nproc-per-node``). If used for GPU training, this number needs to be less
2024-11-01T17:51:30.0169623Z     or equal to the number of GPUs on the current system (``nproc_per_node``),
2024-11-01T17:51:30.0169920Z     and each process will be operating on a single GPU from *GPU 0 to
2024-11-01T17:51:30.0170111Z     GPU (nproc_per_node - 1)*.
2024-11-01T17:51:30.0170207Z     
2024-11-01T17:51:30.0170351Z     **How to use this module:**
2024-11-01T17:51:30.0170445Z     
2024-11-01T17:51:30.0170719Z     1. Single-Node multi-process distributed training
2024-11-01T17:51:30.0170830Z     
2024-11-01T17:51:30.0170931Z     ::
2024-11-01T17:51:30.0171038Z     
2024-11-01T17:51:30.0171445Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2024-11-01T17:51:30.0171790Z                    YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
2024-11-01T17:51:30.0171987Z                    arguments of your training script)
2024-11-01T17:51:30.0172081Z     
2024-11-01T17:51:30.0172468Z     2. Multi-Node multi-process distributed training: (e.g. two nodes)
2024-11-01T17:51:30.0172563Z     
2024-11-01T17:51:30.0172669Z     
2024-11-01T17:51:30.0172878Z     Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
2024-11-01T17:51:30.0172973Z     
2024-11-01T17:51:30.0173086Z     ::
2024-11-01T17:51:30.0173180Z     
2024-11-01T17:51:30.0173641Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2024-11-01T17:51:30.0173993Z                    --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
2024-11-01T17:51:30.0174368Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2024-11-01T17:51:30.0174602Z                    and all other arguments of your training script)
2024-11-01T17:51:30.0174697Z     
2024-11-01T17:51:30.0174812Z     Node 2:
2024-11-01T17:51:30.0174910Z     
2024-11-01T17:51:30.0175021Z     ::
2024-11-01T17:51:30.0175115Z     
2024-11-01T17:51:30.0175519Z         python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
2024-11-01T17:51:30.0175831Z                    --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
2024-11-01T17:51:30.0176204Z                    --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
2024-11-01T17:51:30.0176436Z                    and all other arguments of your training script)
2024-11-01T17:51:30.0176534Z     
2024-11-01T17:51:30.0176796Z     3. To look up what optional arguments this module offers:
2024-11-01T17:51:30.0176891Z     
2024-11-01T17:51:30.0176992Z     ::
2024-11-01T17:51:30.0177099Z     
2024-11-01T17:51:30.0177345Z         python -m torch.distributed.launch --help
2024-11-01T17:51:30.0177456Z     
2024-11-01T17:51:30.0177553Z     
2024-11-01T17:51:30.0177676Z     **Important Notices:**
2024-11-01T17:51:30.0177786Z     
2024-11-01T17:51:30.0178128Z     1. This utility and multi-process distributed (single-node or
2024-11-01T17:51:30.0178571Z     multi-node) GPU training currently only achieves the best performance using
2024-11-01T17:51:30.0178929Z     the NCCL distributed backend. Thus NCCL backend is the recommended backend to
2024-11-01T17:51:30.0179067Z     use for GPU training.
2024-11-01T17:51:30.0179162Z     
2024-11-01T17:51:30.0179554Z     2. In your training program, you must parse the command-line argument:
2024-11-01T17:51:30.0179969Z     ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
2024-11-01T17:51:30.0180310Z     If your training program uses GPUs, you should ensure that your code only
2024-11-01T17:51:30.0180619Z     runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
2024-11-01T17:51:30.0180715Z     
2024-11-01T17:51:30.0180866Z     Parsing the local_rank argument
2024-11-01T17:51:30.0180960Z     
2024-11-01T17:51:30.0181057Z     ::
2024-11-01T17:51:30.0181166Z     
2024-11-01T17:51:30.0181289Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0181462Z         >>> import argparse
2024-11-01T17:51:30.0181643Z         >>> parser = argparse.ArgumentParser()
2024-11-01T17:51:30.0181990Z         >>> parser.add_argument("--local-rank", "--local_rank", type=int)
2024-11-01T17:51:30.0182147Z         >>> args = parser.parse_args()
2024-11-01T17:51:30.0182241Z     
2024-11-01T17:51:30.0182426Z     Set your device to local rank using either
2024-11-01T17:51:30.0182520Z     
2024-11-01T17:51:30.0182631Z     ::
2024-11-01T17:51:30.0182725Z     
2024-11-01T17:51:30.0183017Z         >>> torch.cuda.set_device(args.local_rank)  # before your code runs
2024-11-01T17:51:30.0183128Z     
2024-11-01T17:51:30.0183229Z     or
2024-11-01T17:51:30.0183337Z     
2024-11-01T17:51:30.0183435Z     ::
2024-11-01T17:51:30.0183584Z     
2024-11-01T17:51:30.0183783Z         >>> with torch.cuda.device(args.local_rank):
2024-11-01T17:51:30.0183937Z         >>>    # your code to run
2024-11-01T17:51:30.0184056Z         >>>    ...
2024-11-01T17:51:30.0184152Z     
2024-11-01T17:51:30.0184285Z     .. versionchanged:: 2.0.0
2024-11-01T17:51:30.0184391Z     
2024-11-01T17:51:30.0184830Z         The launcher will passes the ``--local-rank=<rank>`` argument to your script.
2024-11-01T17:51:30.0185282Z         From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
2024-11-01T17:51:30.0185536Z         previously used underscored ``--local_rank``.
2024-11-01T17:51:30.0185646Z     
2024-11-01T17:51:30.0185983Z         For backward compatibility, it may be necessary for users to handle both
2024-11-01T17:51:30.0186555Z         cases in their argument parsing code. This means including both ``"--local-rank"``
2024-11-01T17:51:30.0186971Z         and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
2024-11-01T17:51:30.0187330Z         provided, the launcher will trigger an error: "error: unrecognized arguments:
2024-11-01T17:51:30.0187756Z         --local-rank=<rank>". For training code that only supports PyTorch 2.0.0+,
2024-11-01T17:51:30.0188032Z         including ``"--local-rank"`` should be sufficient.
2024-11-01T17:51:30.0188144Z     
2024-11-01T17:51:30.0188489Z     3. In your training program, you are supposed to call the following function
2024-11-01T17:51:30.0188866Z     at the beginning to start the distributed backend. It is strongly recommended
2024-11-01T17:51:30.0189186Z     that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
2024-11-01T17:51:30.0189479Z     but ``env://`` is the one that is officially supported by this module.
2024-11-01T17:51:30.0189592Z     
2024-11-01T17:51:30.0189693Z     ::
2024-11-01T17:51:30.0189800Z     
2024-11-01T17:51:30.0190168Z         >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
2024-11-01T17:51:30.0190443Z         >>>                                      init_method='env://')
2024-11-01T17:51:30.0190553Z     
2024-11-01T17:51:30.0190904Z     4. In your training program, you can either use regular distributed functions
2024-11-01T17:51:30.0191274Z     or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
2024-11-01T17:51:30.0191569Z     training program uses GPUs for training and you would like to use
2024-11-01T17:51:30.0191855Z     :func:`torch.nn.parallel.DistributedDataParallel` module,
2024-11-01T17:51:30.0191990Z     here is how to configure it.
2024-11-01T17:51:30.0192084Z     
2024-11-01T17:51:30.0192196Z     ::
2024-11-01T17:51:30.0192290Z     
2024-11-01T17:51:30.0192586Z         >>> model = torch.nn.parallel.DistributedDataParallel(model,
2024-11-01T17:51:30.0195077Z         >>>                                                   device_ids=[args.local_rank],
2024-11-01T17:51:30.0195319Z         >>>                                                   output_device=args.local_rank)
2024-11-01T17:51:30.0195414Z     
2024-11-01T17:51:30.0195766Z     Please ensure that ``device_ids`` argument is set to be the only GPU device id
2024-11-01T17:51:30.0196132Z     that your code will be operating on. This is generally the local rank of the
2024-11-01T17:51:30.0196556Z     process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
2024-11-01T17:51:30.0196884Z     and ``output_device`` needs to be ``args.local_rank`` in order to use this
2024-11-01T17:51:30.0196985Z     utility
2024-11-01T17:51:30.0197092Z     
2024-11-01T17:51:30.0197459Z     5. Another way to pass ``local_rank`` to the subprocesses via environment variable
2024-11-01T17:51:30.0197790Z     ``LOCAL_RANK``. This behavior is enabled when you launch the script with
2024-11-01T17:51:30.0198220Z     ``--use-env=True``. You must adjust the subprocess example above to replace
2024-11-01T17:51:30.0198582Z     ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
2024-11-01T17:51:30.0198906Z     will not pass ``--local-rank`` when you specify this flag.
2024-11-01T17:51:30.0199000Z     
2024-11-01T17:51:30.0199130Z     .. warning::
2024-11-01T17:51:30.0199223Z     
2024-11-01T17:51:30.0199520Z         ``local_rank`` is NOT globally unique: it is only unique per process
2024-11-01T17:51:30.0199896Z         on a machine.  Thus, don't use it to decide if you should, e.g.,
2024-11-01T17:51:30.0200062Z         write to a networked filesystem.  See
2024-11-01T17:51:30.0200382Z         https://github.com/pytorch/pytorch/issues/12042 for an example of
2024-11-01T17:51:30.0200690Z         how things can go wrong if you don't do this correctly.
2024-11-01T17:51:30.0200796Z     
2024-11-01T17:51:30.0200890Z     
2024-11-01T17:51:30.0200983Z     
2024-11-01T17:51:30.0201129Z     
2024-11-01T17:51:30.0201561Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0201731Z     
2024-11-01T17:51:30.0201852Z       warnings.warn(msg)
2024-11-01T17:51:30.0201957Z     
2024-11-01T17:51:30.0202191Z --- Parse Warning: 36 / 103 ---
2024-11-01T17:51:30.0203878Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361.
2024-11-01T17:51:30.0204343Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0204437Z     
2024-11-01T17:51:30.0204797Z     Creates an :class:`ShardedTensor` from local shards and the global metadata.
2024-11-01T17:51:30.0205009Z     Needs to be called on all ranks in an SPMD fashion.
2024-11-01T17:51:30.0205118Z     
2024-11-01T17:51:30.0205218Z     Args:
2024-11-01T17:51:30.0205606Z         local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
2024-11-01T17:51:30.0205868Z             of shards that represent the local shards on this rank.
2024-11-01T17:51:30.0206202Z         global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
2024-11-01T17:51:30.0206381Z             shape of the overall sharded tensor.
2024-11-01T17:51:30.0206477Z     
2024-11-01T17:51:30.0206900Z     Keyword args:
2024-11-01T17:51:30.0207273Z         process_group (ProcessGroup, optional): The process group to work on. If None,
2024-11-01T17:51:30.0207449Z             the default process group will be used.
2024-11-01T17:51:30.0207705Z         init_rrefs (bool, optional): Whether or not to initialize
2024-11-01T17:51:30.0208000Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2024-11-01T17:51:30.0208300Z             Need to initialize the RPC Framework if specified as ``True``.
2024-11-01T17:51:30.0208422Z             Default: ``False``.
2024-11-01T17:51:30.0208533Z     
2024-11-01T17:51:30.0208636Z     Returns:
2024-11-01T17:51:30.0208856Z         A :class:`ShardedTensor` object handle on this rank
2024-11-01T17:51:30.0208964Z     
2024-11-01T17:51:30.0209060Z     
2024-11-01T17:51:30.0209179Z     Examples:
2024-11-01T17:51:30.0209549Z         Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
2024-11-01T17:51:30.0209826Z         each shard have a (5, 5) local tensor, we can do it like below:
2024-11-01T17:51:30.0210048Z     
2024-11-01T17:51:30.0210153Z         on rank 0:
2024-11-01T17:51:30.0210340Z         >>> # xdoctest: +SKIP("not distributed")
2024-11-01T17:51:30.0210513Z         >>> local_shard_metadata = ShardMetadata(
2024-11-01T17:51:30.0210661Z         >>>     shard_offsets=[0, 0],
2024-11-01T17:51:30.0210791Z         >>>     shard_lengths=[5, 5],
2024-11-01T17:51:30.0210931Z         >>>     placement="rank:0/cuda:0"
2024-11-01T17:51:30.0211045Z         >>> )
2024-11-01T17:51:30.0211328Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2024-11-01T17:51:30.0211619Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2024-11-01T17:51:30.0211714Z     
2024-11-01T17:51:30.0211833Z         on rank 1:
2024-11-01T17:51:30.0212004Z         >>> # xdoctest: +SKIP("not distributed")
2024-11-01T17:51:30.0212174Z         >>> local_shard_metadata = ShardMetadata(
2024-11-01T17:51:30.0212320Z         >>>     shard_offsets=[5, 0],
2024-11-01T17:51:30.0212450Z         >>>     shard_lengths=[5, 5],
2024-11-01T17:51:30.0212603Z         >>>     placement="rank:1/cuda:1"
2024-11-01T17:51:30.0212701Z         >>> )
2024-11-01T17:51:30.0212973Z         >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
2024-11-01T17:51:30.0213253Z         >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
2024-11-01T17:51:30.0213347Z     
2024-11-01T17:51:30.0213806Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0213947Z     
2024-11-01T17:51:30.0214078Z       warnings.warn(msg)
2024-11-01T17:51:30.0214171Z     
2024-11-01T17:51:30.0214496Z --- Parse Warning: 37 / 103 ---
2024-11-01T17:51:30.0216259Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=784.
2024-11-01T17:51:30.0216705Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0216816Z     
2024-11-01T17:51:30.0217174Z     Initialize a ShardedTensor given only one local tensor, global sharded tensor
2024-11-01T17:51:30.0217340Z     size and sharding spec on each rank.
2024-11-01T17:51:30.0217434Z     
2024-11-01T17:51:30.0217534Z     Args:
2024-11-01T17:51:30.0217871Z         local_tensor (Tensor): Single tensor of local shard stored in each rank.
2024-11-01T17:51:30.0218236Z         sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
2024-11-01T17:51:30.0218489Z             The specification describing how to shard the Tensor.
2024-11-01T17:51:30.0218727Z         global_size (Sequence[int]): Size of the sharded tensor.
2024-11-01T17:51:30.0219080Z         process_group (ProcessGroup, optional): The process group to aggregate on.
2024-11-01T17:51:30.0219196Z             Default: None
2024-11-01T17:51:30.0219449Z         init_rrefs (bool, optional): Whether or not to initialize
2024-11-01T17:51:30.0219746Z             :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
2024-11-01T17:51:30.0220030Z             Need to initialize the RPC Framework if specified as ``True``.
2024-11-01T17:51:30.0220163Z             Default: ``False``.
2024-11-01T17:51:30.0220255Z     
2024-11-01T17:51:30.0220373Z     Returns:
2024-11-01T17:51:30.0220726Z         A :class:`ShardedTensor` sharded based on the given sharding_spec with local
2024-11-01T17:51:30.0220894Z             tensor stored in the current rank.
2024-11-01T17:51:30.0221003Z     
2024-11-01T17:51:30.0221111Z     Examples:
2024-11-01T17:51:30.0221247Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0221449Z         >>> # All tensors below are of torch.int64 type.
2024-11-01T17:51:30.0221632Z         >>> # We have 2 process groups, 2 ranks.
2024-11-01T17:51:30.0221888Z         >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
2024-11-01T17:51:30.0222205Z         >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
2024-11-01T17:51:30.0222331Z         >>> local_tensor
2024-11-01T17:51:30.0222462Z         tensor([[1, 2, 3, 4]]) # Rank 0
2024-11-01T17:51:30.0222601Z         tensor([[3, 4, 5, 6]]) # Rank 1
2024-11-01T17:51:30.0222720Z         >>> sharding_dim = 0
2024-11-01T17:51:30.0222904Z         >>> sharding_spec = ChunkShardingSpec(
2024-11-01T17:51:30.0223029Z                 dim=sharding_dim,
2024-11-01T17:51:30.0223147Z                 placements=[
2024-11-01T17:51:30.0223286Z                     "rank:0/cuda:0",
2024-11-01T17:51:30.0223407Z                     "rank:1/cuda:1",
2024-11-01T17:51:30.0223525Z                 ],
2024-11-01T17:51:30.0223624Z             )
2024-11-01T17:51:30.0223984Z         >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4])
2024-11-01T17:51:30.0224097Z         >>> st
2024-11-01T17:51:30.0224213Z         ShardedTensor(
2024-11-01T17:51:30.0224361Z             ShardedTensorMetadata(
2024-11-01T17:51:30.0224486Z                 shards_metadata=[
2024-11-01T17:51:30.0224878Z                     ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
2024-11-01T17:51:30.0225241Z                     ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
2024-11-01T17:51:30.0225342Z                 ],
2024-11-01T17:51:30.0225489Z                 size=torch.Size([2, 4])
2024-11-01T17:51:30.0225588Z         )
2024-11-01T17:51:30.0225719Z         >>> st.local_tensor()
2024-11-01T17:51:30.0225882Z         tensor([1, 2, 3, 4]) # Rank 0
2024-11-01T17:51:30.0226074Z         tensor([3, 4, 5, 6]) # Rank 1
2024-11-01T17:51:30.0226169Z     
2024-11-01T17:51:30.0226562Z     Warning: This API is experimental and subject to change. It lacks of a fully across
2024-11-01T17:51:30.0226928Z              rank validations, and we only validate the local shard on the current rank.
2024-11-01T17:51:30.0227269Z              We fully rely on the user to ensure local tensor is sharded based on the
2024-11-01T17:51:30.0227403Z              sharding spec.
2024-11-01T17:51:30.0227497Z     
2024-11-01T17:51:30.0227942Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0228043Z     
2024-11-01T17:51:30.0228172Z       warnings.warn(msg)
2024-11-01T17:51:30.0228269Z     
2024-11-01T17:51:30.0228508Z --- Parse Warning: 38 / 103 ---
2024-11-01T17:51:30.0230177Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1023.
2024-11-01T17:51:30.0230636Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0230732Z     
2024-11-01T17:51:30.0231118Z     Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
2024-11-01T17:51:30.0231236Z     single local shard.
2024-11-01T17:51:30.0231333Z     
2024-11-01T17:51:30.0231744Z     If ``resharding_spec`` is same as the original one, this becomes a no-op.
2024-11-01T17:51:30.0232096Z     If only ``resharding_spec`` shares the same sharding dim with the original one,
2024-11-01T17:51:30.0232247Z     we swap local shards directly.
2024-11-01T17:51:30.0232627Z     For more generic cases, we merge different shards across different ranks and split
2024-11-01T17:51:30.0233009Z     the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
2024-11-01T17:51:30.0233106Z     
2024-11-01T17:51:30.0233206Z     Args:
2024-11-01T17:51:30.0233620Z         resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
2024-11-01T17:51:30.0234018Z             specification describing how the tensor is sharded.
2024-11-01T17:51:30.0234129Z     
2024-11-01T17:51:30.0234236Z     Returns:
2024-11-01T17:51:30.0234540Z         A :class:`ShardedTensor` object whose local shards are resharded.
2024-11-01T17:51:30.0234681Z     
2024-11-01T17:51:30.0234785Z     Examples:
2024-11-01T17:51:30.0234923Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0235093Z         >>> # We have 2 process groups, 2 ranks.
2024-11-01T17:51:30.0235365Z         >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
2024-11-01T17:51:30.0235535Z         >>> tensor = torch.stack([tensor, tensor])
2024-11-01T17:51:30.0235640Z         >>> tensor
2024-11-01T17:51:30.0235828Z         tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
2024-11-01T17:51:30.0235999Z         tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
2024-11-01T17:51:30.0236183Z         tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
2024-11-01T17:51:30.0236362Z         tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
2024-11-01T17:51:30.0236497Z         >>> sharding_dim = 0
2024-11-01T17:51:30.0236645Z         >>> spec = ChunkShardingSpec(
2024-11-01T17:51:30.0236770Z                 dim=sharding_dim,
2024-11-01T17:51:30.0236905Z                 placements=[
2024-11-01T17:51:30.0237029Z                     "rank:0/cuda:0",
2024-11-01T17:51:30.0237164Z                     "rank:1/cuda:1",
2024-11-01T17:51:30.0237281Z                     "rank:2/cuda:2",
2024-11-01T17:51:30.0237412Z                     "rank:3/cuda:3",
2024-11-01T17:51:30.0237514Z                 ],
2024-11-01T17:51:30.0237616Z             )
2024-11-01T17:51:30.0237765Z         >>> current_offsets = [0] * 2
2024-11-01T17:51:30.0237911Z         >>> current_offsets[0] = rank * 2
2024-11-01T17:51:30.0238118Z         >>> shard_metadata = ShardMetadata(
2024-11-01T17:51:30.0238386Z                 shard_offsets=copy.deepcopy(current_offsets),
2024-11-01T17:51:30.0238547Z                 shard_sizes=tensor.size(),
2024-11-01T17:51:30.0238719Z                 placement=spec.placements[rank],
2024-11-01T17:51:30.0238822Z             )
2024-11-01T17:51:30.0238956Z         >>> local_shards = [
2024-11-01T17:51:30.0239063Z                 Shard(
2024-11-01T17:51:30.0239200Z                     tensor=tensor,
2024-11-01T17:51:30.0239352Z                     metadata=shard_metadata,
2024-11-01T17:51:30.0239456Z                 )
2024-11-01T17:51:30.0239572Z             ]
2024-11-01T17:51:30.0239894Z         >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
2024-11-01T17:51:30.0240029Z         >>> sharding_dim = 1
2024-11-01T17:51:30.0240207Z         >>> resharding_spec = ChunkShardingSpec(
2024-11-01T17:51:30.0240344Z                 dim=sharding_dim,
2024-11-01T17:51:30.0240462Z                 placements=[
2024-11-01T17:51:30.0240588Z                     "rank:0/cuda:0",
2024-11-01T17:51:30.0240724Z                     "rank:1/cuda:1",
2024-11-01T17:51:30.0240841Z                     "rank:2/cuda:2",
2024-11-01T17:51:30.0240972Z                     "rank:3/cuda:3",
2024-11-01T17:51:30.0241077Z                 ],
2024-11-01T17:51:30.0241179Z             )
2024-11-01T17:51:30.0241335Z         >>> st.reshard(resharding_spec)
2024-11-01T17:51:30.0241500Z         >>> tensor = st.local_shards()[0].tensor
2024-11-01T17:51:30.0241617Z         >>> tensor
2024-11-01T17:51:30.0241825Z         tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
2024-11-01T17:51:30.0242041Z         tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
2024-11-01T17:51:30.0242241Z         tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
2024-11-01T17:51:30.0242452Z         tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
2024-11-01T17:51:30.0242558Z     
2024-11-01T17:51:30.0243011Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0243118Z     
2024-11-01T17:51:30.0243243Z       warnings.warn(msg)
2024-11-01T17:51:30.0243349Z     
2024-11-01T17:51:30.0243576Z --- Parse Warning: 39 / 103 ---
2024-11-01T17:51:30.0245163Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12.
2024-11-01T17:51:30.0245656Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0245752Z     
2024-11-01T17:51:30.0246066Z     Representation of a sharding plan, describes how to shard a module
2024-11-01T17:51:30.0246474Z     across hosts. `plan` is used to shard module parameters according to the spec provided,
2024-11-01T17:51:30.0246889Z     `output_plan` and `return_local_tensor` are optional, they are used to specify the output
2024-11-01T17:51:30.0247273Z     layout of a module with a spec, and when to convert back to data parallel fashion.
2024-11-01T17:51:30.0247372Z     
2024-11-01T17:51:30.0247485Z     Args:
2024-11-01T17:51:30.0247875Z         plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
2024-11-01T17:51:30.0248125Z               :class:`torch.distributed._shard.sharder.Sharder`]):
2024-11-01T17:51:30.0248637Z             a dict describes how to shard a module, there're currently two ways to shard a module:
2024-11-01T17:51:30.0249027Z                 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
2024-11-01T17:51:30.0249202Z                    a parameter to a `ShardingSpec`.
2024-11-01T17:51:30.0249603Z                 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
2024-11-01T17:51:30.0249741Z                    to a `Sharder` object.
2024-11-01T17:51:30.0250208Z         output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
2024-11-01T17:51:30.0250791Z             a dict specifies the layout of a module's output which produces a ShardedTensor,
2024-11-01T17:51:30.0251153Z             keyed by the name of module to ShardingSpec("" in key means the root module).
2024-11-01T17:51:30.0251282Z             Default: `None`
2024-11-01T17:51:30.0257765Z         return_local_tensor (List[str], optional): a list of string, each element enables
2024-11-01T17:51:30.0258376Z             a module's sharded output to be returned as a Tensor from its local shards to
2024-11-01T17:51:30.0258744Z             ensure further processing in a data parallel fashion. ("" in list means the
2024-11-01T17:51:30.0258863Z             root module).
2024-11-01T17:51:30.0258996Z             Default: None
2024-11-01T17:51:30.0259103Z     Example:
2024-11-01T17:51:30.0259558Z       Suppose we want to shard a module with two linear layers and then run it with DDP, we also
2024-11-01T17:51:30.0259995Z       want to convert the output of the second linear layer back to DDP, we can do it as follows:
2024-11-01T17:51:30.0260111Z     
2024-11-01T17:51:30.0260366Z         >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
2024-11-01T17:51:30.0260509Z         >>> class MyModule(nn.Module):
2024-11-01T17:51:30.0260731Z         >>>     def __init__(self) -> None:
2024-11-01T17:51:30.0260862Z         >>>        super().__init__()
2024-11-01T17:51:30.0261016Z         >>>        self.fc1 = nn.Linear()
2024-11-01T17:51:30.0261158Z         >>>        self.gelu = nn.GELU()
2024-11-01T17:51:30.0261310Z         >>>        self.fc2 = nn.Linear()
2024-11-01T17:51:30.0261451Z         >>>        self.relu = nn.Linear()
2024-11-01T17:51:30.0261551Z         >>>
2024-11-01T17:51:30.0261708Z         >>>     def forward(self, input):
2024-11-01T17:51:30.0261960Z         >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
2024-11-01T17:51:30.0262071Z     
2024-11-01T17:51:30.0262168Z     
2024-11-01T17:51:30.0262361Z         >>> # xdoctest: +SKIP("Undefined spec1, spec2)
2024-11-01T17:51:30.0262528Z         >>> sharding_plan = ShardingPlan(
2024-11-01T17:51:30.0262641Z         >>>    plan={
2024-11-01T17:51:30.0262790Z         >>>        "fc1.weight": spec1,
2024-11-01T17:51:30.0262921Z         >>>        "fc2.weight": spec2
2024-11-01T17:51:30.0263038Z         >>>    },
2024-11-01T17:51:30.0263160Z         >>>    output_plan={
2024-11-01T17:51:30.0263291Z         >>>        "fc2": output_spec
2024-11-01T17:51:30.0263525Z         >>>    },
2024-11-01T17:51:30.0263670Z         >>>    return_local_tensor=["fc2"]
2024-11-01T17:51:30.0263785Z         >>> )
2024-11-01T17:51:30.0263883Z     
2024-11-01T17:51:30.0264316Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0264426Z     
2024-11-01T17:51:30.0264549Z       warnings.warn(msg)
2024-11-01T17:51:30.0264661Z     
2024-11-01T17:51:30.0264893Z --- Parse Warning: 40 / 103 ---
2024-11-01T17:51:30.0266701Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72.
2024-11-01T17:51:30.0267152Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0267251Z     
2024-11-01T17:51:30.0267452Z     Run post-localSGD algorithm.
2024-11-01T17:51:30.0267552Z     
2024-11-01T17:51:30.0267983Z     This DDP communication hook is used for running post-localSGD algorithm,
2024-11-01T17:51:30.0268205Z     by combining with a model averaging component (e.g.,
2024-11-01T17:51:30.0268690Z     :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
2024-11-01T17:51:30.0268843Z     that runs after the optimizer step.
2024-11-01T17:51:30.0268940Z     
2024-11-01T17:51:30.0269056Z     Args:
2024-11-01T17:51:30.0269444Z         state (PostLocalSGDState): State information to run post-localSGD.
2024-11-01T17:51:30.0270013Z             Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
2024-11-01T17:51:30.0270748Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2024-11-01T17:51:30.0271141Z             Note that since DDP comm hook only supports single process single device mode,
2024-11-01T17:51:30.0271357Z             only exactly one tensor is stored in this bucket.
2024-11-01T17:51:30.0271470Z     
2024-11-01T17:51:30.0271576Z     Returns:
2024-11-01T17:51:30.0271930Z         Future handler of the communication, which updates the gradients in place.
2024-11-01T17:51:30.0272043Z     
2024-11-01T17:51:30.0272171Z     Example::
2024-11-01T17:51:30.0272309Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0272654Z         >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
2024-11-01T17:51:30.0272826Z                                   start_localSGD_iter=10)
2024-11-01T17:51:30.0273094Z         >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:30.0273591Z         >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
2024-11-01T17:51:30.0274292Z         >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
2024-11-01T17:51:30.0274390Z     
2024-11-01T17:51:30.0274841Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0274941Z     
2024-11-01T17:51:30.0275062Z       warnings.warn(msg)
2024-11-01T17:51:30.0275172Z     
2024-11-01T17:51:30.0275401Z --- Parse Warning: 41 / 103 ---
2024-11-01T17:51:30.0277136Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342.
2024-11-01T17:51:30.0277589Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0277699Z     
2024-11-01T17:51:30.0277843Z     Implement PowerSGD algorithm.
2024-11-01T17:51:30.0277953Z     
2024-11-01T17:51:30.0278271Z     This DDP communication hook implements PowerSGD gradient compression
2024-11-01T17:51:30.0278610Z     algorithm described in the `paper <https://arxiv.org/abs/1905.13727>`_.
2024-11-01T17:51:30.0279012Z     Once gradient tensors are aggregated across all workers, this hook applies
2024-11-01T17:51:30.0279142Z     compression as follows:
2024-11-01T17:51:30.0279252Z     
2024-11-01T17:51:30.0280024Z     1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
2024-11-01T17:51:30.0280136Z     
2024-11-01T17:51:30.0280753Z         1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
2024-11-01T17:51:30.0280851Z     
2024-11-01T17:51:30.0281479Z         1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
2024-11-01T17:51:30.0281575Z     
2024-11-01T17:51:30.0281733Z     2. Handles uncompressed tensors:
2024-11-01T17:51:30.0281829Z     
2024-11-01T17:51:30.0282564Z         2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
2024-11-01T17:51:30.0282664Z     
2024-11-01T17:51:30.0283154Z         2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
2024-11-01T17:51:30.0283266Z     
2024-11-01T17:51:30.0283605Z     3. Handles the tensors that should be compressed by PowerSGD compression:
2024-11-01T17:51:30.0283715Z     
2024-11-01T17:51:30.0284175Z         3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
2024-11-01T17:51:30.0284670Z         such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
2024-11-01T17:51:30.0284857Z     
2024-11-01T17:51:30.0285077Z         3.2. Computes each P in Ps, which is equal to MQ;
2024-11-01T17:51:30.0285189Z     
2024-11-01T17:51:30.0285331Z         3.3. Allreduces Ps as a batch;
2024-11-01T17:51:30.0285443Z     
2024-11-01T17:51:30.0285596Z         3.4. Orthogonalizes each P in Ps;
2024-11-01T17:51:30.0285691Z     
2024-11-01T17:51:30.0286001Z         3.5. Computes each Q in Qs, which is approximately equal to M^TP;
2024-11-01T17:51:30.0286100Z     
2024-11-01T17:51:30.0286253Z         3.6. Allreduces Qs as a batch;
2024-11-01T17:51:30.0286350Z     
2024-11-01T17:51:30.0286814Z         3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
2024-11-01T17:51:30.0286911Z     
2024-11-01T17:51:30.0287498Z     Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
2024-11-01T17:51:30.0287944Z     This not only gives the user more control over the tradeoff between speedup and accuracy,
2024-11-01T17:51:30.0288576Z     but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
2024-11-01T17:51:30.0288690Z     
2024-11-01T17:51:30.0288795Z     Args:
2024-11-01T17:51:30.0289412Z         state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
2024-11-01T17:51:30.0289936Z             To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
2024-11-01T17:51:30.0290086Z             and ``min_compression_rate``.
2024-11-01T17:51:30.0290824Z         bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
2024-11-01T17:51:30.0291199Z             Note that since DDP comm hook only supports single process single device mode,
2024-11-01T17:51:30.0291432Z             only exactly one tensor is stored in this bucket.
2024-11-01T17:51:30.0291530Z     
2024-11-01T17:51:30.0291657Z     Returns:
2024-11-01T17:51:30.0292012Z         Future handler of the communication, which updates the gradients in place.
2024-11-01T17:51:30.0292110Z     
2024-11-01T17:51:30.0292238Z     Example::
2024-11-01T17:51:30.0292363Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0292756Z         >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
2024-11-01T17:51:30.0293031Z                                   start_powerSGD_iter=10, min_compression_rate=0.5)
2024-11-01T17:51:30.0293272Z         >>> ddp_model.register_comm_hook(state, powerSGD_hook)
2024-11-01T17:51:30.0293370Z     
2024-11-01T17:51:30.0293805Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0293917Z     
2024-11-01T17:51:30.0294039Z       warnings.warn(msg)
2024-11-01T17:51:30.0294151Z     
2024-11-01T17:51:30.0294380Z --- Parse Warning: 42 / 103 ---
2024-11-01T17:51:30.0296182Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=36.
2024-11-01T17:51:30.0296626Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0296736Z     
2024-11-01T17:51:30.0297061Z     Averages parameters periodically after the warm-up stage.
2024-11-01T17:51:30.0297158Z     
2024-11-01T17:51:30.0297646Z     This can be used for running `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2024-11-01T17:51:30.0297922Z     by running :class:`~torch.nn.DistributedDataParallel` (DDP)
2024-11-01T17:51:30.0298273Z     using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
2024-11-01T17:51:30.0298372Z     
2024-11-01T17:51:30.0298477Z     Args:
2024-11-01T17:51:30.0298729Z         period (int): The number of steps per model averaging.
2024-11-01T17:51:30.0299224Z                       Usually the period should be greater than ``1`` to reduce the communication cost.
2024-11-01T17:51:30.0299431Z                       Otherwise, only DDP needs to be used.
2024-11-01T17:51:30.0299811Z         warmup_steps (int): The number of warm-up steps. During this stage,
2024-11-01T17:51:30.0300000Z                             model averaging is skipped.
2024-11-01T17:51:30.0300334Z         process_group: The process group to be used for all-reduce.
2024-11-01T17:51:30.0300565Z                        If ``None``, the default process group, which
2024-11-01T17:51:30.0300847Z                        is created by :func:`torch.distributed.init_process_group`,
2024-11-01T17:51:30.0301025Z                        will be used. (default: ``None``)
2024-11-01T17:51:30.0301137Z     
2024-11-01T17:51:30.0301248Z     Example::
2024-11-01T17:51:30.0301357Z     
2024-11-01T17:51:30.0301541Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0301661Z         >>> import torch
2024-11-01T17:51:30.0301845Z         >>> import torch.distributed as dist
2024-11-01T17:51:30.0302293Z         >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
2024-11-01T17:51:30.0302692Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2024-11-01T17:51:30.0302823Z         >>> import torch.nn as nn
2024-11-01T17:51:30.0302939Z         >>>
2024-11-01T17:51:30.0303192Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2024-11-01T17:51:30.0303335Z         >>> torch.cuda.set_device(rank)
2024-11-01T17:51:30.0303539Z         >>> module = nn.Linear(1, 1, bias=False).cuda()
2024-11-01T17:51:30.0303765Z         >>> model = nn.parallel.DistributedDataParallel(
2024-11-01T17:51:30.0303981Z         >>>    module, device_ids=[rank], output_device=rank
2024-11-01T17:51:30.0304086Z         >>> )
2024-11-01T17:51:30.0304377Z         >>> # Register a post-localSGD communication hook.
2024-11-01T17:51:30.0304793Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2024-11-01T17:51:30.0305021Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:30.0305138Z         >>>
2024-11-01T17:51:30.0305541Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2024-11-01T17:51:30.0305783Z         >>> # After 100 steps, run model averaging every 4 steps.
2024-11-01T17:51:30.0306302Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2024-11-01T17:51:30.0306936Z         >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2024-11-01T17:51:30.0307079Z         >>> for step in range(0, 200):
2024-11-01T17:51:30.0307229Z         >>>    optimizer.zero_grad()
2024-11-01T17:51:30.0307386Z         >>>    loss = loss_fn(output, labels)
2024-11-01T17:51:30.0307514Z         >>>    loss.backward()
2024-11-01T17:51:30.0307657Z         >>>    optimizer.step()
2024-11-01T17:51:30.0307941Z         >>>    # Will average model parameters globally every 4 steps. Thus,
2024-11-01T17:51:30.0308334Z         >>>    # inter-node communication only occurs every 4 iterations after
2024-11-01T17:51:30.0308511Z         >>>    # the initial ``warmup_steps`` period.
2024-11-01T17:51:30.0308736Z         >>>    averager.average_parameters(model.parameters())
2024-11-01T17:51:30.0308850Z     
2024-11-01T17:51:30.0309281Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0309392Z     
2024-11-01T17:51:30.0309514Z       warnings.warn(msg)
2024-11-01T17:51:30.0309624Z     
2024-11-01T17:51:30.0309855Z --- Parse Warning: 43 / 103 ---
2024-11-01T17:51:30.0311780Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=18.
2024-11-01T17:51:30.0312475Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0312574Z     
2024-11-01T17:51:30.0313051Z     Runs hierarchical model averaging (`hierarchical SGD <https://arxiv.org/pdf/2010.12998.pdf>`_).
2024-11-01T17:51:30.0313148Z     
2024-11-01T17:51:30.0313606Z     Process groups of different sizes are organized in a hierarchy, and they average parameters
2024-11-01T17:51:30.0314080Z     by using different periods concurrently after the warm-up stage.
2024-11-01T17:51:30.0314693Z     This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
2024-11-01T17:51:30.0315283Z     that supports `post-local SGD <https://arxiv.org/abs/1808.07217>`_, which essentially only supports
2024-11-01T17:51:30.0315826Z     a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
2024-11-01T17:51:30.0316355Z     level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
2024-11-01T17:51:30.0316870Z     Similarly, the process groups within this class do not have such an intra-machine process
2024-11-01T17:51:30.0317371Z     subgroup, which should be embedded by the post-local SGD communication hook instead.
2024-11-01T17:51:30.0317468Z     
2024-11-01T17:51:30.0317589Z     Args:
2024-11-01T17:51:30.0317964Z         period_group_size_dict: An ordered dict mapping keys of model averaging period to
2024-11-01T17:51:30.0318276Z                                 process group size, used for initializing process groups of
2024-11-01T17:51:30.0318610Z                                 different sizes in a hierarchy to average parameters concurrently.
2024-11-01T17:51:30.0318921Z                                 Particularly, at each iteration, there will be at most a single
2024-11-01T17:51:30.0319374Z                                 process group that runs averaging -- the period of such group should
2024-11-01T17:51:30.0319702Z                                 have the largest period which the current step can be divided by.
2024-11-01T17:51:30.0319970Z                                 For example, if the dict has three keys: 2, 4, and 8,
2024-11-01T17:51:30.0320282Z                                 then this means totally three process groups will be created to
2024-11-01T17:51:30.0320606Z                                 average parameters every 2, 4, and 8 iterations, respectively.
2024-11-01T17:51:30.0320959Z                                 At the 4th iteration, only the second process group will run
2024-11-01T17:51:30.0321238Z                                 averaging, because the first process group should be a
2024-11-01T17:51:30.0321576Z                                 subset of the second process group, and no need to execute the first
2024-11-01T17:51:30.0321758Z                                 process group redundantly.
2024-11-01T17:51:30.0322087Z                                 On the other hand, the third process group can only be triggered
2024-11-01T17:51:30.0322434Z                                 every 8 iterations, so it will not be triggered at the 4th iteration.
2024-11-01T17:51:30.0323003Z         warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
2024-11-01T17:51:30.0323615Z         process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
2024-11-01T17:51:30.0323909Z                                                 If ``None``, the default process group, which is created
2024-11-01T17:51:30.0324227Z                                                 by :func:`torch.distributed.init_process_group`, will be used.
2024-11-01T17:51:30.0324424Z                                                 (default: ``None``)
2024-11-01T17:51:30.0324521Z     
2024-11-01T17:51:30.0324636Z     Example::
2024-11-01T17:51:30.0324953Z         >>> # xdoctest: +SKIP('undefined rank')
2024-11-01T17:51:30.0325125Z         >>> from collections import OrderedDict
2024-11-01T17:51:30.0325312Z         >>> import torch
2024-11-01T17:51:30.0325478Z         >>> import torch.distributed as dist
2024-11-01T17:51:30.0325879Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2024-11-01T17:51:30.0326016Z         >>>     PostLocalSGDState,
2024-11-01T17:51:30.0326145Z         >>>     post_localSGD_hook,
2024-11-01T17:51:30.0326263Z         >>> )
2024-11-01T17:51:30.0326788Z         >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
2024-11-01T17:51:30.0326931Z         >>> import torch.nn as nn
2024-11-01T17:51:30.0327032Z         >>>
2024-11-01T17:51:30.0327300Z         >>> dist.init_process_group("nccl", rank=rank, world_size=16)
2024-11-01T17:51:30.0327441Z         >>> torch.cuda.set_device(rank)
2024-11-01T17:51:30.0327632Z         >>> module = nn.Linear(1, 1, bias=False).to(rank)
2024-11-01T17:51:30.0327873Z         >>> model = nn.parallel.DistributedDataParallel(
2024-11-01T17:51:30.0328079Z         >>>    module, device_ids=[rank], output_device=rank
2024-11-01T17:51:30.0328195Z         >>> )
2024-11-01T17:51:30.0328470Z         >>> # Register a post-localSGD communication hook.
2024-11-01T17:51:30.0329000Z         >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
2024-11-01T17:51:30.0329169Z         >>> subgroup, _ = dist.new_subgroups()
2024-11-01T17:51:30.0329608Z         >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
2024-11-01T17:51:30.0329851Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:30.0329957Z         >>>
2024-11-01T17:51:30.0330388Z         >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
2024-11-01T17:51:30.0330563Z         >>> # the 16 processes every 16 iterations.
2024-11-01T17:51:30.0330854Z         >>> averager = hierarchicalSGD.HierarchicalModelAverager(
2024-11-01T17:51:30.0331187Z         >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
2024-11-01T17:51:30.0331662Z         >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2024-11-01T17:51:30.0332077Z         >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
2024-11-01T17:51:30.0332302Z         >>> # After 100 steps, run model averaging at two levels.
2024-11-01T17:51:30.0332502Z         >>> for step in range(0, 200):
2024-11-01T17:51:30.0332637Z         >>>    optimizer.zero_grad()
2024-11-01T17:51:30.0332807Z         >>>    loss = loss_fn(output, labels)
2024-11-01T17:51:30.0332934Z         >>>    loss.backward()
2024-11-01T17:51:30.0333063Z         >>>    optimizer.step()
2024-11-01T17:51:30.0333296Z         >>>    # Average parameters after ``optimizer.step()``.
2024-11-01T17:51:30.0333807Z         >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
2024-11-01T17:51:30.0334052Z         >>>    averager.average_parameters(model.parameters())
2024-11-01T17:51:30.0334149Z     
2024-11-01T17:51:30.0334277Z     .. warning ::
2024-11-01T17:51:30.0334673Z         The last group size in the dict must be the size of the provided ``process_group``,
2024-11-01T17:51:30.0335004Z         which indicates model averaging at the highest level of the hierarchy.
2024-11-01T17:51:30.0335474Z         If ``process_group`` is not provided, then the last group size should be equal to the world size.
2024-11-01T17:51:30.0335572Z     
2024-11-01T17:51:30.0335703Z     .. warning ::
2024-11-01T17:51:30.0336019Z         `HierarchicalModelAverager` is experimental and subject to change.
2024-11-01T17:51:30.0336128Z     
2024-11-01T17:51:30.0336557Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0336655Z     
2024-11-01T17:51:30.0336823Z       warnings.warn(msg)
2024-11-01T17:51:30.0336919Z     
2024-11-01T17:51:30.0337214Z --- Parse Warning: 44 / 103 ---
2024-11-01T17:51:30.0338933Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=40.
2024-11-01T17:51:30.0339394Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0339495Z     
2024-11-01T17:51:30.0339922Z     StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
2024-11-01T17:51:30.0340305Z     on the coordinator rank, and then broadcast and shard each tensor to all ranks.
2024-11-01T17:51:30.0340403Z     
2024-11-01T17:51:30.0340652Z     . N.B. Intended to be used with DynamicMetaLoadPlanner
2024-11-01T17:51:30.0340751Z     
2024-11-01T17:51:30.0340877Z     .. warning::
2024-11-01T17:51:30.0341118Z         Current implementation only supports loading Tensors.
2024-11-01T17:51:30.0341215Z     
2024-11-01T17:51:30.0341389Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0341513Z     >>> sd = {"mode": model}
2024-11-01T17:51:30.0341636Z     >>> dcp.load(
2024-11-01T17:51:30.0341741Z     >>>    sd,
2024-11-01T17:51:30.0341973Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2024-11-01T17:51:30.0342145Z     >>>    planner=DynamicMetaLoadPlanner(),
2024-11-01T17:51:30.0342301Z     >>>    checkpoint_id="path_to_model.pt"
2024-11-01T17:51:30.0342417Z     >>> )
2024-11-01T17:51:30.0342513Z     
2024-11-01T17:51:30.0342957Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0343054Z     
2024-11-01T17:51:30.0343180Z       warnings.warn(msg)
2024-11-01T17:51:30.0343296Z     
2024-11-01T17:51:30.0343524Z --- Parse Warning: 45 / 103 ---
2024-11-01T17:51:30.0345208Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/format_utils.py line=151.
2024-11-01T17:51:30.0345657Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0345769Z     
2024-11-01T17:51:30.0346290Z     Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
2024-11-01T17:51:30.0346933Z     avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
2024-11-01T17:51:30.0347091Z     metadata file, like Torch Save files.
2024-11-01T17:51:30.0347189Z     
2024-11-01T17:51:30.0347471Z     . N.B. Intended to be used with BroadcastingTorchSaveReader
2024-11-01T17:51:30.0347568Z     
2024-11-01T17:51:30.0347694Z     .. warning::
2024-11-01T17:51:30.0347931Z         Current implementation only supports loading Tensors.
2024-11-01T17:51:30.0348033Z     
2024-11-01T17:51:30.0348202Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0348328Z     >>> sd = {"mode": model}
2024-11-01T17:51:30.0348454Z     >>> dcp.load(
2024-11-01T17:51:30.0348558Z     >>>    sd,
2024-11-01T17:51:30.0348789Z     >>>    storage_reader=BroadcastingTorchSaveReader(),
2024-11-01T17:51:30.0348964Z     >>>    planner=DynamicMetaLoadPlanner(),
2024-11-01T17:51:30.0349118Z     >>>    checkpoint_id="path_to_model.pt"
2024-11-01T17:51:30.0349235Z     >>> )
2024-11-01T17:51:30.0349335Z     
2024-11-01T17:51:30.0349781Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0349878Z     
2024-11-01T17:51:30.0350000Z       warnings.warn(msg)
2024-11-01T17:51:30.0350111Z     
2024-11-01T17:51:30.0350340Z --- Parse Warning: 46 / 103 ---
2024-11-01T17:51:30.0352054Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/optimizer.py line=220.
2024-11-01T17:51:30.0352583Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0352695Z     
2024-11-01T17:51:30.0353001Z     Load a state_dict in conjunction with FSDP sharded optimizer state.
2024-11-01T17:51:30.0353111Z     
2024-11-01T17:51:30.0353350Z     This is the current recommended way to checkpoint FSDP.
2024-11-01T17:51:30.0353477Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0353712Z     >>> import torch.distributed.checkpoint as dist_cp
2024-11-01T17:51:30.0353817Z     >>> # Save
2024-11-01T17:51:30.0354086Z     >>> model: torch.nn.Model
2024-11-01T17:51:30.0354245Z     >>> optim_params = model.parameters()
2024-11-01T17:51:30.0354445Z     >>> optim = torch.optim.SGD(optim_params, lr=0.01)
2024-11-01T17:51:30.0354563Z     >>> # Save
2024-11-01T17:51:30.0354878Z     >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
2024-11-01T17:51:30.0355013Z     >>>     state_dict = {
2024-11-01T17:51:30.0355241Z     >>>         "optimizer": FSDP.optim_state_dict(model, optim),
2024-11-01T17:51:30.0355402Z     >>>         "model": model.state_dict()
2024-11-01T17:51:30.0355506Z     >>>     }
2024-11-01T17:51:30.0355637Z     >>>     dist_cp.save_state_dict(
2024-11-01T17:51:30.0355788Z     >>>         state_dict=optim_state,
2024-11-01T17:51:30.0356036Z     >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
2024-11-01T17:51:30.0356244Z     >>>         planner=dist_cp.DefaultSavePlanner(),
2024-11-01T17:51:30.0356348Z     >>>     )
2024-11-01T17:51:30.0356462Z     >>>
2024-11-01T17:51:30.0356567Z     >>> # Load
2024-11-01T17:51:30.0356893Z     >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
2024-11-01T17:51:30.0357094Z     >>>     model_state_dict = model_tp.state_dict()
2024-11-01T17:51:30.0357214Z     >>>     checkpoint = {
2024-11-01T17:51:30.0357370Z     >>>         "model": model_state_dict
2024-11-01T17:51:30.0357476Z     >>>     }
2024-11-01T17:51:30.0357607Z     >>>     dist_cp.load_state_dict(
2024-11-01T17:51:30.0357762Z     >>>         state_dict=checkpoint,
2024-11-01T17:51:30.0358026Z     >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
2024-11-01T17:51:30.0358230Z     >>>         planner=dist_cp.DefaultLoadPlanner(),
2024-11-01T17:51:30.0358333Z     >>>     )
2024-11-01T17:51:30.0358566Z     >>>     model.load_state_dict(checkpoint["model_state"])
2024-11-01T17:51:30.0358707Z     >>>
2024-11-01T17:51:30.0358955Z     >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
2024-11-01T17:51:30.0359096Z     >>>         model_state_dict,
2024-11-01T17:51:30.0359244Z     >>>         optimizer_key="optimizer",
2024-11-01T17:51:30.0359508Z     >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
2024-11-01T17:51:30.0359657Z     >>>     )
2024-11-01T17:51:30.0359771Z     >>>
2024-11-01T17:51:30.0359976Z     >>>     flattened_osd = FSDP.optim_state_dict_to_load(
2024-11-01T17:51:30.0360160Z     >>>        model, optim, optim_state["optimizer"]
2024-11-01T17:51:30.0360282Z     >>>     )
2024-11-01T17:51:30.0360383Z     >>>
2024-11-01T17:51:30.0360565Z     >>>     optim.load_state_dict(flattened_osd)
2024-11-01T17:51:30.0360661Z     
2024-11-01T17:51:30.0361101Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0361212Z     
2024-11-01T17:51:30.0361334Z       warnings.warn(msg)
2024-11-01T17:51:30.0361451Z     
2024-11-01T17:51:30.0361682Z --- Parse Warning: 47 / 103 ---
2024-11-01T17:51:30.0363261Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=110.
2024-11-01T17:51:30.0363709Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0363806Z     
2024-11-01T17:51:30.0364262Z     Abstract class defining the protocol used by save_state_dict to plan the save process.
2024-11-01T17:51:30.0364407Z     
2024-11-01T17:51:30.0364844Z     SavePlanners are stateful objects that can be used to customize the whole save process.
2024-11-01T17:51:30.0364940Z     
2024-11-01T17:51:30.0365370Z     SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
2024-11-01T17:51:30.0365527Z     will be visible to the whole process.
2024-11-01T17:51:30.0365629Z     
2024-11-01T17:51:30.0366049Z     A planner subclass can expect the following sequence of calls during save_state_dict:
2024-11-01T17:51:30.0366144Z     
2024-11-01T17:51:30.0366379Z     1) set_up_planner - called on all ranks.
2024-11-01T17:51:30.0366556Z         Signals the start of a checkpoint save.
2024-11-01T17:51:30.0366665Z     
2024-11-01T17:51:30.0366894Z     2) create_local_plan - called on all ranks.
2024-11-01T17:51:30.0367313Z         Process the state_dict and produces a `SavePlan` that will be sent for global planning.
2024-11-01T17:51:30.0367467Z     
2024-11-01T17:51:30.0367794Z     3) create_global_plan - called on the coordinator rank only.
2024-11-01T17:51:30.0368095Z         Takes the SavePlan from all ranks and make any global decision.
2024-11-01T17:51:30.0368192Z     
2024-11-01T17:51:30.0368406Z     4) finish_plan - called on all ranks.
2024-11-01T17:51:30.0368730Z         This gives each rank a chance to adjust to global planning decisions.
2024-11-01T17:51:30.0368829Z     
2024-11-01T17:51:30.0369119Z     5) resolve_data - called multiple times on each rank
2024-11-01T17:51:30.0369425Z         Lookups a value on the `state_dict` for the storage layer to write.
2024-11-01T17:51:30.0369538Z     
2024-11-01T17:51:30.0369971Z     Users are recommended to extend DefaultSavePlanner instead of this interface directly as
2024-11-01T17:51:30.0370249Z     most changes can be expressed by changes in a single method.
2024-11-01T17:51:30.0370347Z     
2024-11-01T17:51:30.0370520Z     There are 3 usual patterns of extension:
2024-11-01T17:51:30.0370631Z     
2024-11-01T17:51:30.0371000Z     Rewriting state_dict. This is the simplest way to extend the save process as it
2024-11-01T17:51:30.0371408Z     doesn't requite understanding the intrincacies of how SavePlan works:
2024-11-01T17:51:30.0371505Z     
2024-11-01T17:51:30.0371661Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0371863Z     >>> class RenamePlanner(DefaultSavePlanner):
2024-11-01T17:51:30.0372040Z     >>>     def set_up_planner(
2024-11-01T17:51:30.0372163Z     >>>         self,
2024-11-01T17:51:30.0372317Z     >>>         state_dict: STATE_DICT_TYPE,
2024-11-01T17:51:30.0372511Z     >>>         storage_meta: Optional[StorageMeta],
2024-11-01T17:51:30.0372647Z     >>>         is_coordinator: bool,
2024-11-01T17:51:30.0372795Z     >>>     ) -> None:
2024-11-01T17:51:30.0372972Z     >>>         # prefix all keys with `foo_``
2024-11-01T17:51:30.0373422Z     >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
2024-11-01T17:51:30.0373539Z     
2024-11-01T17:51:30.0374034Z     Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
2024-11-01T17:51:30.0374148Z     
2024-11-01T17:51:30.0374302Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0374481Z     >>> class FP16Planner(DefaultSavePlanner):
2024-11-01T17:51:30.0374641Z     >>>     def create_local_plan(self):
2024-11-01T17:51:30.0374813Z     >>>         plan = super().create_local_plan()
2024-11-01T17:51:30.0374950Z     >>>         for p in plan:
2024-11-01T17:51:30.0375116Z     >>>             if p.tensor_data is not None:
2024-11-01T17:51:30.0375366Z     >>>                 p.tensor_data.properties.dtype = torch.float16
2024-11-01T17:51:30.0375488Z     >>>         return plan
2024-11-01T17:51:30.0375589Z     >>>
2024-11-01T17:51:30.0375769Z     >>>     def resolve_data(self, write_item):
2024-11-01T17:51:30.0375947Z     >>>         item = super().resolve_data(write_item)
2024-11-01T17:51:30.0376448Z     >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
2024-11-01T17:51:30.0376545Z     
2024-11-01T17:51:30.0377146Z     Using the global planning step to make central decisions that can't be made individually by each rank
2024-11-01T17:51:30.0377257Z     
2024-11-01T17:51:30.0377413Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0377579Z     >>> from itertools import zip_longest
2024-11-01T17:51:30.0377730Z     >>> from dataclasses import replace
2024-11-01T17:51:30.0377988Z     >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
2024-11-01T17:51:30.0378500Z     >>>     # This uses the default local plan behavior of having all non-sharded writes in rank 0
2024-11-01T17:51:30.0378755Z     >>>     # This sample doesn't handle ShardedTensors
2024-11-01T17:51:30.0378951Z     >>>     def create_global_plan(self, all_plans):
2024-11-01T17:51:30.0379169Z     >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
2024-11-01T17:51:30.0379318Z     >>>         items_per_rank = [
2024-11-01T17:51:30.0379526Z     >>>             [item for item in items if item is not None]
2024-11-01T17:51:30.0379773Z     >>>             for items in zip(*zip_longest(*iters), strict=True)
2024-11-01T17:51:30.0379881Z     >>>         ]
2024-11-01T17:51:30.0380000Z     >>>         all_plans = [
2024-11-01T17:51:30.0380172Z     >>>             replace(plan, items=items)
2024-11-01T17:51:30.0380461Z     >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
2024-11-01T17:51:30.0380585Z     >>>         ]
2024-11-01T17:51:30.0380787Z     >>>         return super().create_global_plan(all_plans)
2024-11-01T17:51:30.0380897Z     
2024-11-01T17:51:30.0381284Z     Finally, some planners need to save additional metadata in the checkpoint, this is
2024-11-01T17:51:30.0381675Z     accomplished by having each rank contribute their data items in the local plan and
2024-11-01T17:51:30.0381841Z     the global planner aggregate them:
2024-11-01T17:51:30.0381939Z     
2024-11-01T17:51:30.0382113Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0382340Z     >>> class SaveExtraDataPlanner(DefaultSavePlanner):
2024-11-01T17:51:30.0382599Z     >>>     def create_local_plan(self) -> SavePlan:
2024-11-01T17:51:30.0382768Z     >>>         plan = super().create_local_plan()
2024-11-01T17:51:30.0383070Z     >>>         return replace(plan, planner_data="per-rank-data")
2024-11-01T17:51:30.0383219Z     >>>
2024-11-01T17:51:30.0383750Z     >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
2024-11-01T17:51:30.0384043Z     >>>         global_plan, metadata = super().create_global_plan(all_plans)
2024-11-01T17:51:30.0384272Z     >>>         merged_data = [p.planner_data for p in global_plan]
2024-11-01T17:51:30.0384528Z     >>>         metadata = replace(metadata, planner_data=merged_data)
2024-11-01T17:51:30.0384686Z     >>>         return global_plan, metadata
2024-11-01T17:51:30.0384787Z     
2024-11-01T17:51:30.0385237Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0385335Z     
2024-11-01T17:51:30.0385471Z       warnings.warn(msg)
2024-11-01T17:51:30.0385567Z     
2024-11-01T17:51:30.0385812Z --- Parse Warning: 48 / 103 ---
2024-11-01T17:51:30.0387376Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/planner.py line=272.
2024-11-01T17:51:30.0387824Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0387935Z     
2024-11-01T17:51:30.0388342Z     Abstract class defining the protocol used by load_state_dict to plan the load process.
2024-11-01T17:51:30.0388453Z     
2024-11-01T17:51:30.0388864Z     LoadPlanner are stateful objects that can be used to customize the whole load process.
2024-11-01T17:51:30.0389019Z     
2024-11-01T17:51:30.0389487Z     LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
2024-11-01T17:51:30.0389645Z     will be visible to the whole process.
2024-11-01T17:51:30.0389757Z     
2024-11-01T17:51:30.0390162Z     A planner subclass can expect the following sequence of calls during load_state_dict:
2024-11-01T17:51:30.0390273Z     
2024-11-01T17:51:30.0390495Z     1) set_up_planner - called on all ranks.
2024-11-01T17:51:30.0390697Z         Signals the start of loading a checkpoint.
2024-11-01T17:51:30.0390794Z     
2024-11-01T17:51:30.0391020Z     2) create_local_plan - called on all ranks.
2024-11-01T17:51:30.0391460Z         Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
2024-11-01T17:51:30.0391558Z     
2024-11-01T17:51:30.0391896Z     3) create_global_plan - called on the coordinator rank only.
2024-11-01T17:51:30.0392185Z         Takes the LoadPlan from all ranks and make any global decision.
2024-11-01T17:51:30.0392300Z     
2024-11-01T17:51:30.0392569Z     4) load_bytes - called multiple times on each rank
2024-11-01T17:51:30.0392877Z         This is called once per non-tensor value in state_dict.
2024-11-01T17:51:30.0392991Z     
2024-11-01T17:51:30.0393391Z     5) resolve_tensor and commit_tensor - called multiple times on each rank
2024-11-01T17:51:30.0393678Z         They are called in pair for each Tensor value in state_dict.
2024-11-01T17:51:30.0393777Z     
2024-11-01T17:51:30.0394368Z     Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
2024-11-01T17:51:30.0394650Z     most changes can be expressed by changes in a single method.
2024-11-01T17:51:30.0394747Z     
2024-11-01T17:51:30.0394942Z     There are two usual patterns of extension:
2024-11-01T17:51:30.0395038Z     
2024-11-01T17:51:30.0395417Z     Rewriting state_dict. This is the simplest way to extend the load process as it
2024-11-01T17:51:30.0395862Z     doesn't requite understanding the intrincacies of how LoadPlan works. We need
2024-11-01T17:51:30.0396209Z     to keep a reference to the original state_dict as load happens in place so
2024-11-01T17:51:30.0396397Z     we need to be able to perform it in place
2024-11-01T17:51:30.0396493Z     
2024-11-01T17:51:30.0396664Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0396851Z     >>> class RenamePlanner(DefaultLoadPlanner):
2024-11-01T17:51:30.0396991Z     >>>     def set_up_planner(
2024-11-01T17:51:30.0397155Z     >>>         self,
2024-11-01T17:51:30.0397309Z     >>>         state_dict: STATE_DICT_TYPE,
2024-11-01T17:51:30.0397455Z     >>>         metadata: Metadata,
2024-11-01T17:51:30.0397591Z     >>>         is_coordinator: bool,
2024-11-01T17:51:30.0397753Z     >>>     ) -> None:
2024-11-01T17:51:30.0397933Z     >>>         self.original_state_dict = state_dict
2024-11-01T17:51:30.0398200Z     >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
2024-11-01T17:51:30.0398302Z     >>>
2024-11-01T17:51:30.0398465Z     >>>         if self.flatten_sharded_tensors:
2024-11-01T17:51:30.0398708Z     >>>             state_dict = _flatten_sharded_tensors(state_dict)
2024-11-01T17:51:30.0398810Z     >>>
2024-11-01T17:51:30.0398975Z     >>>         if self.flatten_state_dict:
2024-11-01T17:51:30.0399240Z     >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
2024-11-01T17:51:30.0399344Z     >>>
2024-11-01T17:51:30.0399512Z     >>>         self.state_dict = state_dict
2024-11-01T17:51:30.0399656Z     >>>         self.metadata = metadata
2024-11-01T17:51:30.0399850Z     >>>         self.is_coordinator = is_coordinator
2024-11-01T17:51:30.0399952Z     >>>
2024-11-01T17:51:30.0400141Z     >>>     def load_bytes(self, read_item, value):
2024-11-01T17:51:30.0400288Z     >>>         # Remove the "foo_" prefix
2024-11-01T17:51:30.0400741Z     >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
2024-11-01T17:51:30.0400895Z     
2024-11-01T17:51:30.0400991Z     
2024-11-01T17:51:30.0401421Z     Modifying resolve_tensor and commit_tensor to handle load time transformation.
2024-11-01T17:51:30.0401518Z     
2024-11-01T17:51:30.0401689Z     >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0401911Z     >>> class MetaModelMaterialize(DefaultSavePlanner):
2024-11-01T17:51:30.0402081Z     >>>     def resolve_tensor(self, read_item):
2024-11-01T17:51:30.0402290Z     >>>         tensor = super().resolve_tensor(read_item)
2024-11-01T17:51:30.0402502Z     >>>         return torch.empty_like(tensor, device="cpu")
2024-11-01T17:51:30.0402618Z     >>>
2024-11-01T17:51:30.0402807Z     >>>     def commit_tensor(self, read_item, tensor):
2024-11-01T17:51:30.0403049Z     >>>         self.state_dict[read_item.dest_index.fqn] = tensor
2024-11-01T17:51:30.0403147Z     
2024-11-01T17:51:30.0403579Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0403690Z     
2024-11-01T17:51:30.0403815Z       warnings.warn(msg)
2024-11-01T17:51:30.0403926Z     
2024-11-01T17:51:30.0404161Z --- Parse Warning: 49 / 103 ---
2024-11-01T17:51:30.0405740Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61.
2024-11-01T17:51:30.0406198Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0406298Z     
2024-11-01T17:51:30.0406516Z     Load a distributed ``state_dict`` in SPMD style.
2024-11-01T17:51:30.0406864Z     
2024-11-01T17:51:30.0407147Z     Each rank will try to read the least amount of data necessary
2024-11-01T17:51:30.0407484Z     to fullfill the requested `state_dict`. When loading :class:`ShardedTensor`
2024-11-01T17:51:30.0407855Z     or :class:`DTensor` instances, each rank only reads data for their local shards.
2024-11-01T17:51:30.0407953Z     
2024-11-01T17:51:30.0408336Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2024-11-01T17:51:30.0408730Z     load will first call ``state_dict`` before attempting deserialization, followed by
2024-11-01T17:51:30.0408969Z     ``load_state_dict`` once the deserialization is complete.
2024-11-01T17:51:30.0409457Z     For each non-``Stateful`` object, load will deserailize the object, and then replace
2024-11-01T17:51:30.0409680Z     it in the ``state_dict`` with the deserialized object.
2024-11-01T17:51:30.0409914Z     
2024-11-01T17:51:30.0410046Z     .. warning::
2024-11-01T17:51:30.0410289Z         All tensors in ``state_dict`` must be allocated on their
2024-11-01T17:51:30.0410535Z         destination device *prior to* calling this function.
2024-11-01T17:51:30.0410633Z     
2024-11-01T17:51:30.0411065Z         All non-tensor data is loaded using `torch.load()` and modified in place
2024-11-01T17:51:30.0411182Z         on state_dict.
2024-11-01T17:51:30.0411282Z     
2024-11-01T17:51:30.0411410Z     .. warning::
2024-11-01T17:51:30.0411718Z         Users must call `load_state_dict` on the root module to ensure load
2024-11-01T17:51:30.0412055Z         pos-processing and non-tensor data properly propagates.
2024-11-01T17:51:30.0412157Z     
2024-11-01T17:51:30.0412275Z     .. note:
2024-11-01T17:51:30.0412609Z         If no process group is initialized, this function will assume the intent
2024-11-01T17:51:30.0412948Z         is to load a checkpoint into the local process. This can be useful in the
2024-11-01T17:51:30.0413331Z         case of local inference, and when using regular Tensors (as opposed to DTensor
2024-11-01T17:51:30.0413455Z          or ShardedTensor)
2024-11-01T17:51:30.0413566Z     
2024-11-01T17:51:30.0413672Z     .. note:
2024-11-01T17:51:30.0413885Z         Rank 0 is assumed to be the coordinator rank.
2024-11-01T17:51:30.0413983Z     
2024-11-01T17:51:30.0414085Z     Args:
2024-11-01T17:51:30.0414317Z         state_dict (Dict[str, Any]): The state_dict to save.
2024-11-01T17:51:30.0414572Z         checkpoint_id (Union[str, os.PathLike, None]):
2024-11-01T17:51:30.0414994Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2024-11-01T17:51:30.0415303Z             depends on the storage. It can be a path to a folder or to a file.
2024-11-01T17:51:30.0415650Z             It can also be a key if the storage is a key-value store.
2024-11-01T17:51:30.0415777Z             (Default: ``None``)
2024-11-01T17:51:30.0415961Z         storage_reader (Optional[StorageReader]):
2024-11-01T17:51:30.0416271Z             Instance of StorageWriter used to perform reads. If this is not
2024-11-01T17:51:30.0416562Z             specified, DCP will automatically infer the reader based on the
2024-11-01T17:51:30.0416862Z             checkpoint_id. If checkpoint_id is also None, an exception will
2024-11-01T17:51:30.0417011Z             be raised. (Default: ``None``)
2024-11-01T17:51:30.0417178Z         planner (Optional[LoadPlanner]):
2024-11-01T17:51:30.0417472Z             Instance of LoadPlanner. If this is not specificed, the default
2024-11-01T17:51:30.0417659Z             planner will be used. (Default: ``None``)
2024-11-01T17:51:30.0417851Z         process_group (Optional[ProcessGroup]):
2024-11-01T17:51:30.0418178Z             ProcessGroup to be used for cross-rank synchronization.
2024-11-01T17:51:30.0418315Z             (Default: ``None``)
2024-11-01T17:51:30.0418411Z     
2024-11-01T17:51:30.0418530Z     Returns:
2024-11-01T17:51:30.0418635Z         None.
2024-11-01T17:51:30.0418730Z     
2024-11-01T17:51:30.0418851Z     Examples
2024-11-01T17:51:30.0418974Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0419117Z         >>> my_model = MyModule()
2024-11-01T17:51:30.0419313Z         >>> optimizer = Adagrad(my_model.parameters())
2024-11-01T17:51:30.0419492Z         >>> model_state_dict = my_model.state_dict()
2024-11-01T17:51:30.0419937Z         >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1")
2024-11-01T17:51:30.0420037Z     
2024-11-01T17:51:30.0420276Z         >>> torch.distributed.checkpoint.load_state_dict(
2024-11-01T17:51:30.0420432Z         >>>     state_dict=model_state_dict,
2024-11-01T17:51:30.0420611Z         >>>     storage_reader=fs_storage_reader,
2024-11-01T17:51:30.0420713Z         >>> )
2024-11-01T17:51:30.0420809Z     
2024-11-01T17:51:30.0421108Z         >>> # module.load_state_dict() function might have customized steps
2024-11-01T17:51:30.0421293Z         >>> # to flush the state_dict, must call it to
2024-11-01T17:51:30.0421487Z         >>> # ensure correct behavior.
2024-11-01T17:51:30.0421673Z         >>> my_model.load_state_dict(model_state_dict)
2024-11-01T17:51:30.0421786Z     
2024-11-01T17:51:30.0421895Z     .. note::
2024-11-01T17:51:30.0422193Z         load_state_dict uses collectives to coordinate reads across ranks.
2024-11-01T17:51:30.0422590Z         For NCCL-based process groups, internal tensor representations of
2024-11-01T17:51:30.0422936Z         objects must be moved to the GPU device before communication takes place.
2024-11-01T17:51:30.0423295Z         In this case, the device used is given by ``torch.cuda.current_device()``
2024-11-01T17:51:30.0423726Z         and it is the user's responsibility to ensure that this is set so that each
2024-11-01T17:51:30.0424012Z         rank has an individual GPU, via ``torch.cuda.set_device()``.
2024-11-01T17:51:30.0424109Z     
2024-11-01T17:51:30.0424542Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0424656Z     
2024-11-01T17:51:30.0424779Z       warnings.warn(msg)
2024-11-01T17:51:30.0424890Z     
2024-11-01T17:51:30.0425123Z --- Parse Warning: 50 / 103 ---
2024-11-01T17:51:30.0426712Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67.
2024-11-01T17:51:30.0427208Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0427306Z     
2024-11-01T17:51:30.0427541Z     Save a distributed model in SPMD style.
2024-11-01T17:51:30.0427640Z     
2024-11-01T17:51:30.0427927Z     This function is different from ``torch.save()`` as it handles
2024-11-01T17:51:30.0428299Z     ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
2024-11-01T17:51:30.0428409Z     
2024-11-01T17:51:30.0428790Z     For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
2024-11-01T17:51:30.0429001Z     save will call ``state_dict`` before serialization.
2024-11-01T17:51:30.0429114Z     
2024-11-01T17:51:30.0429226Z     .. warning::
2024-11-01T17:51:30.0429585Z         There is no guarantees of Backwards Compatibility across PyTorch versions
2024-11-01T17:51:30.0429713Z         for saved state_dicts.
2024-11-01T17:51:30.0429808Z     
2024-11-01T17:51:30.0429934Z     .. warning::
2024-11-01T17:51:30.0430248Z         If using the `process_group` argument, make sure that only its ranks
2024-11-01T17:51:30.0430574Z         call `save_state_dict` and that all data in state_dict belong to it.
2024-11-01T17:51:30.0430674Z     
2024-11-01T17:51:30.0430794Z     .. note::
2024-11-01T17:51:30.0431265Z         When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
2024-11-01T17:51:30.0431645Z         the shard_group should be calling `save_state_dict` and the corresponding process
2024-11-01T17:51:30.0431801Z         group needs to be passed in.
2024-11-01T17:51:30.0431897Z     
2024-11-01T17:51:30.0432018Z     .. note::
2024-11-01T17:51:30.0432425Z         If no process group is available, this function assumes the intention is to save the
2024-11-01T17:51:30.0432590Z          state_dict in the local process.
2024-11-01T17:51:30.0432689Z     
2024-11-01T17:51:30.0432791Z     .. note:
2024-11-01T17:51:30.0433001Z         Rank 0 is assumed to be the coordinator rank.
2024-11-01T17:51:30.0433101Z     
2024-11-01T17:51:30.0433211Z     
2024-11-01T17:51:30.0433312Z     Args:
2024-11-01T17:51:30.0433534Z         state_dict (Dict[str, Any]): The state_dict to save.
2024-11-01T17:51:30.0433749Z         checkpoint_id (Union[str, os.PathLike, None]):
2024-11-01T17:51:30.0434210Z             The ID of this checkpoint instance. The meaning of the checkpoint_id
2024-11-01T17:51:30.0434538Z             depends on the storage. It can be a path to a folder or to a file.
2024-11-01T17:51:30.0434927Z             It can also be a key if the storage is a key-value store.
2024-11-01T17:51:30.0435064Z             (Default: ``None``)
2024-11-01T17:51:30.0435245Z         storage_writer (Optional[StorageWriter]):
2024-11-01T17:51:30.0435545Z             Instance of StorageWriter used to perform writes. If this is not
2024-11-01T17:51:30.0435852Z             specified, DCP will automatically infer the writer based on the
2024-11-01T17:51:30.0436138Z             checkpoint_id. If checkpoint_id is also None, an exception will
2024-11-01T17:51:30.0436301Z             be raised. (Default: ``None``)
2024-11-01T17:51:30.0436458Z         planner (Optional[SavePlanner]):
2024-11-01T17:51:30.0436765Z             Instance of SavePlanner. If this is not specificed, the default
2024-11-01T17:51:30.0436949Z             planner will be used. (Default: ``None``)
2024-11-01T17:51:30.0437141Z         process_group (Optional[ProcessGroup]):
2024-11-01T17:51:30.0437468Z             ProcessGroup to be used for cross-rank synchronization.
2024-11-01T17:51:30.0437593Z             (Default: ``None``)
2024-11-01T17:51:30.0437708Z     
2024-11-01T17:51:30.0437814Z     Returns:
2024-11-01T17:51:30.0438051Z         Metadata: Metadata object for the saved checkpoint.
2024-11-01T17:51:30.0438149Z     
2024-11-01T17:51:30.0438256Z     Example:
2024-11-01T17:51:30.0438396Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0438526Z         >>> my_model = MyModule()
2024-11-01T17:51:30.0438637Z     
2024-11-01T17:51:30.0438793Z         >>> state_dict = {"model": my_model}
2024-11-01T17:51:30.0438923Z     
2024-11-01T17:51:30.0439419Z         >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1")
2024-11-01T17:51:30.0439609Z         >>> torch.distributed.checkpoint.save(
2024-11-01T17:51:30.0439765Z         >>>     state_dict=state_dict,
2024-11-01T17:51:30.0439930Z         >>>     storage_writer=fs_storage_writer,
2024-11-01T17:51:30.0440047Z         >>> )
2024-11-01T17:51:30.0440143Z     
2024-11-01T17:51:30.0440255Z     .. note::
2024-11-01T17:51:30.0440574Z         save_state_dict uses collectives to coordinate writes across ranks.
2024-11-01T17:51:30.0440960Z         For NCCL-based process groups, internal tensor representations of
2024-11-01T17:51:30.0441317Z         objects must be moved to the GPU device before communication takes place.
2024-11-01T17:51:30.0441655Z         In this case, the device used is given by ``torch.cuda.current_device()``
2024-11-01T17:51:30.0442073Z         and it is the user's responsibility to ensure that this is set so that
2024-11-01T17:51:30.0442375Z         each rank has an individual GPU, via ``torch.cuda.set_device()``.
2024-11-01T17:51:30.0442475Z     
2024-11-01T17:51:30.0442928Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0443024Z     
2024-11-01T17:51:30.0443160Z       warnings.warn(msg)
2024-11-01T17:51:30.0443257Z     
2024-11-01T17:51:30.0443502Z --- Parse Warning: 51 / 103 ---
2024-11-01T17:51:30.0445122Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170.
2024-11-01T17:51:30.0445584Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0446052Z     Asynchronous version of ``save``. This code first de-stages the state_dict on to the
2024-11-01T17:51:30.0446475Z         staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
2024-11-01T17:51:30.0446591Z     
2024-11-01T17:51:30.0446708Z         .. warning::
2024-11-01T17:51:30.0446952Z             This feature is experimental and subject to change.
2024-11-01T17:51:30.0447050Z     
2024-11-01T17:51:30.0447168Z         Args:
2024-11-01T17:51:30.0447389Z             state_dict (Dict[str, Any]): The state_dict to save.
2024-11-01T17:51:30.0447597Z             checkpoint_id (Union[str, os.PathLike, None]):
2024-11-01T17:51:30.0447971Z                 The ID of this checkpoint instance. The meaning of the checkpoint_id
2024-11-01T17:51:30.0448284Z                 depends on the storage. It can be a path to a folder or to a file.
2024-11-01T17:51:30.0448636Z                 It can also be a key if the storage is a key-value store.
2024-11-01T17:51:30.0448765Z                 (Default: ``None``)
2024-11-01T17:51:30.0448968Z             storage_writer (Optional[StorageWriter]):
2024-11-01T17:51:30.0449358Z                 Instance of StorageWriter used to perform 'stage' and  'save'. If
2024-11-01T17:51:30.0449724Z                 this is not specified, DCP will automatically infer the writer based on the
2024-11-01T17:51:30.0450031Z                 checkpoint_id. If checkpoint_id is also None, an exception will
2024-11-01T17:51:30.0450188Z                 be raised. (Default: ``None``)
2024-11-01T17:51:30.0450365Z             planner (Optional[SavePlanner]):
2024-11-01T17:51:30.0450662Z                 Instance of SavePlanner. If this is not specificed, the default
2024-11-01T17:51:30.0450867Z                 planner will be used. (Default: ``None``)
2024-11-01T17:51:30.0451049Z             process_group (Optional[ProcessGroup]):
2024-11-01T17:51:30.0451386Z                 ProcessGroup to be used for cross-rank synchronization.
2024-11-01T17:51:30.0451526Z                 (Default: ``None``)
2024-11-01T17:51:30.0451621Z     
2024-11-01T17:51:30.0451744Z         Returns:
2024-11-01T17:51:30.0452082Z             Future: A future holding the resultant Metadata object from `save`.
2024-11-01T17:51:30.0452192Z     
2024-11-01T17:51:30.0452349Z         Example:
2024-11-01T17:51:30.0452480Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0452629Z             >>> my_model = MyModule()
2024-11-01T17:51:30.0452725Z     
2024-11-01T17:51:30.0452899Z             >>> state_dict = {"model": my_model}
2024-11-01T17:51:30.0452997Z     
2024-11-01T17:51:30.0453437Z             >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1")
2024-11-01T17:51:30.0453755Z             >>> checkpoint_future = torch.distributed.checkpoint.async_save(
2024-11-01T17:51:30.0453897Z             >>>     state_dict=state_dict,
2024-11-01T17:51:30.0454090Z             >>>     storage_writer=fs_storage_writer,
2024-11-01T17:51:30.0454195Z             >>> )
2024-11-01T17:51:30.0454314Z             >>>
2024-11-01T17:51:30.0454449Z             >>> # ... do some work ...
2024-11-01T17:51:30.0454550Z             >>>
2024-11-01T17:51:30.0454717Z             >>> checkpoint_future.result()
2024-11-01T17:51:30.0454813Z     
2024-11-01T17:51:30.0454932Z         
2024-11-01T17:51:30.0455367Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0455463Z     
2024-11-01T17:51:30.0455598Z       warnings.warn(msg)
2024-11-01T17:51:30.0455693Z     
2024-11-01T17:51:30.0455936Z --- Parse Warning: 52 / 103 ---
2024-11-01T17:51:30.0457636Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/elastic/events/__init__.py line=91.
2024-11-01T17:51:30.0458100Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0458197Z     
2024-11-01T17:51:30.0458484Z     Initialize rendezvous event object and record its operations.
2024-11-01T17:51:30.0458584Z     
2024-11-01T17:51:30.0458685Z     Args:
2024-11-01T17:51:30.0458882Z         run_id (str): The run id of the rendezvous.
2024-11-01T17:51:30.0459094Z         message (str): The message describing the event.
2024-11-01T17:51:30.0459480Z         node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
2024-11-01T17:51:30.0459755Z         name (str): Event name. (E.g. Current action being performed).
2024-11-01T17:51:30.0459933Z         hostname (str): Hostname of the node.
2024-11-01T17:51:30.0460179Z         pid (Optional[int]): The process id of the node.
2024-11-01T17:51:30.0460529Z         master_endpoint (str): The master endpoint for the rendezvous store, if known.
2024-11-01T17:51:30.0460953Z         local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
2024-11-01T17:51:30.0461178Z         rank (Optional[int]): The rank of the node, if known.
2024-11-01T17:51:30.0461302Z     Returns:
2024-11-01T17:51:30.0461406Z         None
2024-11-01T17:51:30.0461515Z     Example:
2024-11-01T17:51:30.0461714Z         >>> # See DynamicRendezvousHandler class
2024-11-01T17:51:30.0461831Z         >>> def _record(
2024-11-01T17:51:30.0461958Z         ...     self,
2024-11-01T17:51:30.0462079Z         ...     message: str,
2024-11-01T17:51:30.0462299Z         ...     node_state: NodeState = NodeState.RUNNING,
2024-11-01T17:51:30.0462447Z         ...     rank: Optional[int] = None,
2024-11-01T17:51:30.0462599Z         ... ) -> None:
2024-11-01T17:51:30.0462780Z         ...     construct_and_record_rdzv_event(
2024-11-01T17:51:30.0463022Z         ...         name=f"{self.__class__.__name__}.{get_method_name()}",
2024-11-01T17:51:30.0463200Z         ...         run_id=self._settings.run_id,
2024-11-01T17:51:30.0463328Z         ...         message=message,
2024-11-01T17:51:30.0463482Z         ...         node_state=node_state,
2024-11-01T17:51:30.0463645Z         ...         hostname=self._this_node.addr,
2024-11-01T17:51:30.0463812Z         ...         pid=self._this_node.pid,
2024-11-01T17:51:30.0464066Z         ...         local_id=self._this_node.local_id,
2024-11-01T17:51:30.0464264Z         ...         rank=rank,
2024-11-01T17:51:30.0464380Z         ...     )
2024-11-01T17:51:30.0464520Z     
2024-11-01T17:51:30.0464952Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0465061Z     
2024-11-01T17:51:30.0465181Z       warnings.warn(msg)
2024-11-01T17:51:30.0465292Z     
2024-11-01T17:51:30.0465524Z --- Parse Warning: 53 / 103 ---
2024-11-01T17:51:30.0467030Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/api.py line=113.
2024-11-01T17:51:30.0467477Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0467592Z     
2024-11-01T17:51:30.0467887Z     This configures FSDP-native mixed precision training.
2024-11-01T17:51:30.0467986Z     
2024-11-01T17:51:30.0468110Z     Attributes:
2024-11-01T17:51:30.0468450Z         param_dtype (Optional[torch.dtype]): This specifies the dtype for model
2024-11-01T17:51:30.0468752Z             parameters during forward and backward and thus the dtype for
2024-11-01T17:51:30.0469068Z             forward and backward computation. Outside forward and backward, the
2024-11-01T17:51:30.0469355Z             *sharded* parameters are kept in full precision (e.g. for the
2024-11-01T17:51:30.0469701Z             optimizer step), and for model checkpointing, the parameters are
2024-11-01T17:51:30.0469925Z             always saved in full precision. (Default: ``None``)
2024-11-01T17:51:30.0470267Z         reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
2024-11-01T17:51:30.0470657Z             gradient reduction (i.e. reduce-scatter or all-reduce). If this is
2024-11-01T17:51:30.0470942Z             ``None`` but ``param_dtype`` is not ``None``, then this takes on
2024-11-01T17:51:30.0471232Z             the ``param_dtype`` value, still running gradient reduction in low
2024-11-01T17:51:30.0471555Z             precision. This is permitted to differ from ``param_dtype``, e.g.
2024-11-01T17:51:30.0471844Z             to force gradient reduction to run in full precision. (Default:
2024-11-01T17:51:30.0471953Z             ``None``)
2024-11-01T17:51:30.0472269Z         buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
2024-11-01T17:51:30.0472605Z             buffers. FSDP does not shard buffers. Rather, FSDP casts them to
2024-11-01T17:51:30.0472900Z             ``buffer_dtype`` in the first forward pass and keeps them in that
2024-11-01T17:51:30.0473201Z             dtype thereafter. For model checkpointing, the buffers are saved
2024-11-01T17:51:30.0473490Z             in full precision except for ``LOCAL_STATE_DICT``. (Default:
2024-11-01T17:51:30.0473597Z             ``None``)
2024-11-01T17:51:30.0474030Z         keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
2024-11-01T17:51:30.0474365Z             gradients to full precision after the backward pass in preparation
2024-11-01T17:51:30.0474667Z             for the optimizer step. If ``True``, then FSDP keeps the gradients
2024-11-01T17:51:30.0474986Z             in the dtype used for gradient reduction, which can save memory if
2024-11-01T17:51:30.0475285Z             using a custom optimizer that supports running in low precision.
2024-11-01T17:51:30.0475424Z             (Default: ``False``)
2024-11-01T17:51:30.0475724Z         cast_forward_inputs (bool): If ``True``, then this FSDP module casts
2024-11-01T17:51:30.0476034Z             its forward args and kwargs to ``param_dtype``. This is to ensure
2024-11-01T17:51:30.0476338Z             that parameter and input dtypes match for forward computation, as
2024-11-01T17:51:30.0476649Z             required by many ops. This may need to be set to ``True`` when only
2024-11-01T17:51:30.0476977Z             applying mixed precision to some but not all FSDP modules, in which
2024-11-01T17:51:30.0477473Z             case a mixed-precision FSDP submodule needs to recast its inputs.
2024-11-01T17:51:30.0477616Z             (Default: ``False``)
2024-11-01T17:51:30.0477928Z         cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
2024-11-01T17:51:30.0478235Z             casts its forward args and kwargs to ``param_dtype``, overriding
2024-11-01T17:51:30.0478597Z             the value of ``cast_forward_inputs``. For non-root FSDP modules,
2024-11-01T17:51:30.0478803Z             this does not do anything. (Default: ``True``)
2024-11-01T17:51:30.0479122Z         _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
2024-11-01T17:51:30.0479388Z             module classes to ignore for mixed precision when using an
2024-11-01T17:51:30.0479666Z             ``auto_wrap_policy``: Modules of these classes will have FSDP
2024-11-01T17:51:30.0479968Z             applied to them separately with mixed precision disabled (meaning
2024-11-01T17:51:30.0480283Z             that the final FSDP construction would deviate from the specified
2024-11-01T17:51:30.0480564Z             policy). If ``auto_wrap_policy`` is not specified, then this does
2024-11-01T17:51:30.0480877Z             not do anything. This API is experimental and subject to change.
2024-11-01T17:51:30.0481024Z             (Default: ``(_BatchNorm,)``)
2024-11-01T17:51:30.0481122Z     
2024-11-01T17:51:30.0481387Z     .. note:: This API is experimental and subject to change.
2024-11-01T17:51:30.0481487Z     
2024-11-01T17:51:30.0481823Z     .. note:: Only floating point tensors are cast to their specified dtypes.
2024-11-01T17:51:30.0481923Z     
2024-11-01T17:51:30.0482204Z     .. note:: In ``summon_full_params``, parameters are forced to full
2024-11-01T17:51:30.0482353Z         precision, but buffers are not.
2024-11-01T17:51:30.0482450Z     
2024-11-01T17:51:30.0482768Z     .. note:: Layer norm and batch norm accumulate in ``float32`` even when
2024-11-01T17:51:30.0483082Z         their inputs are in a low precision like ``float16`` or ``bfloat16``.
2024-11-01T17:51:30.0483511Z         Disabling FSDP's mixed precision for those norm modules only means that
2024-11-01T17:51:30.0483817Z         the affine parameters are kept in ``float32``. However, this incurs
2024-11-01T17:51:30.0484246Z         separate all-gathers and reduce-scatters for those norm modules, which
2024-11-01T17:51:30.0484571Z         may be inefficient, so if the workload permits, the user should prefer
2024-11-01T17:51:30.0484824Z         to still apply mixed precision to those modules.
2024-11-01T17:51:30.0484935Z     
2024-11-01T17:51:30.0485249Z     .. note:: By default, if the user passes a model with any ``_BatchNorm``
2024-11-01T17:51:30.0485559Z         modules and specifies an ``auto_wrap_policy``, then the batch norm
2024-11-01T17:51:30.0485882Z         modules will have FSDP applied to them separately with mixed precision
2024-11-01T17:51:30.0486144Z         disabled. See the ``_module_classes_to_ignore`` argument.
2024-11-01T17:51:30.0486244Z     
2024-11-01T17:51:30.0486540Z     .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
2024-11-01T17:51:30.0486855Z         ``cast_forward_inputs=False`` by default. For the root FSDP instance,
2024-11-01T17:51:30.0487098Z         its ``cast_root_forward_inputs`` takes precedence over its
2024-11-01T17:51:30.0487438Z         ``cast_forward_inputs``. For non-root FSDP instances, their
2024-11-01T17:51:30.0487746Z         ``cast_root_forward_inputs`` values are ignored. The default setting is
2024-11-01T17:51:30.0488076Z         sufficient for the typical case where each FSDP instance has the same
2024-11-01T17:51:30.0488389Z         ``MixedPrecision`` configuration and only needs to cast inputs to the
2024-11-01T17:51:30.0488736Z         ``param_dtype`` at the beginning of the model's forward pass.
2024-11-01T17:51:30.0488833Z     
2024-11-01T17:51:30.0489135Z     .. note:: For nested FSDP instances with different ``MixedPrecision``
2024-11-01T17:51:30.0489515Z         configurations, we recommend setting individual ``cast_forward_inputs``
2024-11-01T17:51:30.0489899Z         values to configure casting inputs or not before each instance's
2024-11-01T17:51:30.0490206Z         forward. In such a case, since the casts happen before each FSDP
2024-11-01T17:51:30.0490592Z         instance's forward, a parent FSDP instance should have its non-FSDP
2024-11-01T17:51:30.0490941Z         submodules run before its FSDP submodules to avoid the activation dtype
2024-11-01T17:51:30.0491253Z         being changed due to a different ``MixedPrecision`` configuration.
2024-11-01T17:51:30.0491350Z     
2024-11-01T17:51:30.0491476Z         Example::
2024-11-01T17:51:30.0491572Z     
2024-11-01T17:51:30.0491775Z             >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0492019Z             >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
2024-11-01T17:51:30.0492158Z             >>> model[1] = FSDP(
2024-11-01T17:51:30.0492279Z             >>>     model[1],
2024-11-01T17:51:30.0492699Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
2024-11-01T17:51:30.0492819Z             >>> )
2024-11-01T17:51:30.0492937Z             >>> model = FSDP(
2024-11-01T17:51:30.0493062Z             >>>     model,
2024-11-01T17:51:30.0493479Z             >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
2024-11-01T17:51:30.0493596Z             >>> )
2024-11-01T17:51:30.0493694Z     
2024-11-01T17:51:30.0494009Z         The above shows a working example. On the other hand, if ``model[1]``
2024-11-01T17:51:30.0494315Z         were replaced with ``model[0]``, meaning that the submodule using
2024-11-01T17:51:30.0494626Z         different ``MixedPrecision`` ran its forward first, then ``model[1]``
2024-11-01T17:51:30.0494949Z         would incorrectly see ``float16`` activations instead of ``bfloat16``
2024-11-01T17:51:30.0495055Z         ones.
2024-11-01T17:51:30.0495168Z     
2024-11-01T17:51:30.0495265Z     
2024-11-01T17:51:30.0495702Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0495813Z     
2024-11-01T17:51:30.0495936Z       warnings.warn(msg)
2024-11-01T17:51:30.0496045Z     
2024-11-01T17:51:30.0496279Z --- Parse Warning: 54 / 103 ---
2024-11-01T17:51:30.0498159Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=649.
2024-11-01T17:51:30.0498660Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0499038Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2024-11-01T17:51:30.0499149Z     
2024-11-01T17:51:30.0499617Z             Also takes (optional) configuration for the model's and optimizer's state dict.
2024-11-01T17:51:30.0499946Z             The target module does not have to be a FSDP module. If the target
2024-11-01T17:51:30.0500263Z             module is a FSDP module, its ``state_dict_type`` will also be changed.
2024-11-01T17:51:30.0500375Z     
2024-11-01T17:51:30.0500743Z             .. note:: This API should be called for only the top-level (root)
2024-11-01T17:51:30.0500852Z                 module.
2024-11-01T17:51:30.0500961Z     
2024-11-01T17:51:30.0501284Z             .. note:: This API enables users to transparently use the conventional
2024-11-01T17:51:30.0501582Z                 ``state_dict`` API to take model checkpoints in cases where the
2024-11-01T17:51:30.0501890Z                 root FSDP module is wrapped by another ``nn.Module``. For example,
2024-11-01T17:51:30.0502287Z                 the following will ensure ``state_dict`` is called on all non-FSDP
2024-11-01T17:51:30.0502613Z                 instances, while dispatching into `sharded_state_dict` implementation
2024-11-01T17:51:30.0502781Z                 for FSDP:
2024-11-01T17:51:30.0502877Z     
2024-11-01T17:51:30.0503039Z             Example::
2024-11-01T17:51:30.0503149Z     
2024-11-01T17:51:30.0503344Z                 >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0503503Z                 >>> model = DDP(FSDP(...))
2024-11-01T17:51:30.0503658Z                 >>> FSDP.set_state_dict_type(
2024-11-01T17:51:30.0503771Z                 >>>     model,
2024-11-01T17:51:30.0503984Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2024-11-01T17:51:30.0504296Z                 >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
2024-11-01T17:51:30.0504636Z                 >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
2024-11-01T17:51:30.0504744Z                 >>> )
2024-11-01T17:51:30.0504942Z                 >>> param_state_dict = model.state_dict()
2024-11-01T17:51:30.0505188Z                 >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2024-11-01T17:51:30.0505287Z     
2024-11-01T17:51:30.0505405Z             Args:
2024-11-01T17:51:30.0505587Z                 module (torch.nn.Module): Root module.
2024-11-01T17:51:30.0505932Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2024-11-01T17:51:30.0506269Z                 state_dict_config (Optional[StateDictConfig]): the configuration for the
2024-11-01T17:51:30.0506441Z                     target ``state_dict_type``.
2024-11-01T17:51:30.0507064Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
2024-11-01T17:51:30.0507227Z                     for the optimizer state dict.
2024-11-01T17:51:30.0507337Z     
2024-11-01T17:51:30.0507449Z             Returns:
2024-11-01T17:51:30.0507776Z                 A StateDictSettings that include the previous state_dict type and
2024-11-01T17:51:30.0507933Z                 configuration for the module.
2024-11-01T17:51:30.0508053Z             
2024-11-01T17:51:30.0508490Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0508590Z     
2024-11-01T17:51:30.0508728Z       warnings.warn(msg)
2024-11-01T17:51:30.0508825Z     
2024-11-01T17:51:30.0509068Z --- Parse Warning: 55 / 103 ---
2024-11-01T17:51:30.0510933Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=805.
2024-11-01T17:51:30.0511511Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0511887Z     Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
2024-11-01T17:51:30.0511985Z     
2024-11-01T17:51:30.0512470Z             This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
2024-11-01T17:51:30.0512659Z             :meth:`set_state_dict_type` for the detail.
2024-11-01T17:51:30.0512772Z     
2024-11-01T17:51:30.0512893Z             Example::
2024-11-01T17:51:30.0513007Z     
2024-11-01T17:51:30.0513205Z                 >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0513349Z                 >>> model = DDP(FSDP(...))
2024-11-01T17:51:30.0513521Z                 >>> with FSDP.state_dict_type(
2024-11-01T17:51:30.0513634Z                 >>>     model,
2024-11-01T17:51:30.0513839Z                 >>>     StateDictType.SHARDED_STATE_DICT,
2024-11-01T17:51:30.0514067Z                 >>> ):
2024-11-01T17:51:30.0514242Z                 >>>     checkpoint = model.state_dict()
2024-11-01T17:51:30.0514335Z     
2024-11-01T17:51:30.0514452Z             Args:
2024-11-01T17:51:30.0514631Z                 module (torch.nn.Module): Root module.
2024-11-01T17:51:30.0514973Z                 state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
2024-11-01T17:51:30.0515378Z                 state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
2024-11-01T17:51:30.0515751Z                     configuration for the target ``state_dict_type``.
2024-11-01T17:51:30.0516080Z                 optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
2024-11-01T17:51:30.0516367Z                    ``state_dict`` configuration for the target ``state_dict_type``.
2024-11-01T17:51:30.0516484Z             
2024-11-01T17:51:30.0516929Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0517042Z     
2024-11-01T17:51:30.0517160Z       warnings.warn(msg)
2024-11-01T17:51:30.0517272Z     
2024-11-01T17:51:30.0517500Z --- Parse Warning: 56 / 103 ---
2024-11-01T17:51:30.0519376Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1818.
2024-11-01T17:51:30.0519844Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0519941Z     
2024-11-01T17:51:30.0520383Z     Transform the state-dict of an optimizer corresponding to a sharded model.
2024-11-01T17:51:30.0520478Z     
2024-11-01T17:51:30.0520837Z     The given state-dict can be transformed to one of three types:
2024-11-01T17:51:30.0521269Z     1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
2024-11-01T17:51:30.0521366Z     
2024-11-01T17:51:30.0521734Z     For full optimizer state_dict, all states are unflattened and not sharded.
2024-11-01T17:51:30.0522054Z     Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
2024-11-01T17:51:30.0522177Z     avoid OOM.
2024-11-01T17:51:30.0522273Z     
2024-11-01T17:51:30.0522617Z     For sharded optimizer state_dict, all states are unflattened but sharded.
2024-11-01T17:51:30.0522930Z     CPU only can be specified via :meth:`state_dict_type` to further save
2024-11-01T17:51:30.0523040Z     memory.
2024-11-01T17:51:30.0523154Z     
2024-11-01T17:51:30.0523472Z     For local state_dict, no transformation will be performed. But a state
2024-11-01T17:51:30.0523837Z     will be converted from nn.Tensor to ShardedTensor to represent its sharding
2024-11-01T17:51:30.0523988Z     nature (this is not supported yet).
2024-11-01T17:51:30.0524098Z     
2024-11-01T17:51:30.0524258Z     Example::
2024-11-01T17:51:30.0524356Z     
2024-11-01T17:51:30.0524560Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0524903Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2024-11-01T17:51:30.0525150Z         >>> from torch.distributed.fsdp import StateDictType
2024-11-01T17:51:30.0525408Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2024-11-01T17:51:30.0525714Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2024-11-01T17:51:30.0525843Z         >>> # Save a checkpoint
2024-11-01T17:51:30.0525972Z         >>> model, optim = ...
2024-11-01T17:51:30.0526126Z         >>> FSDP.set_state_dict_type(
2024-11-01T17:51:30.0526235Z         >>>     model,
2024-11-01T17:51:30.0526420Z         >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:30.0526609Z         >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0526827Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0526947Z         >>> )
2024-11-01T17:51:30.0527098Z         >>> state_dict = model.state_dict()
2024-11-01T17:51:30.0527351Z         >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
2024-11-01T17:51:30.0527557Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2024-11-01T17:51:30.0527700Z         >>> # Load a checkpoint
2024-11-01T17:51:30.0527826Z         >>> model, optim = ...
2024-11-01T17:51:30.0528043Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2024-11-01T17:51:30.0528245Z         >>> FSDP.set_state_dict_type(
2024-11-01T17:51:30.0528352Z         >>>     model,
2024-11-01T17:51:30.0528578Z         >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:30.0528768Z         >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0528992Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0529096Z         >>> )
2024-11-01T17:51:30.0529254Z         >>> model.load_state_dict(state_dict)
2024-11-01T17:51:30.0529479Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2024-11-01T17:51:30.0529640Z         >>>     model, optim, optim_state_dict
2024-11-01T17:51:30.0529754Z         >>> )
2024-11-01T17:51:30.0529925Z         >>> optim.load_state_dict(optim_state_dict)
2024-11-01T17:51:30.0530032Z     
2024-11-01T17:51:30.0530133Z     Args:
2024-11-01T17:51:30.0530417Z         model (torch.nn.Module): Root module (which may or may not be a
2024-11-01T17:51:30.0530716Z             :class:`FullyShardedDataParallel` instance) whose parameters
2024-11-01T17:51:30.0530909Z             were passed into the optimizer ``optim``.
2024-11-01T17:51:30.0531282Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2024-11-01T17:51:30.0531435Z             parameters.
2024-11-01T17:51:30.0531735Z         optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
2024-11-01T17:51:30.0532055Z             transform. If the value is None, optim.state_dict() will be used. (
2024-11-01T17:51:30.0532176Z             Default: ``None``)
2024-11-01T17:51:30.0532609Z         group (dist.ProcessGroup): Model's process group across which parameters
2024-11-01T17:51:30.0532879Z             are sharded or ``None`` if using the default process group. (
2024-11-01T17:51:30.0533012Z             Default: ``None``)
2024-11-01T17:51:30.0533107Z     
2024-11-01T17:51:30.0533212Z     Returns:
2024-11-01T17:51:30.0533510Z         Dict[str, Any]: A :class:`dict` containing the optimizer state for
2024-11-01T17:51:30.0533756Z         ``model``. The sharding of the optimizer state is based on
2024-11-01T17:51:30.0533890Z         ``state_dict_type``.
2024-11-01T17:51:30.0533989Z     
2024-11-01T17:51:30.0534431Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0534526Z     
2024-11-01T17:51:30.0534646Z       warnings.warn(msg)
2024-11-01T17:51:30.0534755Z     
2024-11-01T17:51:30.0534984Z --- Parse Warning: 57 / 103 ---
2024-11-01T17:51:30.0536955Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1916.
2024-11-01T17:51:30.0537400Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0537511Z     
2024-11-01T17:51:30.0538152Z     Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
2024-11-01T17:51:30.0538264Z     
2024-11-01T17:51:30.0538502Z     Given a ``optim_state_dict`` that is transformed through
2024-11-01T17:51:30.0538804Z     :meth:`optim_state_dict`, it gets converted to the flattened optimizer
2024-11-01T17:51:30.0539125Z     state_dict that can be loaded to ``optim`` which is the optimizer for
2024-11-01T17:51:30.0539399Z     ``model``. ``model`` must be sharded by FullyShardedDataParallel.
2024-11-01T17:51:30.0539510Z     
2024-11-01T17:51:30.0539695Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0540050Z         >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
2024-11-01T17:51:30.0540277Z         >>> from torch.distributed.fsdp import StateDictType
2024-11-01T17:51:30.0540533Z         >>> from torch.distributed.fsdp import FullStateDictConfig
2024-11-01T17:51:30.0547577Z         >>> from torch.distributed.fsdp import FullOptimStateDictConfig
2024-11-01T17:51:30.0547873Z         >>> # Save a checkpoint
2024-11-01T17:51:30.0548001Z         >>> model, optim = ...
2024-11-01T17:51:30.0548228Z         >>> FSDP.set_state_dict_type(
2024-11-01T17:51:30.0548338Z         >>>     model,
2024-11-01T17:51:30.0548516Z         >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:30.0548722Z         >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0548938Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0549057Z         >>> )
2024-11-01T17:51:30.0549212Z         >>> state_dict = model.state_dict()
2024-11-01T17:51:30.0549390Z         >>> original_osd = optim.state_dict()
2024-11-01T17:51:30.0549575Z         >>> optim_state_dict = FSDP.optim_state_dict(
2024-11-01T17:51:30.0549683Z         >>>     model,
2024-11-01T17:51:30.0549805Z         >>>     optim,
2024-11-01T17:51:30.0549959Z         >>>     optim_state_dict=original_osd
2024-11-01T17:51:30.0550076Z         >>> )
2024-11-01T17:51:30.0550283Z         >>> save_a_checkpoint(state_dict, optim_state_dict)
2024-11-01T17:51:30.0550427Z         >>> # Load a checkpoint
2024-11-01T17:51:30.0550555Z         >>> model, optim = ...
2024-11-01T17:51:30.0550773Z         >>> state_dict, optim_state_dict = load_a_checkpoint()
2024-11-01T17:51:30.0550924Z         >>> FSDP.set_state_dict_type(
2024-11-01T17:51:30.0551030Z         >>>     model,
2024-11-01T17:51:30.0551211Z         >>>     StateDictType.FULL_STATE_DICT,
2024-11-01T17:51:30.0551399Z         >>>     FullStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0551626Z         >>>     FullOptimStateDictConfig(rank0_only=False),
2024-11-01T17:51:30.0551726Z         >>> )
2024-11-01T17:51:30.0551884Z         >>> model.load_state_dict(state_dict)
2024-11-01T17:51:30.0552110Z         >>> optim_state_dict = FSDP.optim_state_dict_to_load(
2024-11-01T17:51:30.0552268Z         >>>     model, optim, optim_state_dict
2024-11-01T17:51:30.0552386Z         >>> )
2024-11-01T17:51:30.0552559Z         >>> optim.load_state_dict(optim_state_dict)
2024-11-01T17:51:30.0552659Z     
2024-11-01T17:51:30.0552775Z     Args:
2024-11-01T17:51:30.0553068Z         model (torch.nn.Module): Root module (which may or may not be a
2024-11-01T17:51:30.0553367Z             :class:`FullyShardedDataParallel` instance) whose parameters
2024-11-01T17:51:30.0553556Z             were passed into the optimizer ``optim``.
2024-11-01T17:51:30.0554078Z         optim (torch.optim.Optimizer): Optimizer for ``model`` 's
2024-11-01T17:51:30.0554195Z             parameters.
2024-11-01T17:51:30.0554547Z         optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
2024-11-01T17:51:30.0554845Z         is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
2024-11-01T17:51:30.0555163Z             KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
2024-11-01T17:51:30.0555497Z             KeyedOptimizer or torch.distributed's NamedOptimizer.
2024-11-01T17:51:30.0555781Z         load_directly (bool): If this is set to True, this API will also
2024-11-01T17:51:30.0556091Z             call optim.load_state_dict(result) before returning the result.
2024-11-01T17:51:30.0556407Z             Otherwise, users are responsible to call ``optim.load_state_dict()``
2024-11-01T17:51:30.0556531Z             (Default: ``False``)
2024-11-01T17:51:30.0556967Z         group (dist.ProcessGroup): Model's process group across which parameters
2024-11-01T17:51:30.0557240Z             are sharded or ``None`` if using the default process group. (
2024-11-01T17:51:30.0557379Z             Default: ``None``)
2024-11-01T17:51:30.0557477Z     
2024-11-01T17:51:30.0557924Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0558020Z     
2024-11-01T17:51:30.0558142Z       warnings.warn(msg)
2024-11-01T17:51:30.0558249Z     
2024-11-01T17:51:30.0558481Z --- Parse Warning: 58 / 103 ---
2024-11-01T17:51:30.0560127Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=137.
2024-11-01T17:51:30.0560670Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0560782Z     
2024-11-01T17:51:30.0561089Z     RemoteModule instance can only be created after RPC initialization.
2024-11-01T17:51:30.0561200Z     
2024-11-01T17:51:30.0561551Z     It creates a user-specified module on a specified remote node.
2024-11-01T17:51:30.0561904Z     It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2024-11-01T17:51:30.0562053Z     executed on the remote node.
2024-11-01T17:51:30.0562395Z     It takes care of autograd recording to ensure the backward pass propagates
2024-11-01T17:51:30.0562626Z     gradients back to the corresponding remote module.
2024-11-01T17:51:30.0563144Z     It can be shared across processors using `RPC framework <https://pytorch.org/docs/stable/rpc.html>`__,
2024-11-01T17:51:30.0563434Z     without incurring any overheads of copying the actual module,
2024-11-01T17:51:30.0563729Z     which is equivalent to an :class:`~torch.distributed.rpc.RRef`
2024-11-01T17:51:30.0563908Z     pointing to the remote module.
2024-11-01T17:51:30.0564019Z     
2024-11-01T17:51:30.0564302Z     The arguments of ``forward_async`` and ``forward`` are the same as
2024-11-01T17:51:30.0564614Z     the ``forward`` method of the module returned by the ``module_cls``.
2024-11-01T17:51:30.0564713Z     
2024-11-01T17:51:30.0565184Z     Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
2024-11-01T17:51:30.0565280Z     
2024-11-01T17:51:30.0565642Z     Particularly, to create a hybrid model, typically the local modules should be
2024-11-01T17:51:30.0566197Z     created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
2024-11-01T17:51:30.0566312Z     Hybrid Example:
2024-11-01T17:51:30.0566484Z             >>> class HybridModel(nn.Module):
2024-11-01T17:51:30.0566700Z             >>>     def __init__(self) -> None:
2024-11-01T17:51:30.0566878Z             >>>         nn.Module.__init__(self)
2024-11-01T17:51:30.0567085Z             >>>         self.remote_embedding = RemoteModule(...)
2024-11-01T17:51:30.0567267Z             >>>         self.local_linear = nn.Linear(...)
2024-11-01T17:51:30.0567377Z     
2024-11-01T17:51:30.0567674Z     For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2024-11-01T17:51:30.0568201Z     that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
2024-11-01T17:51:30.0568496Z     the generated ``RemoteModule`` will have 2 methods in signature of
2024-11-01T17:51:30.0568749Z     ``def forward(input: Tensor) -> Tensor:`` and
2024-11-01T17:51:30.0569042Z     ``def forward_async(input: Tensor) -> Future[Tensor]:``.
2024-11-01T17:51:30.0569137Z     
2024-11-01T17:51:30.0569281Z     .. note::
2024-11-01T17:51:30.0569487Z         If the remote module is placed on a cuda device,
2024-11-01T17:51:30.0569855Z         any input CPU tensors will be automatically moved to the same cuda device,
2024-11-01T17:51:30.0570460Z         and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
2024-11-01T17:51:30.0570570Z     
2024-11-01T17:51:30.0570671Z     Args:
2024-11-01T17:51:30.0571199Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2024-11-01T17:51:30.0571667Z             The device can be a local device or a remote device specified by one of the following remote
2024-11-01T17:51:30.0571772Z             formats:
2024-11-01T17:51:30.0571885Z     
2024-11-01T17:51:30.0572091Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2024-11-01T17:51:30.0572318Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2024-11-01T17:51:30.0572415Z     
2024-11-01T17:51:30.0572776Z             In addition, the device field can be optional and the default value is "cpu".
2024-11-01T17:51:30.0572993Z         module_cls (nn.Module): For example,
2024-11-01T17:51:30.0573191Z             >>> class MyModule(nn.Module):
2024-11-01T17:51:30.0573342Z             >>>     def forward(input):
2024-11-01T17:51:30.0573478Z             >>>         return input + 1
2024-11-01T17:51:30.0573579Z             >>>
2024-11-01T17:51:30.0573726Z             >>> module_cls = MyModule
2024-11-01T17:51:30.0574005Z         args (Sequence, optional): args to be passed to ``module_cls``.
2024-11-01T17:51:30.0574295Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2024-11-01T17:51:30.0574682Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2024-11-01T17:51:30.0575054Z             to be created. The type object should be decorated by @torch.jit.interface.
2024-11-01T17:51:30.0575459Z             If not provided, the generated RemoteModule is not torchscript-able.
2024-11-01T17:51:30.0575817Z             Warning, this is an experimental API and susceptible to frequent changes.
2024-11-01T17:51:30.0575917Z     
2024-11-01T17:51:30.0576025Z     Returns:
2024-11-01T17:51:30.0576395Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2024-11-01T17:51:30.0576801Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2024-11-01T17:51:30.0577194Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2024-11-01T17:51:30.0577461Z         on the user-provided module on the remote side.
2024-11-01T17:51:30.0577572Z     
2024-11-01T17:51:30.0577683Z     Example::
2024-11-01T17:51:30.0577903Z         Run the following code in two different processes:
2024-11-01T17:51:30.0578014Z     
2024-11-01T17:51:30.0578174Z         >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0578306Z         >>> # On worker 0:
2024-11-01T17:51:30.0578422Z         >>> import torch
2024-11-01T17:51:30.0578601Z         >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:30.0578761Z         >>> from torch import nn, Tensor
2024-11-01T17:51:30.0579079Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2024-11-01T17:51:30.0579194Z         >>>
2024-11-01T17:51:30.0579390Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2024-11-01T17:51:30.0579574Z         >>> remote_linear_module = RemoteModule(
2024-11-01T17:51:30.0579757Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2024-11-01T17:51:30.0579900Z         >>> )
2024-11-01T17:51:30.0580058Z         >>> input = torch.randn(128, 20)
2024-11-01T17:51:30.0580281Z         >>> ret_fut = remote_linear_module.forward_async(input)
2024-11-01T17:51:30.0580420Z         >>> ret = ret_fut.wait()
2024-11-01T17:51:30.0580539Z         >>> rpc.shutdown()
2024-11-01T17:51:30.0580649Z     
2024-11-01T17:51:30.0580764Z         >>> # On worker 1:
2024-11-01T17:51:30.0580876Z         >>> import torch
2024-11-01T17:51:30.0581067Z         >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:30.0581169Z         >>>
2024-11-01T17:51:30.0581424Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2024-11-01T17:51:30.0581542Z         >>> rpc.shutdown()
2024-11-01T17:51:30.0581702Z     
2024-11-01T17:51:30.0582151Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0582244Z     
2024-11-01T17:51:30.0582379Z       warnings.warn(msg)
2024-11-01T17:51:30.0582473Z     
2024-11-01T17:51:30.0582701Z --- Parse Warning: 59 / 103 ---
2024-11-01T17:51:30.0584430Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=514.
2024-11-01T17:51:30.0584879Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0584987Z     
2024-11-01T17:51:30.0585439Z     Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
2024-11-01T17:51:30.0585579Z     
2024-11-01T17:51:30.0586090Z     This alternate initialization method can be particularly useful if we want to create multiple
2024-11-01T17:51:30.0586551Z     RemoteModule instances that share the same underlying module and reduce memory consumption.
2024-11-01T17:51:30.0586648Z     
2024-11-01T17:51:30.0587044Z     Moreover, this also provides a workaround for passing script RemoteModule over RPC,
2024-11-01T17:51:30.0587312Z     which is not supported. The recommended way is as follows:
2024-11-01T17:51:30.0587407Z     
2024-11-01T17:51:30.0587586Z         1. the sender creates a RemoteModule;
2024-11-01T17:51:30.0587796Z         2. the sender sends its ``module_rref`` over RPC;
2024-11-01T17:51:30.0588307Z         3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
2024-11-01T17:51:30.0588402Z     
2024-11-01T17:51:30.0588512Z     Example::
2024-11-01T17:51:30.0588749Z         Run the following code in two different processes:
2024-11-01T17:51:30.0588843Z     
2024-11-01T17:51:30.0589021Z         >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0589142Z         >>> # On worker 0:
2024-11-01T17:51:30.0589273Z         >>> import torch
2024-11-01T17:51:30.0589449Z         >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:30.0589594Z         >>> from torch import nn, Tensor
2024-11-01T17:51:30.0589922Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2024-11-01T17:51:30.0590028Z         >>>
2024-11-01T17:51:30.0590233Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2024-11-01T17:51:30.0590382Z         >>> remote_module = RemoteModule(
2024-11-01T17:51:30.0590569Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2024-11-01T17:51:30.0590685Z         >>> )
2024-11-01T17:51:30.0590785Z         >>>
2024-11-01T17:51:30.0590944Z         >>> remote_module1 = rpc.rpc_sync(
2024-11-01T17:51:30.0591067Z         >>>     "worker1/cpu",
2024-11-01T17:51:30.0591256Z         >>>     RemoteModule.init_from_module_rref,
2024-11-01T17:51:30.0591479Z         >>>     ("worker1/cpu", remote_module1.get_module_rref()),
2024-11-01T17:51:30.0591579Z         >>> )
2024-11-01T17:51:30.0591714Z         >>> rpc.shutdown()
2024-11-01T17:51:30.0591810Z     
2024-11-01T17:51:30.0591939Z         >>> # On worker 1:
2024-11-01T17:51:30.0592056Z         >>> import torch
2024-11-01T17:51:30.0592230Z         >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:30.0592377Z         >>>
2024-11-01T17:51:30.0592571Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2024-11-01T17:51:30.0592702Z         >>> rpc.shutdown()
2024-11-01T17:51:30.0592797Z     
2024-11-01T17:51:30.0592911Z     Args:
2024-11-01T17:51:30.0593450Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2024-11-01T17:51:30.0594034Z             The device can be a local device or a remote device specified by one of the following remote
2024-11-01T17:51:30.0594164Z             formats:
2024-11-01T17:51:30.0594258Z     
2024-11-01T17:51:30.0594479Z                 1. "rank:<rank>/<device>" (ex: "rank:0/cuda:0").
2024-11-01T17:51:30.0594697Z                 2. "<worker_name>/<device>" (ex: "trainer0/cuda:0").
2024-11-01T17:51:30.0594805Z     
2024-11-01T17:51:30.0595169Z             In addition, the device field can be optional and the default value is "cpu".
2024-11-01T17:51:30.0595537Z         module_rref (RRef[nn.Module]): The module reference shared by both the caller and
2024-11-01T17:51:30.0595695Z             the created remote module.
2024-11-01T17:51:30.0596079Z         _module_interface_cls (type, optional): The TorchScript interface type for the module
2024-11-01T17:51:30.0596447Z             to be created. The type object should be decorated by @torch.jit.interface.
2024-11-01T17:51:30.0596856Z             If not provided, the generated RemoteModule is not torchscript-able.
2024-11-01T17:51:30.0597262Z             Warning, this is an experimental API and susceptible to frequent changes.
2024-11-01T17:51:30.0597359Z     
2024-11-01T17:51:30.0597513Z     Returns:
2024-11-01T17:51:30.0597875Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2024-11-01T17:51:30.0598291Z         user-provided ``module_rref``, it has a blocking ``forward`` method and an
2024-11-01T17:51:30.0598684Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2024-11-01T17:51:30.0598951Z         on the user-provided module on the remote side.
2024-11-01T17:51:30.0599062Z     
2024-11-01T17:51:30.0599486Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0599582Z     
2024-11-01T17:51:30.0599715Z       warnings.warn(msg)
2024-11-01T17:51:30.0599809Z     
2024-11-01T17:51:30.0600055Z --- Parse Warning: 60 / 103 ---
2024-11-01T17:51:30.0601632Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/nn/api/remote_module.py line=606.
2024-11-01T17:51:30.0602094Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0602189Z     
2024-11-01T17:51:30.0602529Z         A RemoteModule instance can only be created after RPC initialization.
2024-11-01T17:51:30.0602625Z     
2024-11-01T17:51:30.0602982Z         It creates a user-specified module on a specified remote node.
2024-11-01T17:51:30.0603352Z         It behaves like a regular ``nn.Module`` except that the ``forward`` method is
2024-11-01T17:51:30.0603492Z         executed on the remote node.
2024-11-01T17:51:30.0603853Z         It takes care of autograd recording to ensure the backward pass propagates
2024-11-01T17:51:30.0604070Z         gradients back to the corresponding remote module.
2024-11-01T17:51:30.0604181Z     
2024-11-01T17:51:30.0604499Z         It generates two methods ``forward_async`` and ``forward`` based on the
2024-11-01T17:51:30.0604826Z         signature of the ``forward`` method of ``module_cls``. ``forward_async``
2024-11-01T17:51:30.0605195Z         runs asynchronously and returns a Future. The arguments of ``forward_async``
2024-11-01T17:51:30.0605487Z         and ``forward`` are the same as the ``forward`` method of the module
2024-11-01T17:51:30.0605643Z         returned by the ``module_cls``.
2024-11-01T17:51:30.0605740Z     
2024-11-01T17:51:30.0606084Z         For example, if ``module_cls`` returns an instance of ``nn.Linear``,
2024-11-01T17:51:30.0606896Z         that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
2024-11-01T17:51:30.0607222Z         the generated ``RemoteModule`` will have 2 methods with the signatures:
2024-11-01T17:51:30.0607331Z     
2024-11-01T17:51:30.0607569Z         | ``def forward(input: Tensor) -> Tensor:``
2024-11-01T17:51:30.0607882Z         | ``def forward_async(input: Tensor) -> Future[Tensor]:``
2024-11-01T17:51:30.0607981Z     
2024-11-01T17:51:30.0608084Z     Args:
2024-11-01T17:51:30.0608625Z         remote_device (str): Device on the destination worker where we'd like to place this module.
2024-11-01T17:51:30.0609119Z             The format should be "<workername>/<device>", where the device field can be parsed as torch.device type.
2024-11-01T17:51:30.0609338Z             E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
2024-11-01T17:51:30.0609700Z             In addition, the device field can be optional and the default value is "cpu".
2024-11-01T17:51:30.0610088Z         module_cls (nn.Module): Class for the module to be created remotely. For example,
2024-11-01T17:51:30.0610183Z     
2024-11-01T17:51:30.0610345Z             >>> class MyModule(nn.Module):
2024-11-01T17:51:30.0610479Z             >>>     def forward(input):
2024-11-01T17:51:30.0610617Z             >>>         return input + 1
2024-11-01T17:51:30.0610733Z             >>>
2024-11-01T17:51:30.0610865Z             >>> module_cls = MyModule
2024-11-01T17:51:30.0611101Z     
2024-11-01T17:51:30.0611477Z         args (Sequence, optional): args to be passed to ``module_cls``.
2024-11-01T17:51:30.0611752Z         kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
2024-11-01T17:51:30.0611862Z     
2024-11-01T17:51:30.0611965Z     Returns:
2024-11-01T17:51:30.0612326Z         A remote module instance which wraps the :class:`~nn.Module` created by the
2024-11-01T17:51:30.0612740Z         user-provided ``module_cls``, it has a blocking ``forward`` method and an
2024-11-01T17:51:30.0613133Z         asynchronous ``forward_async`` method that returns a future of the ``forward`` call
2024-11-01T17:51:30.0613400Z         on the user-provided module on the remote side.
2024-11-01T17:51:30.0613497Z     
2024-11-01T17:51:30.0613628Z     Example::
2024-11-01T17:51:30.0613847Z         Run the following code in two different processes:
2024-11-01T17:51:30.0613956Z     
2024-11-01T17:51:30.0614116Z         >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0614248Z         >>> # On worker 0:
2024-11-01T17:51:30.0614362Z         >>> import torch
2024-11-01T17:51:30.0614544Z         >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:30.0614702Z         >>> from torch import nn, Tensor
2024-11-01T17:51:30.0615016Z         >>> from torch.distributed.nn.api.remote_module import RemoteModule
2024-11-01T17:51:30.0615132Z         >>>
2024-11-01T17:51:30.0615325Z         >>> rpc.init_rpc("worker0", rank=0, world_size=2)
2024-11-01T17:51:30.0615510Z         >>> remote_linear_module = RemoteModule(
2024-11-01T17:51:30.0615693Z         >>>     "worker1/cpu", nn.Linear, args=(20, 30),
2024-11-01T17:51:30.0615795Z         >>> )
2024-11-01T17:51:30.0615948Z         >>> input = torch.randn(128, 20)
2024-11-01T17:51:30.0616169Z         >>> ret_fut = remote_linear_module.forward_async(input)
2024-11-01T17:51:30.0616308Z         >>> ret = ret_fut.wait()
2024-11-01T17:51:30.0616428Z         >>> rpc.shutdown()
2024-11-01T17:51:30.0616526Z     
2024-11-01T17:51:30.0616654Z         >>> # On worker 1:
2024-11-01T17:51:30.0616769Z         >>> import torch
2024-11-01T17:51:30.0616966Z         >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:30.0617066Z         >>>
2024-11-01T17:51:30.0617274Z         >>> rpc.init_rpc("worker1", rank=1, world_size=2)
2024-11-01T17:51:30.0617391Z         >>> rpc.shutdown()
2024-11-01T17:51:30.0617486Z     
2024-11-01T17:51:30.0617765Z         Furthermore, a more practical example that is combined with
2024-11-01T17:51:30.0618484Z         `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
2024-11-01T17:51:30.0619001Z         can be found in this `tutorial <https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html>`__.
2024-11-01T17:51:30.0619097Z     
2024-11-01T17:51:30.0619547Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0619643Z     
2024-11-01T17:51:30.0619764Z       warnings.warn(msg)
2024-11-01T17:51:30.0619876Z     
2024-11-01T17:51:30.0620107Z --- Parse Warning: 61 / 103 ---
2024-11-01T17:51:30.0621718Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/optimizer.py line=130.
2024-11-01T17:51:30.0622164Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0622277Z     
2024-11-01T17:51:30.0622597Z     DistributedOptimizer takes remote references to parameters scattered
2024-11-01T17:51:30.0622945Z     across workers and applies the given optimizer locally for each parameter.
2024-11-01T17:51:30.0623055Z     
2024-11-01T17:51:30.0623402Z     This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
2024-11-01T17:51:30.0623629Z     to retrieve the gradients for specific parameters.
2024-11-01T17:51:30.0623726Z     
2024-11-01T17:51:30.0623893Z     Concurrent calls to
2024-11-01T17:51:30.0624252Z     :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
2024-11-01T17:51:30.0624449Z     either from the same or different clients, will
2024-11-01T17:51:30.0624869Z     be serialized on each worker -- as each worker's optimizer can only work
2024-11-01T17:51:30.0625185Z     on one set of gradients at a time. However, there is no guarantee that
2024-11-01T17:51:30.0625624Z     the full forward-backward-optimizer sequence will execute for one client
2024-11-01T17:51:30.0625960Z     at a time. This means that the gradients being applied may not correspond
2024-11-01T17:51:30.0626295Z     to the latest forward pass executed on a given worker. Also, there is no
2024-11-01T17:51:30.0626446Z     guaranteed ordering across workers.
2024-11-01T17:51:30.0626541Z     
2024-11-01T17:51:30.0626906Z     `DistributedOptimizer` creates the local optimizer with TorchScript enabled
2024-11-01T17:51:30.0627245Z     by default, so that optimizer updates are not blocked by the Python Global
2024-11-01T17:51:30.0627623Z     Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
2024-11-01T17:51:30.0627967Z     Model Parallel). This feature is currently enabled for most optimizers. You
2024-11-01T17:51:30.0628354Z     can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
2024-11-01T17:51:30.0628494Z     for your own custom optimizers.
2024-11-01T17:51:30.0628588Z     
2024-11-01T17:51:30.0628705Z     Args:
2024-11-01T17:51:30.0628981Z         optimizer_class (optim.Optimizer): the class of optimizer to
2024-11-01T17:51:30.0629135Z             instantiate on each worker.
2024-11-01T17:51:30.0629442Z         params_rref (list[RRef]): list of RRefs to local or remote parameters
2024-11-01T17:51:30.0629570Z             to optimize.
2024-11-01T17:51:30.0629884Z         args: arguments to pass to the optimizer constructor on each worker.
2024-11-01T17:51:30.0630201Z         kwargs: arguments to pass to the optimizer constructor on each worker.
2024-11-01T17:51:30.0630314Z     
2024-11-01T17:51:30.0630425Z     Example::
2024-11-01T17:51:30.0630603Z         >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0630842Z         >>> import torch.distributed.autograd as dist_autograd
2024-11-01T17:51:30.0631034Z         >>> import torch.distributed.rpc as rpc
2024-11-01T17:51:30.0631168Z         >>> from torch import optim
2024-11-01T17:51:30.0631440Z         >>> from torch.distributed.optim import DistributedOptimizer
2024-11-01T17:51:30.0631593Z         >>>
2024-11-01T17:51:30.0631787Z         >>> with dist_autograd.context() as context_id:
2024-11-01T17:51:30.0631927Z         >>>   # Forward pass.
2024-11-01T17:51:30.0632219Z         >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
2024-11-01T17:51:30.0632518Z         >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
2024-11-01T17:51:30.0632697Z         >>>   loss = rref1.to_here() + rref2.to_here()
2024-11-01T17:51:30.0632802Z         >>>
2024-11-01T17:51:30.0632939Z         >>>   # Backward pass.
2024-11-01T17:51:30.0633173Z         >>>   dist_autograd.backward(context_id, [loss.sum()])
2024-11-01T17:51:30.0633290Z         >>>
2024-11-01T17:51:30.0633407Z         >>>   # Optimizer.
2024-11-01T17:51:30.0633622Z         >>>   dist_optim = DistributedOptimizer(
2024-11-01T17:51:30.0633752Z         >>>      optim.SGD,
2024-11-01T17:51:30.0634011Z         >>>      [rref1, rref2],
2024-11-01T17:51:30.0634140Z         >>>      lr=0.05,
2024-11-01T17:51:30.0634246Z         >>>   )
2024-11-01T17:51:30.0634405Z         >>>   dist_optim.step(context_id)
2024-11-01T17:51:30.0634501Z     
2024-11-01T17:51:30.0634746Z     __ https://github.com/pytorch/tutorials/pull/1465
2024-11-01T17:51:30.0634855Z     
2024-11-01T17:51:30.0635328Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0635435Z     
2024-11-01T17:51:30.0635555Z       warnings.warn(msg)
2024-11-01T17:51:30.0635650Z     
2024-11-01T17:51:30.0635943Z --- Parse Warning: 62 / 103 ---
2024-11-01T17:51:30.0637693Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9.
2024-11-01T17:51:30.0638154Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0638254Z     
2024-11-01T17:51:30.0638938Z     Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
2024-11-01T17:51:30.0639151Z     This optimizer runs local optimizer at every step.
2024-11-01T17:51:30.0639749Z     After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.
2024-11-01T17:51:30.0639846Z     
2024-11-01T17:51:30.0639946Z     Args:
2024-11-01T17:51:30.0640095Z         optim: The local optimizer.
2024-11-01T17:51:30.0640485Z         averager: A model averager instance to run post-localSGD algorithm.
2024-11-01T17:51:30.0640595Z     
2024-11-01T17:51:30.0640711Z     Example::
2024-11-01T17:51:30.0640808Z     
2024-11-01T17:51:30.0641004Z         >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.0641119Z         >>> import torch
2024-11-01T17:51:30.0641294Z         >>> import torch.distributed as dist
2024-11-01T17:51:30.0641679Z         >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
2024-11-01T17:51:30.0641864Z         >>> import torch.nn as nn
2024-11-01T17:51:30.0642139Z         >>> from torch.distributed.optim import PostLocalSGDOptimizer
2024-11-01T17:51:30.0642528Z         >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
2024-11-01T17:51:30.0642674Z         >>>   PostLocalSGDState,
2024-11-01T17:51:30.0642804Z         >>>   post_localSGD_hook,
2024-11-01T17:51:30.0642921Z         >>> )
2024-11-01T17:51:30.0643022Z         >>>
2024-11-01T17:51:30.0643261Z         >>> model = nn.parallel.DistributedDataParallel(
2024-11-01T17:51:30.0643467Z         >>>    module, device_ids=[rank], output_device=rank
2024-11-01T17:51:30.0643568Z         >>> )
2024-11-01T17:51:30.0643683Z         >>>
2024-11-01T17:51:30.0643955Z         >>> # Register a post-localSGD communication hook.
2024-11-01T17:51:30.0644378Z         >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
2024-11-01T17:51:30.0644605Z         >>> model.register_comm_hook(state, post_localSGD_hook)
2024-11-01T17:51:30.0644758Z         >>>
2024-11-01T17:51:30.0645136Z         >>> # Create a post-localSGD optimizer that wraps a local optimizer.
2024-11-01T17:51:30.0645515Z         >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
2024-11-01T17:51:30.0645765Z         >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
2024-11-01T17:51:30.0646065Z         >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
2024-11-01T17:51:30.0646239Z         >>> opt = PostLocalSGDOptimizer(
2024-11-01T17:51:30.0646373Z         >>>     optim=local_optim,
2024-11-01T17:51:30.0646738Z         >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
2024-11-01T17:51:30.0646841Z         >>> )
2024-11-01T17:51:30.0646941Z         >>>
2024-11-01T17:51:30.0647297Z         >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
2024-11-01T17:51:30.0647848Z         >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
2024-11-01T17:51:30.0648513Z         >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
2024-11-01T17:51:30.0648654Z         >>> for step in range(0, 200):
2024-11-01T17:51:30.0648794Z         >>>    opt.zero_grad()
2024-11-01T17:51:30.0648948Z         >>>    loss = loss_fn(output, labels)
2024-11-01T17:51:30.0649069Z         >>>    loss.backward()
2024-11-01T17:51:30.0649240Z         >>>    opt.step()
2024-11-01T17:51:30.0649335Z     
2024-11-01T17:51:30.0649830Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0649936Z     
2024-11-01T17:51:30.0650073Z       warnings.warn(msg)
2024-11-01T17:51:30.0650168Z     
2024-11-01T17:51:30.0650400Z --- Parse Warning: 63 / 103 ---
2024-11-01T17:51:30.0652172Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=282.
2024-11-01T17:51:30.0652622Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0652730Z     
2024-11-01T17:51:30.0653307Z     Wrap an arbitrary :class:`optim.Optimizer <torch.optim.Optimizer>` and shards its states across ranks in the group.
2024-11-01T17:51:30.0653416Z     
2024-11-01T17:51:30.0653595Z     The sharing is done as described by ZeRO_.
2024-11-01T17:51:30.0653690Z     
2024-11-01T17:51:30.0653912Z     The local optimizer instance in each rank is only
2024-11-01T17:51:30.0654250Z     responsible for updating approximately ``1 / world_size`` parameters and
2024-11-01T17:51:30.0654559Z     hence only needs to keep ``1 / world_size`` optimizer states. After
2024-11-01T17:51:30.0654906Z     parameters are updated locally, each rank will broadcast its parameters to
2024-11-01T17:51:30.0655191Z     all other peers to keep all model replicas in the same state.
2024-11-01T17:51:30.0655457Z     ``ZeroRedundancyOptimizer`` can be used in conjunction with
2024-11-01T17:51:30.0655915Z     :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
2024-11-01T17:51:30.0656049Z     memory consumption.
2024-11-01T17:51:30.0656144Z     
2024-11-01T17:51:30.0656585Z     ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
2024-11-01T17:51:30.0656924Z     of parameters at each rank. Each parameter belongs to a single rank and is
2024-11-01T17:51:30.0657299Z     not divided among ranks. The partition is arbitrary and might not match the
2024-11-01T17:51:30.0657478Z     the parameter registration or usage order.
2024-11-01T17:51:30.0657573Z     
2024-11-01T17:51:30.0657695Z     Arguments:
2024-11-01T17:51:30.0657970Z         params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
2024-11-01T17:51:30.0658265Z             or :class:`dict` s giving all parameters, which will be sharded
2024-11-01T17:51:30.0658422Z             across ranks.
2024-11-01T17:51:30.0658535Z     
2024-11-01T17:51:30.0658644Z     Keyword Args:
2024-11-01T17:51:30.0658954Z         optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
2024-11-01T17:51:30.0659082Z             optimizer.
2024-11-01T17:51:30.0659378Z         process_group (``ProcessGroup``, optional): ``torch.distributed``
2024-11-01T17:51:30.0659665Z             ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
2024-11-01T17:51:30.0659870Z             :meth:`torch.distributed.init_process_group`).
2024-11-01T17:51:30.0660204Z         parameters_as_bucket_view (bool, optional): if ``True``, parameters are
2024-11-01T17:51:30.0660513Z             packed into buckets to speed up communication, and ``param.data``
2024-11-01T17:51:30.0660809Z             fields point to bucket views at different offsets; if ``False``,
2024-11-01T17:51:30.0661119Z             each individual parameter is communicated separately, and each
2024-11-01T17:51:30.0661340Z             ``params.data`` stays intact (default: ``False``).
2024-11-01T17:51:30.0661627Z         overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
2024-11-01T17:51:30.0661990Z             overlapped with :class:`DistributedDataParallel` 's gradient
2024-11-01T17:51:30.0662302Z             synchronization; this requires (1) either a functional optimizer
2024-11-01T17:51:30.0662570Z             for the ``optimizer_class`` argument or one with a functional
2024-11-01T17:51:30.0662852Z             equivalent and (2) registering a DDP communication hook
2024-11-01T17:51:30.0663214Z             constructed from one of the functions in ``ddp_zero_hook.py``;
2024-11-01T17:51:30.0663452Z             parameters are packed into buckets matching those in
2024-11-01T17:51:30.0663699Z             :class:`DistributedDataParallel`, meaning that the
2024-11-01T17:51:30.0663911Z             ``parameters_as_bucket_view`` argument is ignored.
2024-11-01T17:51:30.0664209Z             If ``False``, :meth:`step` runs disjointly after the backward pass
2024-11-01T17:51:30.0664325Z             (per normal).
2024-11-01T17:51:30.0664466Z             (default: ``False``)
2024-11-01T17:51:30.0664775Z         **defaults: any trailing arguments, which are forwarded to the local
2024-11-01T17:51:30.0664887Z             optimizer.
2024-11-01T17:51:30.0664997Z     
2024-11-01T17:51:30.0665117Z     Example::
2024-11-01T17:51:30.0665227Z     
2024-11-01T17:51:30.0665351Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0665482Z         >>> import torch.nn as nn
2024-11-01T17:51:30.0665800Z         >>> from torch.distributed.optim import ZeroRedundancyOptimizer
2024-11-01T17:51:30.0666093Z         >>> from torch.nn.parallel import DistributedDataParallel as DDP
2024-11-01T17:51:30.0666445Z         >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
2024-11-01T17:51:30.0666610Z         >>> ddp = DDP(model, device_ids=[rank])
2024-11-01T17:51:30.0666795Z         >>> opt = ZeroRedundancyOptimizer(
2024-11-01T17:51:30.0666923Z         >>>     ddp.parameters(),
2024-11-01T17:51:30.0667096Z         >>>     optimizer_class=torch.optim.Adam,
2024-11-01T17:51:30.0667219Z         >>>     lr=0.01
2024-11-01T17:51:30.0667319Z         >>> )
2024-11-01T17:51:30.0667475Z         >>> ddp(inputs).sum().backward()
2024-11-01T17:51:30.0667588Z         >>> opt.step()
2024-11-01T17:51:30.0667695Z     
2024-11-01T17:51:30.0667804Z     .. warning::
2024-11-01T17:51:30.0668098Z         Currently, ``ZeroRedundancyOptimizer`` requires that all of the
2024-11-01T17:51:30.0668384Z         passed-in parameters are the same dense type.
2024-11-01T17:51:30.0668480Z     
2024-11-01T17:51:30.0668601Z     .. warning::
2024-11-01T17:51:30.0668913Z         If you pass ``overlap_with_ddp=True``, be wary of the following: Given
2024-11-01T17:51:30.0669205Z         the way that overlapping :class:`DistributedDataParallel` with
2024-11-01T17:51:30.0669540Z         :class:`ZeroRedundancyOptimizer` is currently implemented, the first
2024-11-01T17:51:30.0669898Z         two or three training iterations do not perform parameter updates in
2024-11-01T17:51:30.0670183Z         the optimizer step, depending on if ``static_graph=False`` or
2024-11-01T17:51:30.0670438Z         ``static_graph=True``, respectively. This is because it needs
2024-11-01T17:51:30.0670706Z         information about the gradient bucketing strategy used by
2024-11-01T17:51:30.0671013Z         :class:`DistributedDataParallel`, which is not finalized until the
2024-11-01T17:51:30.0671317Z         second forward pass if ``static_graph=False`` or until the third
2024-11-01T17:51:30.0671627Z         forward pass if ``static_graph=True``. To adjust for this, one option
2024-11-01T17:51:30.0671763Z         is to prepend dummy inputs.
2024-11-01T17:51:30.0671870Z     
2024-11-01T17:51:30.0672218Z     .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
2024-11-01T17:51:30.0672331Z     
2024-11-01T17:51:30.0672507Z     .. _ZeRO: https://arxiv.org/abs/1910.02054
2024-11-01T17:51:30.0672605Z     
2024-11-01T17:51:30.0672710Z     
2024-11-01T17:51:30.0673143Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0673250Z     
2024-11-01T17:51:30.0673369Z       warnings.warn(msg)
2024-11-01T17:51:30.0673478Z     
2024-11-01T17:51:30.0673709Z --- Parse Warning: 64 / 103 ---
2024-11-01T17:51:30.0675537Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/pipelining/microbatch.py line=28.
2024-11-01T17:51:30.0676029Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0676126Z     
2024-11-01T17:51:30.0676476Z     Custom reducer class that can be used to specify a custom operation that
2024-11-01T17:51:30.0676711Z     reduces losses of multiple microbatches into one value.
2024-11-01T17:51:30.0676824Z     
2024-11-01T17:51:30.0676931Z     Example:
2024-11-01T17:51:30.0677055Z     >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0677215Z     >>> sum_reducer = _CustomReducer(
2024-11-01T17:51:30.0677339Z     >>>     torch.tensor(0.0),
2024-11-01T17:51:30.0677475Z     >>>     lambda a, b: a + b
2024-11-01T17:51:30.0677574Z     >>> )
2024-11-01T17:51:30.0677682Z     
2024-11-01T17:51:30.0678111Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0678209Z     
2024-11-01T17:51:30.0678345Z       warnings.warn(msg)
2024-11-01T17:51:30.0678439Z     
2024-11-01T17:51:30.0678685Z --- Parse Warning: 65 / 103 ---
2024-11-01T17:51:30.0680207Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/functions.py line=6.
2024-11-01T17:51:30.0680670Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0680768Z     
2024-11-01T17:51:30.0681112Z     A decorator for a function indicating that the return value of the function
2024-11-01T17:51:30.0681434Z     is guaranteed to be a :class:`~torch.futures.Future` object and this
2024-11-01T17:51:30.0681776Z     function can run asynchronously on the RPC callee. More specifically, the
2024-11-01T17:51:30.0682129Z     callee extracts the :class:`~torch.futures.Future` returned by the wrapped
2024-11-01T17:51:30.0682462Z     function and installs subsequent processing steps as a callback to that
2024-11-01T17:51:30.0682823Z     :class:`~torch.futures.Future`. The installed callback will read the value
2024-11-01T17:51:30.0683123Z     from the :class:`~torch.futures.Future` when completed and send the
2024-11-01T17:51:30.0683387Z     value back as the RPC response. That also means the returned
2024-11-01T17:51:30.0683735Z     :class:`~torch.futures.Future` only exists on the callee side and is never
2024-11-01T17:51:30.0684145Z     sent through RPC. This decorator is useful when the wrapped function's
2024-11-01T17:51:30.0684443Z     (``fn``) execution needs to pause and resume due to, e.g., containing
2024-11-01T17:51:30.0684768Z     :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
2024-11-01T17:51:30.0684883Z     
2024-11-01T17:51:30.0685191Z     .. note:: To enable asynchronous execution, applications must pass the
2024-11-01T17:51:30.0685542Z         function object returned by this decorator to RPC APIs. If RPC detected
2024-11-01T17:51:30.0685868Z         attributes installed by this decorator, it knows that this function
2024-11-01T17:51:30.0686133Z         returns a ``Future`` object and will handle that accordingly.
2024-11-01T17:51:30.0686460Z         However, this does not mean this decorator has to be outmost one when
2024-11-01T17:51:30.0686780Z         defining a function. For example, when combined with ``@staticmethod``
2024-11-01T17:51:30.0687104Z         or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
2024-11-01T17:51:30.0687426Z         inner decorator to allow the target function be recognized as a static
2024-11-01T17:51:30.0687774Z         or class function. This target function can still execute asynchronously
2024-11-01T17:51:30.0688100Z         because, when accessed, the static or class method preserves attributes
2024-11-01T17:51:30.0688313Z         installed by ``@rpc.functions.async_execution``.
2024-11-01T17:51:30.0688460Z     
2024-11-01T17:51:30.0688553Z     
2024-11-01T17:51:30.0688674Z     Example::
2024-11-01T17:51:30.0689017Z         The returned :class:`~torch.futures.Future` object can come from
2024-11-01T17:51:30.0689219Z         :meth:`~torch.distributed.rpc.rpc_async`,
2024-11-01T17:51:30.0689540Z         :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
2024-11-01T17:51:30.0689787Z         constructor. The example below shows directly using the
2024-11-01T17:51:30.0689981Z         :class:`~torch.futures.Future` returned by
2024-11-01T17:51:30.0690145Z         :meth:`~torch.futures.Future.then`.
2024-11-01T17:51:30.0690254Z     
2024-11-01T17:51:30.0690420Z         >>> from torch.distributed import rpc
2024-11-01T17:51:30.0690534Z         >>>
2024-11-01T17:51:30.0690695Z         >>> # omitting setup and shutdown RPC
2024-11-01T17:51:30.0690795Z         >>>
2024-11-01T17:51:30.0690927Z         >>> # On all workers
2024-11-01T17:51:30.0691079Z         >>> @rpc.functions.async_execution
2024-11-01T17:51:30.0691257Z         >>> def async_add_chained(to, x, y, z):
2024-11-01T17:51:30.0691542Z         >>>     # This function runs on "worker1" and returns immediately when
2024-11-01T17:51:30.0691818Z         >>>     # the callback is installed through the `then(cb)` API. In the
2024-11-01T17:51:30.0692107Z         >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
2024-11-01T17:51:30.0692347Z         >>>     # When the return value of that `rpc_async` arrives at
2024-11-01T17:51:30.0692630Z         >>>     # "worker1", "worker1" will run the lambda function accordingly
2024-11-01T17:51:30.0692914Z         >>>     # and set the value for the previously returned `Future`, which
2024-11-01T17:51:30.0693203Z         >>>     # will then trigger RPC to send the result back to "worker0".
2024-11-01T17:51:30.0693448Z         >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:30.0693685Z         >>>         lambda fut: fut.wait() + z
2024-11-01T17:51:30.0693791Z         >>>     )
2024-11-01T17:51:30.0693891Z         >>>
2024-11-01T17:51:30.0694052Z         >>> # On worker0
2024-11-01T17:51:30.0694180Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0694316Z         >>> ret = rpc.rpc_sync(
2024-11-01T17:51:30.0694429Z         >>>     "worker1",
2024-11-01T17:51:30.0694552Z         >>>     async_add_chained,
2024-11-01T17:51:30.0694738Z         >>>     args=("worker2", torch.ones(2), 1, 1)
2024-11-01T17:51:30.0694840Z         >>> )
2024-11-01T17:51:30.0695064Z         >>> print(ret)  # prints tensor([3., 3.])
2024-11-01T17:51:30.0695159Z     
2024-11-01T17:51:30.0695497Z         When combined with TorchScript decorators, this decorator must be the
2024-11-01T17:51:30.0695610Z         outmost one.
2024-11-01T17:51:30.0695708Z     
2024-11-01T17:51:30.0695853Z         >>> from torch import Tensor
2024-11-01T17:51:30.0696015Z         >>> from torch.futures import Future
2024-11-01T17:51:30.0696194Z         >>> from torch.distributed import rpc
2024-11-01T17:51:30.0696295Z         >>>
2024-11-01T17:51:30.0696457Z         >>> # omitting setup and shutdown RPC
2024-11-01T17:51:30.0696569Z         >>>
2024-11-01T17:51:30.0696690Z         >>> # On all workers
2024-11-01T17:51:30.0696828Z         >>> @torch.jit.script
2024-11-01T17:51:30.0697116Z         >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
2024-11-01T17:51:30.0697248Z         >>>     return x + y
2024-11-01T17:51:30.0697347Z         >>>
2024-11-01T17:51:30.0697500Z         >>> @rpc.functions.async_execution
2024-11-01T17:51:30.0697638Z         >>> @torch.jit.script
2024-11-01T17:51:30.0697993Z         >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
2024-11-01T17:51:30.0698207Z         >>>     return rpc.rpc_async(to, script_add, (x, y))
2024-11-01T17:51:30.0698307Z         >>>
2024-11-01T17:51:30.0698430Z         >>> # On worker0
2024-11-01T17:51:30.0698553Z         >>> ret = rpc.rpc_sync(
2024-11-01T17:51:30.0698666Z         >>>     "worker1",
2024-11-01T17:51:30.0698789Z         >>>     async_add,
2024-11-01T17:51:30.0698994Z         >>>     args=("worker2", torch.ones(2), 1)
2024-11-01T17:51:30.0699106Z         >>> )
2024-11-01T17:51:30.0699322Z         >>> print(ret)  # prints tensor([2., 2.])
2024-11-01T17:51:30.0699419Z     
2024-11-01T17:51:30.0699751Z         When combined with static or class method, this decorator must be the
2024-11-01T17:51:30.0699863Z         inner one.
2024-11-01T17:51:30.0699971Z     
2024-11-01T17:51:30.0700138Z         >>> from torch.distributed import rpc
2024-11-01T17:51:30.0700254Z         >>>
2024-11-01T17:51:30.0700416Z         >>> # omitting setup and shutdown RPC
2024-11-01T17:51:30.0700515Z         >>>
2024-11-01T17:51:30.0700645Z         >>> # On all workers
2024-11-01T17:51:30.0700794Z         >>> class AsyncExecutionClass:
2024-11-01T17:51:30.0700908Z         >>>
2024-11-01T17:51:30.0701026Z         >>>     @staticmethod
2024-11-01T17:51:30.0701195Z         >>>     @rpc.functions.async_execution
2024-11-01T17:51:30.0701376Z         >>>     def static_async_add(to, x, y, z):
2024-11-01T17:51:30.0701627Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:30.0701806Z         >>>             lambda fut: fut.wait() + z
2024-11-01T17:51:30.0701913Z         >>>         )
2024-11-01T17:51:30.0702027Z         >>>
2024-11-01T17:51:30.0702142Z         >>>     @classmethod
2024-11-01T17:51:30.0702305Z         >>>     @rpc.functions.async_execution
2024-11-01T17:51:30.0702493Z         >>>     def class_async_add(cls, to, x, y, z):
2024-11-01T17:51:30.0702674Z         >>>         ret_fut = torch.futures.Future()
2024-11-01T17:51:30.0702906Z         >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:30.0703126Z         >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
2024-11-01T17:51:30.0703245Z         >>>         )
2024-11-01T17:51:30.0703366Z         >>>         return ret_fut
2024-11-01T17:51:30.0703466Z         >>>
2024-11-01T17:51:30.0703639Z         >>>     @rpc.functions.async_execution
2024-11-01T17:51:30.0703821Z         >>>     def bound_async_add(self, to, x, y, z):
2024-11-01T17:51:30.0704084Z         >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
2024-11-01T17:51:30.0704245Z         >>>             lambda fut: fut.wait() + z
2024-11-01T17:51:30.0704353Z         >>>         )
2024-11-01T17:51:30.0704467Z         >>>
2024-11-01T17:51:30.0704579Z         >>> # On worker0
2024-11-01T17:51:30.0704716Z         >>> ret = rpc.rpc_sync(
2024-11-01T17:51:30.0704864Z         >>>     "worker1",
2024-11-01T17:51:30.0705075Z         >>>     AsyncExecutionClass.static_async_add,
2024-11-01T17:51:30.0705243Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2024-11-01T17:51:30.0705343Z         >>> )
2024-11-01T17:51:30.0705524Z         >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:30.0705627Z         >>>
2024-11-01T17:51:30.0705765Z         >>> ret = rpc.rpc_sync(
2024-11-01T17:51:30.0705877Z         >>>     "worker1",
2024-11-01T17:51:30.0706084Z         >>>     AsyncExecutionClass.class_async_add,
2024-11-01T17:51:30.0706253Z         >>>     args=("worker2", torch.ones(2), 1, 2)
2024-11-01T17:51:30.0706357Z         >>> )
2024-11-01T17:51:30.0706784Z         >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:30.0706880Z     
2024-11-01T17:51:30.0707125Z         This decorator also works with RRef helpers, i.e., .
2024-11-01T17:51:30.0707329Z         :meth:`torch.distributed.rpc.RRef.rpc_sync`,
2024-11-01T17:51:30.0707553Z         :meth:`torch.distributed.rpc.RRef.rpc_async`, and
2024-11-01T17:51:30.0707768Z         :meth:`torch.distributed.rpc.RRef.remote`.
2024-11-01T17:51:30.0707866Z     
2024-11-01T17:51:30.0708045Z         >>> from torch.distributed import rpc
2024-11-01T17:51:30.0708145Z         >>>
2024-11-01T17:51:30.0708361Z         >>> # reuse the AsyncExecutionClass class above
2024-11-01T17:51:30.0708585Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2024-11-01T17:51:30.0708893Z         >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
2024-11-01T17:51:30.0709221Z         >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:30.0709399Z         >>>
2024-11-01T17:51:30.0709633Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2024-11-01T17:51:30.0709972Z         >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
2024-11-01T17:51:30.0710153Z         >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:30.0710254Z         >>>
2024-11-01T17:51:30.0710476Z         >>> rref = rpc.remote("worker1", AsyncExecutionClass)
2024-11-01T17:51:30.0710834Z         >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
2024-11-01T17:51:30.0711001Z         >>> print(ret)  # prints tensor([4., 4.])
2024-11-01T17:51:30.0711114Z     
2024-11-01T17:51:30.0711577Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0711687Z     
2024-11-01T17:51:30.0711808Z       warnings.warn(msg)
2024-11-01T17:51:30.0711906Z     
2024-11-01T17:51:30.0712155Z --- Parse Warning: 66 / 103 ---
2024-11-01T17:51:30.0713986Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/rpc/options.py line=108.
2024-11-01T17:51:30.0714457Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0714558Z     
2024-11-01T17:51:30.0714868Z     Set device mapping between each RPC caller and callee pair. This
2024-11-01T17:51:30.0715123Z     function can be called multiple times to incrementally add
2024-11-01T17:51:30.0715270Z     device placement configurations.
2024-11-01T17:51:30.0715382Z     
2024-11-01T17:51:30.0715484Z     Args:
2024-11-01T17:51:30.0715624Z         to (str): Callee name.
2024-11-01T17:51:30.0715909Z         device_map (Dict of int, str, or torch.device): Device placement
2024-11-01T17:51:30.0716184Z             mappings from this worker to the callee. This map must be
2024-11-01T17:51:30.0716300Z             invertible.
2024-11-01T17:51:30.0716396Z     
2024-11-01T17:51:30.0716515Z     Example:
2024-11-01T17:51:30.0716675Z         >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0716803Z         >>> # both workers
2024-11-01T17:51:30.0716920Z         >>> def add(x, y):
2024-11-01T17:51:30.0717189Z         >>>     print(x)  # tensor([1., 1.], device='cuda:1')
2024-11-01T17:51:30.0717419Z         >>>     return x + y, (x + y).to(2)
2024-11-01T17:51:30.0717519Z         >>>
2024-11-01T17:51:30.0717648Z         >>> # on worker 0
2024-11-01T17:51:30.0717845Z         >>> options = TensorPipeRpcBackendOptions(
2024-11-01T17:51:30.0717993Z         >>>     num_worker_threads=8,
2024-11-01T17:51:30.0718150Z         >>>     device_maps={"worker1": {0: 1}}
2024-11-01T17:51:30.0718414Z         >>>     # maps worker0's cuda:0 to worker1's cuda:1
2024-11-01T17:51:30.0718532Z         >>> )
2024-11-01T17:51:30.0718716Z         >>> options.set_device_map("worker1", {1: 2})
2024-11-01T17:51:30.0718985Z         >>> # maps worker0's cuda:1 to worker1's cuda:2
2024-11-01T17:51:30.0719088Z         >>>
2024-11-01T17:51:30.0719267Z         >>> rpc.init_rpc(
2024-11-01T17:51:30.0719441Z         >>>     "worker0",
2024-11-01T17:51:30.0719547Z         >>>     rank=0,
2024-11-01T17:51:30.0719723Z         >>>     world_size=2,
2024-11-01T17:51:30.0719950Z         >>>     backend=rpc.BackendType.TENSORPIPE,
2024-11-01T17:51:30.0720150Z         >>>     rpc_backend_options=options
2024-11-01T17:51:30.0720252Z         >>> )
2024-11-01T17:51:30.0720353Z         >>>
2024-11-01T17:51:30.0720488Z         >>> x = torch.ones(2)
2024-11-01T17:51:30.0720722Z         >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
2024-11-01T17:51:30.0721008Z         >>> # The first argument will be moved to cuda:1 on worker1. When
2024-11-01T17:51:30.0721276Z         >>> # sending the return value back, it will follow the invert of
2024-11-01T17:51:30.0721643Z         >>> # the device map, and hence will be moved back to cuda:0 and
2024-11-01T17:51:30.0721819Z         >>> # cuda:1 on worker0
2024-11-01T17:51:30.0722110Z         >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
2024-11-01T17:51:30.0722402Z         >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
2024-11-01T17:51:30.0722498Z     
2024-11-01T17:51:30.0722944Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0723042Z     
2024-11-01T17:51:30.0723179Z       warnings.warn(msg)
2024-11-01T17:51:30.0723274Z     
2024-11-01T17:51:30.0723504Z --- Parse Warning: 67 / 103 ---
2024-11-01T17:51:30.0725134Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_func_map.py line=32.
2024-11-01T17:51:30.0725580Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0725693Z     
2024-11-01T17:51:30.0726083Z     :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s
2024-11-01T17:51:30.0726529Z     to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting
2024-11-01T17:51:30.0726918Z     the local components of :class:`DTensor`, call the function, and wrap the outputs to
2024-11-01T17:51:30.0727133Z     :class:`DTensor` according to the ``out_placements``.
2024-11-01T17:51:30.0727244Z     
2024-11-01T17:51:30.0727346Z     Args:
2024-11-01T17:51:30.0727654Z         func (Callable): the function to be applied on each local shard of
2024-11-01T17:51:30.0727778Z             :class:`DTensor` s.
2024-11-01T17:51:30.0728109Z         out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]):
2024-11-01T17:51:30.0728579Z             the desired placements of the :class:`DTensor` s in ``func``'s flattened output.
2024-11-01T17:51:30.0728938Z             If the flattened ``output`` is a single value, the ``out_placements`` should be
2024-11-01T17:51:30.0729307Z             of type `PlacementType`. Otherwise if the flattened ``output`` has multiple
2024-11-01T17:51:30.0729660Z             values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1
2024-11-01T17:51:30.0729843Z             mapping to the flattened ``output``.
2024-11-01T17:51:30.0730138Z             Besides, for :class:`Tensor` output, we use `PlacementType` as its
2024-11-01T17:51:30.0730686Z             placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType`
2024-11-01T17:51:30.0730808Z             should be `None`.
2024-11-01T17:51:30.0731174Z             Note that the only exception is when no :class:`DTensor` argument is passed
2024-11-01T17:51:30.0731524Z             in. In this case, even if `out_placements` is not `None`, the result function
2024-11-01T17:51:30.0731891Z             should ignore the desired placements because the function is not running with
2024-11-01T17:51:30.0732032Z             :class:`DTensor` s.
2024-11-01T17:51:30.0732268Z         in_placements (Tuple[`PlacementType`, ...], optional):
2024-11-01T17:51:30.0732692Z             the required placements of the :class:`DTensor` s in the flattened inputs of ``func``.
2024-11-01T17:51:30.0733036Z             If ``in_placements`` is specified, :meth:`local_map` would examine whether the
2024-11-01T17:51:30.0733381Z             placements of each :class:`DTensor` argument is the same as the required
2024-11-01T17:51:30.0733643Z             placements or not. If the placements are not the same and
2024-11-01T17:51:30.0734007Z             ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if
2024-11-01T17:51:30.0734360Z             ``redistribute_inputs`` is ``True``, the argument will be first redistributed to
2024-11-01T17:51:30.0734727Z             the required sharding placements before passing its local tensor to ``func``.
2024-11-01T17:51:30.0735123Z             The only exception is when required placements are not ``None`` and the
2024-11-01T17:51:30.0735533Z             argument is a :class:`torch.Tensor`. In this case, the placements examination
2024-11-01T17:51:30.0735867Z             will be skipped and the argument will be directly passed to ``func``.
2024-11-01T17:51:30.0736205Z             If ``in_placements`` is ``None``, no placements examination will be performed.
2024-11-01T17:51:30.0736336Z             Default: None
2024-11-01T17:51:30.0736526Z         device_mesh (:class:`DeviceMesh`, optional):
2024-11-01T17:51:30.0736844Z             the device mesh that all the :class:`DTensor` s are placed on. If not
2024-11-01T17:51:30.0737295Z             specified, this will be inferred from the input :class:`DTensor` s' device
2024-11-01T17:51:30.0737642Z             mesh. `local_map` requires every :class:`DTensor` s to be placed on the same
2024-11-01T17:51:30.0737795Z             device mesh. Default: None.
2024-11-01T17:51:30.0737964Z         redistribute_inputs (bool, optional):
2024-11-01T17:51:30.0738386Z             the bool value indicating whether to reshard the input :class:`DTensor` s when
2024-11-01T17:51:30.0738737Z             their placements are different from the required input placements. If this
2024-11-01T17:51:30.0739094Z             value is ``False`` and some :class:`DTensor` input has a different placement,
2024-11-01T17:51:30.0739289Z             an exception will be raised. Default: False.
2024-11-01T17:51:30.0739387Z     
2024-11-01T17:51:30.0739504Z     Returns:
2024-11-01T17:51:30.0739887Z         A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor`
2024-11-01T17:51:30.0740250Z         and returns a :class:`DTensor` constructed from the return value of ``func``.
2024-11-01T17:51:30.0740346Z     
2024-11-01T17:51:30.0740460Z     Raises:
2024-11-01T17:51:30.0740819Z         AssertionError: If the input :class:`DTensor` is not placed on the same device
2024-11-01T17:51:30.0741194Z             mesh, or if they are placed on a different device mesh than the ``device_mesh``
2024-11-01T17:51:30.0741339Z             argument passed in.
2024-11-01T17:51:30.0741434Z     
2024-11-01T17:51:30.0741871Z         AssertionError: For any non-DTensor output, we require its corresponding
2024-11-01T17:51:30.0742245Z             output placement in ``out_placements`` be None. An AssertionError will be raised
2024-11-01T17:51:30.0742391Z             if this is not the case.
2024-11-01T17:51:30.0742521Z     
2024-11-01T17:51:30.0742889Z         ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs
2024-11-01T17:51:30.0743117Z             a redistribution according to ``in_placements``.
2024-11-01T17:51:30.0743215Z     
2024-11-01T17:51:30.0743333Z     Example:
2024-11-01T17:51:30.0743491Z         >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0743683Z         >>> def mm_allreduce_forward(device_mesh, W, X):
2024-11-01T17:51:30.0743868Z         >>>     partial_sum_tensor = torch.mm(W, X)
2024-11-01T17:51:30.0744212Z         >>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
2024-11-01T17:51:30.0744360Z         >>>     return reduced_tensor
2024-11-01T17:51:30.0744462Z         >>>
2024-11-01T17:51:30.0744660Z         >>> W = torch.randn(12, 8, requires_grad=False)
2024-11-01T17:51:30.0744840Z         >>> X = torch.randn(8, 16, requires_grad=False)
2024-11-01T17:51:30.0744964Z         >>> Y = torch.mm(W, X)
2024-11-01T17:51:30.0745342Z         >>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
2024-11-01T17:51:30.0745698Z         >>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
2024-11-01T17:51:30.0745812Z         >>>
2024-11-01T17:51:30.0746200Z         >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
2024-11-01T17:51:30.0746389Z         >>> local_mm_allreduce_forward = local_map(
2024-11-01T17:51:30.0746523Z         >>>     mm_allreduce_forward,
2024-11-01T17:51:30.0746716Z         >>>     out_placements=[Replicate()],
2024-11-01T17:51:30.0746948Z         >>>     in_placements=[col_wise, row_wise],
2024-11-01T17:51:30.0747087Z         >>>     device_mesh=device_mesh,
2024-11-01T17:51:30.0747239Z         >>> )
2024-11-01T17:51:30.0747338Z         >>>
2024-11-01T17:51:30.0747824Z         >>> W_dt = distribute_tensor(W, device_mesh, (col_wise))  # col-wisely sharded W tensor
2024-11-01T17:51:30.0748291Z         >>> X_dt = distribute_tensor(X, device_mesh, (row_wise))  # row-wisely sharded X tensor
2024-11-01T17:51:30.0748784Z         >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt)  # apply local_mm_allreduce_forward to DTensors
2024-11-01T17:51:30.0748897Z     
2024-11-01T17:51:30.0749204Z     .. note:: This API is currently experimental and subject to change
2024-11-01T17:51:30.0749314Z     
2024-11-01T17:51:30.0749745Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0749853Z     
2024-11-01T17:51:30.0749977Z       warnings.warn(msg)
2024-11-01T17:51:30.0750073Z     
2024-11-01T17:51:30.0750321Z --- Parse Warning: 68 / 103 ---
2024-11-01T17:51:30.0752054Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25.
2024-11-01T17:51:30.0752510Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0752609Z     
2024-11-01T17:51:30.0753019Z     :meth:`register_sharding` is an experimental API that allows users to register sharding
2024-11-01T17:51:30.0753362Z     strategies for an operator when the tensor inputs and outputs are DTensor.
2024-11-01T17:51:30.0753977Z     It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``,
2024-11-01T17:51:30.0754365Z     e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2)
2024-11-01T17:51:30.0754770Z     when users would like to overwrite default sharding strategies of existing operators.
2024-11-01T17:51:30.0754878Z     
2024-11-01T17:51:30.0754979Z     Args:
2024-11-01T17:51:30.0755175Z         op (Union[OpOverload, List[OpOverload]]):
2024-11-01T17:51:30.0755485Z             An op or a list of ops to register the customized sharding function.
2024-11-01T17:51:30.0755596Z     
2024-11-01T17:51:30.0755701Z     Returns:
2024-11-01T17:51:30.0756165Z         A function decorator which can be used to wrap a function that defines the sharding
2024-11-01T17:51:30.0756576Z         strategy for the operator specified in ``op``. The defined sharding strategy will be
2024-11-01T17:51:30.0756977Z         registered to DTensor and will override the default sharding strategy if DTensor has
2024-11-01T17:51:30.0757417Z         already implemented the operator. The customized sharding function takes the same inputs
2024-11-01T17:51:30.0757790Z         as the original op (except that if an arg is a :class:`torch.Tensor`, it will be
2024-11-01T17:51:30.0758301Z         replaced by a tensor-like object that DTensor uses internally). The function should
2024-11-01T17:51:30.0758783Z         return a sequence of 2-tuples, each specifying acceptable output placements and its
2024-11-01T17:51:30.0758939Z         corresponding intput placements.
2024-11-01T17:51:30.0759047Z     
2024-11-01T17:51:30.0759153Z     Example:
2024-11-01T17:51:30.0759333Z         >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0759525Z         >>> @register_sharding(aten._softmax.default)
2024-11-01T17:51:30.0759759Z         >>> def custom_softmax_sharding(x, dim, half_to_float):
2024-11-01T17:51:30.0759966Z         >>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
2024-11-01T17:51:30.0760114Z         >>>     acceptable_shardings = []
2024-11-01T17:51:30.0760227Z         >>>
2024-11-01T17:51:30.0760483Z         >>>     all_replicate = ([Replicate()], [Replicate(), None, None])
2024-11-01T17:51:30.0760737Z         >>>     acceptable_shardings.append(all_replicate)
2024-11-01T17:51:30.0760886Z         >>>
2024-11-01T17:51:30.0761067Z         >>>     for sharding_dim in range(x.ndim):
2024-11-01T17:51:30.0761233Z         >>>         if sharding_dim != softmax_dim:
2024-11-01T17:51:30.0761362Z         >>>             all_sharded = (
2024-11-01T17:51:30.0761532Z         >>>                 [Shard(sharding_dim)],
2024-11-01T17:51:30.0761716Z         >>>                 [Shard(sharding_dim), None, None],
2024-11-01T17:51:30.0761843Z         >>>             )
2024-11-01T17:51:30.0762051Z         >>>             acceptable_shardings.append(all_sharded)
2024-11-01T17:51:30.0762153Z         >>>
2024-11-01T17:51:30.0762317Z         >>>     return acceptable_shardings
2024-11-01T17:51:30.0762413Z     
2024-11-01T17:51:30.0762717Z     .. note:: This API is currently experimental and subject to change
2024-11-01T17:51:30.0762812Z     
2024-11-01T17:51:30.0763258Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0763357Z     
2024-11-01T17:51:30.0763484Z       warnings.warn(msg)
2024-11-01T17:51:30.0763591Z     
2024-11-01T17:51:30.0763820Z --- Parse Warning: 69 / 103 ---
2024-11-01T17:51:30.0765466Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=378.
2024-11-01T17:51:30.0765914Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0766024Z     
2024-11-01T17:51:30.0766703Z     Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
2024-11-01T17:51:30.0767166Z     ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``.
2024-11-01T17:51:30.0767260Z     
2024-11-01T17:51:30.0767372Z     Keyword Args:
2024-11-01T17:51:30.0767657Z         input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2024-11-01T17:51:30.0768154Z             The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
2024-11-01T17:51:30.0768712Z             DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified
2024-11-01T17:51:30.0768873Z             as a placeholder. default: None.
2024-11-01T17:51:30.0769239Z         desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]):
2024-11-01T17:51:30.0769813Z             The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
2024-11-01T17:51:30.0770398Z             have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None.
2024-11-01T17:51:30.0770596Z         input_kwarg_layouts (Dict[str, Placement]):
2024-11-01T17:51:30.0771165Z             The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors.
2024-11-01T17:51:30.0771304Z             default: None
2024-11-01T17:51:30.0771524Z         desired_input_kwarg_layouts: (Dict[str, Placement]):
2024-11-01T17:51:30.0772095Z             The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module
2024-11-01T17:51:30.0772311Z             have the desired DTensor layouts. default: None.
2024-11-01T17:51:30.0772482Z         use_local_output (bool, optional):
2024-11-01T17:51:30.0773016Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False.
2024-11-01T17:51:30.0773122Z     Returns:
2024-11-01T17:51:30.0773688Z         A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs.
2024-11-01T17:51:30.0773785Z     
2024-11-01T17:51:30.0773908Z     Example::
2024-11-01T17:51:30.0774047Z         >>> # xdoctest: +SKIP(failing)
2024-11-01T17:51:30.0774600Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput
2024-11-01T17:51:30.0774877Z         >>> from torch.distributed.device_mesh import init_device_mesh
2024-11-01T17:51:30.0774981Z         >>> ...
2024-11-01T17:51:30.0775447Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2024-11-01T17:51:30.0775624Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2024-11-01T17:51:30.0775771Z         >>>
2024-11-01T17:51:30.0776269Z         >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor
2024-11-01T17:51:30.0776493Z         >>> # and then redistributed to Replicated DTensor.
2024-11-01T17:51:30.0776620Z         >>> parallelize_module(
2024-11-01T17:51:30.0776819Z         >>>     block, # this can be a submodule or module
2024-11-01T17:51:30.0776948Z         >>>     tp_mesh,
2024-11-01T17:51:30.0777081Z         >>>     parallelize_plan={
2024-11-01T17:51:30.0777264Z         >>>         "attn": PrepareModuleInput(
2024-11-01T17:51:30.0777473Z         >>>             input_layouts=(Shard(0), None, None, ...),
2024-11-01T17:51:30.0777729Z         >>>             desired_input_layouts=(Replicate(), None, None, ...)
2024-11-01T17:51:30.0777836Z         >>>         ),
2024-11-01T17:51:30.0777995Z         >>>     }
2024-11-01T17:51:30.0778107Z         >>> )
2024-11-01T17:51:30.0778201Z     
2024-11-01T17:51:30.0778653Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0778749Z     
2024-11-01T17:51:30.0778868Z       warnings.warn(msg)
2024-11-01T17:51:30.0778978Z     
2024-11-01T17:51:30.0779206Z --- Parse Warning: 70 / 103 ---
2024-11-01T17:51:30.0780859Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributed/tensor/parallel/style.py line=533.
2024-11-01T17:51:30.0781310Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0781418Z     
2024-11-01T17:51:30.0782113Z     Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
2024-11-01T17:51:30.0782586Z     ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``.
2024-11-01T17:51:30.0782721Z     
2024-11-01T17:51:30.0782832Z     Keyword Args:
2024-11-01T17:51:30.0783074Z         output_layouts (Union[Placement, Tuple[Placement]]):
2024-11-01T17:51:30.0783578Z             The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
2024-11-01T17:51:30.0784163Z             DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors,
2024-11-01T17:51:30.0784369Z             ``None`` need to be specified as a placeholder.
2024-11-01T17:51:30.0784651Z         desired_output_layouts (Union[Placement, Tuple[Placement]]):
2024-11-01T17:51:30.0785243Z             The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
2024-11-01T17:51:30.0785418Z             have the desired DTensor layouts.
2024-11-01T17:51:30.0785594Z         use_local_output (bool, optional):
2024-11-01T17:51:30.0786127Z             Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True.
2024-11-01T17:51:30.0786247Z     Returns:
2024-11-01T17:51:30.0786757Z         A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs.
2024-11-01T17:51:30.0786868Z     
2024-11-01T17:51:30.0786977Z     Example::
2024-11-01T17:51:30.0787117Z         >>> # xdoctest: +SKIP(failing)
2024-11-01T17:51:30.0787573Z         >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput
2024-11-01T17:51:30.0787883Z         >>> from torch.distributed.device_mesh import init_device_mesh
2024-11-01T17:51:30.0788047Z         >>> ...
2024-11-01T17:51:30.0788499Z         >>> block = TransformerBlock(...)  # block is a nn.Module that contains an "attn" Attention submodule
2024-11-01T17:51:30.0788692Z         >>> tp_mesh = init_device_mesh("cuda", (8,))
2024-11-01T17:51:30.0788795Z         >>>
2024-11-01T17:51:30.0789399Z         >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor
2024-11-01T17:51:30.0789598Z         >>> # and then redistributed to Sharded DTensor.
2024-11-01T17:51:30.0789728Z         >>> parallelize_module(
2024-11-01T17:51:30.0789939Z         >>>     block, # this can be a submodule or module
2024-11-01T17:51:30.0790053Z         >>>     tp_mesh,
2024-11-01T17:51:30.0790269Z         >>>     parallelize_plan = PrepareModuleOutput(
2024-11-01T17:51:30.0790429Z         >>>         output_layouts=Replicate(),
2024-11-01T17:51:30.0790715Z         >>>         desired_output_layouts=Shard(0)
2024-11-01T17:51:30.0790819Z         >>>     )
2024-11-01T17:51:30.0790924Z         >>> )
2024-11-01T17:51:30.0791055Z     
2024-11-01T17:51:30.0791490Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0791597Z     
2024-11-01T17:51:30.0791717Z       warnings.warn(msg)
2024-11-01T17:51:30.0791813Z     
2024-11-01T17:51:30.0792057Z --- Parse Warning: 71 / 103 ---
2024-11-01T17:51:30.0793673Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/mixture_same_family.py line=13.
2024-11-01T17:51:30.0794263Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0794362Z     
2024-11-01T17:51:30.0794687Z     The `MixtureSameFamily` distribution implements a (batch of) mixture
2024-11-01T17:51:30.0795032Z     distribution where all component are from different parameterizations of
2024-11-01T17:51:30.0795344Z     the same distribution type. It is parameterized by a `Categorical`
2024-11-01T17:51:30.0795624Z     "selecting distribution" (over `k` component) and a component
2024-11-01T17:51:30.0795921Z     distribution, i.e., a `Distribution` with a rightmost batch shape
2024-11-01T17:51:30.0796169Z     (equal to `[k]`) which indexes each (batch of) component.
2024-11-01T17:51:30.0796312Z     
2024-11-01T17:51:30.0796439Z     Examples::
2024-11-01T17:51:30.0796535Z     
2024-11-01T17:51:30.0796726Z         >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.0797014Z         >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
2024-11-01T17:51:30.0797168Z         >>> # weighted normal distributions
2024-11-01T17:51:30.0797348Z         >>> mix = D.Categorical(torch.ones(5,))
2024-11-01T17:51:30.0797558Z         >>> comp = D.Normal(torch.randn(5,), torch.rand(5,))
2024-11-01T17:51:30.0797742Z         >>> gmm = MixtureSameFamily(mix, comp)
2024-11-01T17:51:30.0797837Z     
2024-11-01T17:51:30.0798142Z         >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
2024-11-01T17:51:30.0798329Z         >>> # weighted bivariate normal distributions
2024-11-01T17:51:30.0798492Z         >>> mix = D.Categorical(torch.ones(5,))
2024-11-01T17:51:30.0798653Z         >>> comp = D.Independent(D.Normal(
2024-11-01T17:51:30.0798840Z         ...          torch.randn(5,2), torch.rand(5,2)), 1)
2024-11-01T17:51:30.0799021Z         >>> gmm = MixtureSameFamily(mix, comp)
2024-11-01T17:51:30.0799116Z     
2024-11-01T17:51:30.0799388Z         >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
2024-11-01T17:51:30.0799681Z         >>> # consisting of 5 random weighted bivariate normal distributions
2024-11-01T17:51:30.0799847Z         >>> mix = D.Categorical(torch.rand(3,5))
2024-11-01T17:51:30.0800009Z         >>> comp = D.Independent(D.Normal(
2024-11-01T17:51:30.0800243Z         ...         torch.randn(3,5,2), torch.rand(3,5,2)), 1)
2024-11-01T17:51:30.0800517Z         >>> gmm = MixtureSameFamily(mix, comp)
2024-11-01T17:51:30.0800613Z     
2024-11-01T17:51:30.0800728Z     Args:
2024-11-01T17:51:30.0801092Z         mixture_distribution: `torch.distributions.Categorical`-like
2024-11-01T17:51:30.0801352Z             instance. Manages the probability of selecting component.
2024-11-01T17:51:30.0801609Z             The number of categories must match the rightmost batch
2024-11-01T17:51:30.0801875Z             dimension of the `component_distribution`. Must have either
2024-11-01T17:51:30.0802090Z             scalar `batch_shape` or `batch_shape` matching
2024-11-01T17:51:30.0802342Z             `component_distribution.batch_shape[:-1]`
2024-11-01T17:51:30.0802734Z         component_distribution: `torch.distributions.Distribution`-like
2024-11-01T17:51:30.0803053Z             instance. Right-most batch dimension indexes component.
2024-11-01T17:51:30.0803148Z     
2024-11-01T17:51:30.0803595Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0803696Z     
2024-11-01T17:51:30.0803831Z       warnings.warn(msg)
2024-11-01T17:51:30.0803929Z     
2024-11-01T17:51:30.0804175Z --- Parse Warning: 72 / 103 ---
2024-11-01T17:51:30.0805769Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_bernoulli.py line=111.
2024-11-01T17:51:30.0806219Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0806330Z     
2024-11-01T17:51:30.0806914Z     Creates a RelaxedBernoulli distribution, parametrized by
2024-11-01T17:51:30.0807197Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`
2024-11-01T17:51:30.0807526Z     (but not both). This is a relaxed version of the `Bernoulli` distribution,
2024-11-01T17:51:30.0807816Z     so the values are in (0, 1), and has reparametrizable samples.
2024-11-01T17:51:30.0807914Z     
2024-11-01T17:51:30.0808031Z     Example::
2024-11-01T17:51:30.0808145Z     
2024-11-01T17:51:30.0808418Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:30.0808620Z         >>> m = RelaxedBernoulli(torch.tensor([2.2]),
2024-11-01T17:51:30.0808812Z         ...                      torch.tensor([0.1, 0.2, 0.3, 0.99]))
2024-11-01T17:51:30.0809067Z         >>> m.sample()
2024-11-01T17:51:30.0809236Z         tensor([ 0.2951,  0.3442,  0.8918,  0.9021])
2024-11-01T17:51:30.0809334Z     
2024-11-01T17:51:30.0809449Z     Args:
2024-11-01T17:51:30.0809645Z         temperature (Tensor): relaxation temperature
2024-11-01T17:51:30.0809902Z         probs (Number, Tensor): the probability of sampling `1`
2024-11-01T17:51:30.0810199Z         logits (Number, Tensor): the log-odds of sampling `1`
2024-11-01T17:51:30.0810295Z     
2024-11-01T17:51:30.0810735Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0810833Z     
2024-11-01T17:51:30.0810973Z       warnings.warn(msg)
2024-11-01T17:51:30.0811069Z     
2024-11-01T17:51:30.0811364Z --- Parse Warning: 73 / 103 ---
2024-11-01T17:51:30.0813038Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/distributions/relaxed_categorical.py line=99.
2024-11-01T17:51:30.0813503Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0813626Z     
2024-11-01T17:51:30.0813931Z     Creates a RelaxedOneHotCategorical distribution parametrized by
2024-11-01T17:51:30.0814218Z     :attr:`temperature`, and either :attr:`probs` or :attr:`logits`.
2024-11-01T17:51:30.0814575Z     This is a relaxed version of the :class:`OneHotCategorical` distribution, so
2024-11-01T17:51:30.0814872Z     its samples are on simplex, and are reparametrizable.
2024-11-01T17:51:30.0814967Z     
2024-11-01T17:51:30.0815168Z     Example::
2024-11-01T17:51:30.0815262Z     
2024-11-01T17:51:30.0815529Z         >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:30.0815765Z         >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]),
2024-11-01T17:51:30.0815966Z         ...                              torch.tensor([0.1, 0.2, 0.3, 0.4]))
2024-11-01T17:51:30.0816089Z         >>> m.sample()
2024-11-01T17:51:30.0816259Z         tensor([ 0.1294,  0.2324,  0.3859,  0.2523])
2024-11-01T17:51:30.0816359Z     
2024-11-01T17:51:30.0816473Z     Args:
2024-11-01T17:51:30.0816666Z         temperature (Tensor): relaxation temperature
2024-11-01T17:51:30.0816837Z         probs (Tensor): event probabilities
2024-11-01T17:51:30.0817101Z         logits (Tensor): unnormalized log probability for each event
2024-11-01T17:51:30.0817209Z     
2024-11-01T17:51:30.0817636Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0817733Z     
2024-11-01T17:51:30.0817863Z       warnings.warn(msg)
2024-11-01T17:51:30.0817962Z     
2024-11-01T17:51:30.0818202Z --- Parse Warning: 74 / 103 ---
2024-11-01T17:51:30.0819862Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=245.
2024-11-01T17:51:30.0820324Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0820601Z     Return a new dict with new, potentially nested, key value pair
2024-11-01T17:51:30.0820697Z     
2024-11-01T17:51:30.0820825Z         >>> purchase = {
2024-11-01T17:51:30.0820945Z         ...     "name": "Alice",
2024-11-01T17:51:30.0821219Z         ...     "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2024-11-01T17:51:30.0821450Z         ...     "credit card": "5555-1234-1234-1234",
2024-11-01T17:51:30.0821568Z         ... }
2024-11-01T17:51:30.0821876Z         >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00])  # doctest: +SKIP
2024-11-01T17:51:30.0822090Z         {'credit card': '5555-1234-1234-1234',
2024-11-01T17:51:30.0822255Z          'name': 'Alice',
2024-11-01T17:51:30.0822580Z          'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}}
2024-11-01T17:51:30.0822692Z         
2024-11-01T17:51:30.0823157Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0823268Z     
2024-11-01T17:51:30.0823389Z       warnings.warn(msg)
2024-11-01T17:51:30.0828593Z     
2024-11-01T17:51:30.0828923Z --- Parse Warning: 75 / 103 ---
2024-11-01T17:51:30.0830623Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=261.
2024-11-01T17:51:30.0831101Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0831313Z     Update value in a (potentially) nested dictionary
2024-11-01T17:51:30.0831411Z     
2024-11-01T17:51:30.0831532Z         inputs:
2024-11-01T17:51:30.0831745Z         d - dictionary on which to operate
2024-11-01T17:51:30.0832171Z         keys - list or tuple giving the location of the value to be changed in d
2024-11-01T17:51:30.0832406Z         func - function to operate on that value
2024-11-01T17:51:30.0832518Z     
2024-11-01T17:51:30.0832832Z         If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the
2024-11-01T17:51:30.0833166Z         original dictionary with v replaced by func(v), but does not mutate the
2024-11-01T17:51:30.0833306Z         original dictionary.
2024-11-01T17:51:30.0833403Z     
2024-11-01T17:51:30.0833762Z         If k0 is not a key in d, update_in creates nested dictionaries to the depth
2024-11-01T17:51:30.0834377Z         specified by the keys, with the innermost value set to func(default).
2024-11-01T17:51:30.0834556Z     
2024-11-01T17:51:30.0834687Z         >>> inc = lambda x: x + 1
2024-11-01T17:51:30.0834833Z         >>> update_in({"a": 0}, ["a"], inc)
2024-11-01T17:51:30.0834996Z         {'a': 1}
2024-11-01T17:51:30.0835090Z     
2024-11-01T17:51:30.0835225Z         >>> transaction = {
2024-11-01T17:51:30.0835345Z         ...     "name": "Alice",
2024-11-01T17:51:30.0835625Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2024-11-01T17:51:30.0835874Z         ...     "credit card": "5555-1234-1234-1234",
2024-11-01T17:51:30.0835976Z         ... }
2024-11-01T17:51:30.0836302Z         >>> update_in(transaction, ["purchase", "costs"], sum)  # doctest: +SKIP
2024-11-01T17:51:30.0836515Z         {'credit card': '5555-1234-1234-1234',
2024-11-01T17:51:30.0836686Z          'name': 'Alice',
2024-11-01T17:51:30.0836996Z          'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}}
2024-11-01T17:51:30.0837095Z     
2024-11-01T17:51:30.0837289Z         >>> # updating a value when k0 is not in d
2024-11-01T17:51:30.0837476Z         >>> update_in({}, [1, 2, 3], str, default="bar")
2024-11-01T17:51:30.0837649Z         {1: {2: {3: 'bar'}}}
2024-11-01T17:51:30.0837848Z         >>> update_in({1: "foo"}, [2, 3, 4], inc, 0)
2024-11-01T17:51:30.0838032Z         {1: 'foo', 2: {3: {4: 1}}}
2024-11-01T17:51:30.0838132Z         
2024-11-01T17:51:30.0838565Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0838725Z     
2024-11-01T17:51:30.0838848Z       warnings.warn(msg)
2024-11-01T17:51:30.0838957Z     
2024-11-01T17:51:30.0841811Z --- Parse Warning: 76 / 103 ---
2024-11-01T17:51:30.0843486Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=320.
2024-11-01T17:51:30.0843956Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0844189Z     Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys.
2024-11-01T17:51:30.0844299Z     
2024-11-01T17:51:30.0844586Z         If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless
2024-11-01T17:51:30.0844891Z         ``no_default`` is specified, then it raises KeyError or IndexError.
2024-11-01T17:51:30.0845077Z     
2024-11-01T17:51:30.0845388Z         ``get_in`` is a generalization of ``operator.getitem`` for nested data
2024-11-01T17:51:30.0845585Z         structures such as dictionaries and lists.
2024-11-01T17:51:30.0845680Z     
2024-11-01T17:51:30.0845852Z         >>> transaction = {
2024-11-01T17:51:30.0845975Z         ...     "name": "Alice",
2024-11-01T17:51:30.0846255Z         ...     "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]},
2024-11-01T17:51:30.0846507Z         ...     "credit card": "5555-1234-1234-1234",
2024-11-01T17:51:30.0846614Z         ... }
2024-11-01T17:51:30.0846827Z         >>> get_in(["purchase", "items", 0], transaction)
2024-11-01T17:51:30.0846965Z         'Apple'
2024-11-01T17:51:30.0847108Z         >>> get_in(["name"], transaction)
2024-11-01T17:51:30.0847255Z         'Alice'
2024-11-01T17:51:30.0847438Z         >>> get_in(["purchase", "total"], transaction)
2024-11-01T17:51:30.0847668Z         >>> get_in(["purchase", "items", "apple"], transaction)
2024-11-01T17:51:30.0847865Z         >>> get_in(["purchase", "items", 10], transaction)
2024-11-01T17:51:30.0848070Z         >>> get_in(["purchase", "total"], transaction, 0)
2024-11-01T17:51:30.0848173Z         0
2024-11-01T17:51:30.0848333Z         >>> get_in(["y"], {}, no_default=True)
2024-11-01T17:51:30.0848502Z         Traceback (most recent call last):
2024-11-01T17:51:30.0848606Z             ...
2024-11-01T17:51:30.0848769Z         KeyError: 'y'
2024-11-01T17:51:30.0848866Z     
2024-11-01T17:51:30.0849022Z         See Also:
2024-11-01T17:51:30.0849140Z             itertoolz.get
2024-11-01T17:51:30.0849294Z             operator.getitem
2024-11-01T17:51:30.0849411Z         
2024-11-01T17:51:30.0849846Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0849960Z     
2024-11-01T17:51:30.0850081Z       warnings.warn(msg)
2024-11-01T17:51:30.0850176Z     
2024-11-01T17:51:30.0850421Z --- Parse Warning: 77 / 103 ---
2024-11-01T17:51:30.0852089Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/fx/experimental/unification/unification_tools.py line=373.
2024-11-01T17:51:30.0852554Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0852708Z     Group a collection by a key function
2024-11-01T17:51:30.0852820Z     
2024-11-01T17:51:30.0853072Z         >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"]
2024-11-01T17:51:30.0853251Z         >>> groupby(len, names)  # doctest: +SKIP
2024-11-01T17:51:30.0853600Z         {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}
2024-11-01T17:51:30.0853697Z     
2024-11-01T17:51:30.0853857Z         >>> iseven = lambda x: x % 2 == 0
2024-11-01T17:51:30.0854112Z         >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8])  # doctest: +SKIP
2024-11-01T17:51:30.0854288Z         {False: [1, 3, 5, 7], True: [2, 4, 6, 8]}
2024-11-01T17:51:30.0854387Z     
2024-11-01T17:51:30.0854646Z         Non-callable keys imply grouping on a member.
2024-11-01T17:51:30.0854756Z     
2024-11-01T17:51:30.0854866Z         >>> groupby(
2024-11-01T17:51:30.0854992Z         ...     "gender",
2024-11-01T17:51:30.0855185Z         ...     [
2024-11-01T17:51:30.0855375Z         ...         {"name": "Alice", "gender": "F"},
2024-11-01T17:51:30.0855537Z         ...         {"name": "Bob", "gender": "M"},
2024-11-01T17:51:30.0855718Z         ...         {"name": "Charlie", "gender": "M"},
2024-11-01T17:51:30.0855837Z         ...     ],
2024-11-01T17:51:30.0855963Z         ... )  # doctest:+SKIP
2024-11-01T17:51:30.0856203Z         {'F': [{'gender': 'F', 'name': 'Alice'}],
2024-11-01T17:51:30.0856412Z          'M': [{'gender': 'M', 'name': 'Bob'},
2024-11-01T17:51:30.0856635Z                {'gender': 'M', 'name': 'Charlie'}]}
2024-11-01T17:51:30.0856746Z     
2024-11-01T17:51:30.0856947Z         Not to be confused with ``itertools.groupby``
2024-11-01T17:51:30.0857088Z     
2024-11-01T17:51:30.0857195Z         See Also:
2024-11-01T17:51:30.0857316Z             countby
2024-11-01T17:51:30.0857417Z         
2024-11-01T17:51:30.0857852Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0857963Z     
2024-11-01T17:51:30.0858084Z       warnings.warn(msg)
2024-11-01T17:51:30.0858195Z     
2024-11-01T17:51:30.0858424Z --- Parse Warning: 78 / 103 ---
2024-11-01T17:51:30.0859927Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=601.
2024-11-01T17:51:30.0860375Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0860684Z     Applies Batch Normalization over a N-Dimensional input.
2024-11-01T17:51:30.0860797Z     
2024-11-01T17:51:30.0861448Z         The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
2024-11-01T17:51:30.0861783Z         `Batch Normalization: Accelerating Deep Network Training by Reducing
2024-11-01T17:51:30.0862090Z         Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
2024-11-01T17:51:30.0862203Z     
2024-11-01T17:51:30.0862326Z         .. math::
2024-11-01T17:51:30.0862421Z     
2024-11-01T17:51:30.0862884Z             y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
2024-11-01T17:51:30.0863033Z     
2024-11-01T17:51:30.0863487Z         The mean and standard-deviation are calculated per-dimension over all
2024-11-01T17:51:30.0863914Z         mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
2024-11-01T17:51:30.0864273Z         are learnable parameter vectors of size `C` (where `C` is the input size).
2024-11-01T17:51:30.0864533Z         By default, the elements of :math:`\gamma` are sampled from
2024-11-01T17:51:30.0864853Z         :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
2024-11-01T17:51:30.0865318Z         The standard-deviation is calculated via the biased estimator, equivalent to
2024-11-01T17:51:30.0865475Z         `torch.var(input, unbiased=False)`.
2024-11-01T17:51:30.0865586Z     
2024-11-01T17:51:30.0865927Z         Also by default, during training this layer keeps running estimates of its
2024-11-01T17:51:30.0866276Z         computed mean and variance, which are then used for normalization during
2024-11-01T17:51:30.0866623Z         evaluation. The running estimates are kept with a default :attr:`momentum`
2024-11-01T17:51:30.0866729Z         of 0.1.
2024-11-01T17:51:30.0866842Z     
2024-11-01T17:51:30.0867173Z         If :attr:`track_running_stats` is set to ``False``, this layer then does not
2024-11-01T17:51:30.0867496Z         keep running estimates, and batch statistics are instead used during
2024-11-01T17:51:30.0867630Z         evaluation time as well.
2024-11-01T17:51:30.0867745Z     
2024-11-01T17:51:30.0867854Z         .. note::
2024-11-01T17:51:30.0868176Z             This :attr:`momentum` argument is different from one used in optimizer
2024-11-01T17:51:30.0868559Z             classes and the conventional notion of momentum. Mathematically, the
2024-11-01T17:51:30.0868751Z             update rule for running statistics here is
2024-11-01T17:51:30.0869298Z             :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
2024-11-01T17:51:30.0869620Z             where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
2024-11-01T17:51:30.0869761Z             new observed value.
2024-11-01T17:51:30.0869856Z     
2024-11-01T17:51:30.0870297Z         Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
2024-11-01T17:51:30.0870772Z         statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
2024-11-01T17:51:30.0871080Z         Normalization or Spatio-temporal Batch Normalization.
2024-11-01T17:51:30.0871221Z     
2024-11-01T17:51:30.0871426Z         Currently :class:`SyncBatchNorm` only supports
2024-11-01T17:51:30.0871838Z         :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
2024-11-01T17:51:30.0872148Z         :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
2024-11-01T17:51:30.0872443Z         :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
2024-11-01T17:51:30.0872581Z         Network with DDP.
2024-11-01T17:51:30.0872676Z     
2024-11-01T17:51:30.0872795Z         Args:
2024-11-01T17:51:30.0873031Z             num_features: :math:`C` from an expected input of size
2024-11-01T17:51:30.0873168Z                 :math:`(N, C, +)`
2024-11-01T17:51:30.0873455Z             eps: a value added to the denominator for numerical stability.
2024-11-01T17:51:30.0873628Z                 Default: ``1e-5``
2024-11-01T17:51:30.0874052Z             momentum: the value used for the running_mean and running_var
2024-11-01T17:51:30.0874358Z                 computation. Can be set to ``None`` for cumulative moving average
2024-11-01T17:51:30.0874545Z                 (i.e. simple average). Default: 0.1
2024-11-01T17:51:30.0874841Z             affine: a boolean value that when set to ``True``, this module has
2024-11-01T17:51:30.0875071Z                 learnable affine parameters. Default: ``True``
2024-11-01T17:51:30.0875374Z             track_running_stats: a boolean value that when set to ``True``, this
2024-11-01T17:51:30.0875752Z                 module tracks the running mean and variance, and when set to ``False``,
2024-11-01T17:51:30.0876124Z                 this module does not track such statistics, and initializes statistics
2024-11-01T17:51:30.0876415Z                 buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
2024-11-01T17:51:30.0876778Z                 When these buffers are ``None``, this module always uses batch statistics.
2024-11-01T17:51:30.0877002Z                 in both training and eval modes. Default: ``True``
2024-11-01T17:51:30.0877361Z             process_group: synchronization of stats happen within each process group
2024-11-01T17:51:30.0877675Z                 individually. Default behavior is synchronization across the whole
2024-11-01T17:51:30.0877798Z                 world
2024-11-01T17:51:30.0877895Z     
2024-11-01T17:51:30.0878000Z         Shape:
2024-11-01T17:51:30.0878208Z             - Input: :math:`(N, C, +)`
2024-11-01T17:51:30.0878483Z             - Output: :math:`(N, C, +)` (same shape as input)
2024-11-01T17:51:30.0878594Z     
2024-11-01T17:51:30.0878708Z         .. note::
2024-11-01T17:51:30.0879057Z             Synchronization of batchnorm statistics occurs only while training, i.e.
2024-11-01T17:51:30.0879359Z             synchronization is disabled when ``model.eval()`` is set or if
2024-11-01T17:51:30.0879539Z             ``self.training`` is otherwise ``False``.
2024-11-01T17:51:30.0879651Z     
2024-11-01T17:51:30.0879768Z         Examples::
2024-11-01T17:51:30.0879879Z     
2024-11-01T17:51:30.0880008Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:30.0880160Z             >>> # With Learnable Parameters
2024-11-01T17:51:30.0880320Z             >>> m = nn.SyncBatchNorm(100)
2024-11-01T17:51:30.0880529Z             >>> # creating process group (optional)
2024-11-01T17:51:30.0880753Z             >>> # ranks is a list of int identifying rank ids.
2024-11-01T17:51:30.0880889Z             >>> ranks = list(range(8))
2024-11-01T17:51:30.0881054Z             >>> r1, r2 = ranks[:4], ranks[4:]
2024-11-01T17:51:30.0881273Z             >>> # Note: every rank calls into new_group for every
2024-11-01T17:51:30.0881490Z             >>> # process group created, even if that rank is not
2024-11-01T17:51:30.0881637Z             >>> # part of the group.
2024-11-01T17:51:30.0881985Z             >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2024-11-01T17:51:30.0882285Z             >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2024-11-01T17:51:30.0882479Z             >>> # Without Learnable Parameters
2024-11-01T17:51:30.0882795Z             >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
2024-11-01T17:51:30.0882977Z             >>> input = torch.randn(20, 100, 35, 45, 10)
2024-11-01T17:51:30.0883104Z             >>> output = m(input)
2024-11-01T17:51:30.0883214Z     
2024-11-01T17:51:30.0883379Z             >>> # network is nn.BatchNorm layer
2024-11-01T17:51:30.0883786Z             >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
2024-11-01T17:51:30.0884029Z             >>> # only single gpu per process is currently supported
2024-11-01T17:51:30.0884364Z             >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
2024-11-01T17:51:30.0884533Z             >>>                         sync_bn_network,
2024-11-01T17:51:30.0884723Z             >>>                         device_ids=[args.local_rank],
2024-11-01T17:51:30.0884934Z             >>>                         output_device=args.local_rank)
2024-11-01T17:51:30.0885037Z         
2024-11-01T17:51:30.0885488Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0885584Z     
2024-11-01T17:51:30.0885723Z       warnings.warn(msg)
2024-11-01T17:51:30.0885820Z     
2024-11-01T17:51:30.0886050Z --- Parse Warning: 79 / 103 ---
2024-11-01T17:51:30.0887733Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/batchnorm.py line=824.
2024-11-01T17:51:30.0888205Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0888675Z     Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers.
2024-11-01T17:51:30.0888772Z     
2024-11-01T17:51:30.0888894Z             Args:
2024-11-01T17:51:30.0889255Z                 module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
2024-11-01T17:51:30.0889577Z                 process_group (optional): process group to scope synchronization,
2024-11-01T17:51:30.0889737Z                     default is the whole world
2024-11-01T17:51:30.0889834Z     
2024-11-01T17:51:30.0889955Z             Returns:
2024-11-01T17:51:30.0890328Z                 The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
2024-11-01T17:51:30.0890663Z                 layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
2024-11-01T17:51:30.0890980Z                 a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
2024-11-01T17:51:30.0891106Z                 instead.
2024-11-01T17:51:30.0891202Z     
2024-11-01T17:51:30.0891318Z             Example::
2024-11-01T17:51:30.0891430Z     
2024-11-01T17:51:30.0891609Z                 >>> # Network with nn.BatchNorm layer
2024-11-01T17:51:30.0891831Z                 >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:30.0892001Z                 >>> module = torch.nn.Sequential(
2024-11-01T17:51:30.0892179Z                 >>>            torch.nn.Linear(20, 100),
2024-11-01T17:51:30.0892406Z                 >>>            torch.nn.BatchNorm1d(100),
2024-11-01T17:51:30.0892529Z                 >>>          ).cuda()
2024-11-01T17:51:30.0892726Z                 >>> # creating process group (optional)
2024-11-01T17:51:30.0892941Z                 >>> # ranks is a list of int identifying rank ids.
2024-11-01T17:51:30.0893098Z                 >>> ranks = list(range(8))
2024-11-01T17:51:30.0893254Z                 >>> r1, r2 = ranks[:4], ranks[4:]
2024-11-01T17:51:30.0893474Z                 >>> # Note: every rank calls into new_group for every
2024-11-01T17:51:30.0893708Z                 >>> # process group created, even if that rank is not
2024-11-01T17:51:30.0893849Z                 >>> # part of the group.
2024-11-01T17:51:30.0894073Z                 >>> # xdoctest: +SKIP("distributed")
2024-11-01T17:51:30.0894428Z                 >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
2024-11-01T17:51:30.0894736Z                 >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
2024-11-01T17:51:30.0895164Z                 >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
2024-11-01T17:51:30.0895277Z     
2024-11-01T17:51:30.0895379Z             
2024-11-01T17:51:30.0895818Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0895927Z     
2024-11-01T17:51:30.0896051Z       warnings.warn(msg)
2024-11-01T17:51:30.0896159Z     
2024-11-01T17:51:30.0896390Z --- Parse Warning: 80 / 103 ---
2024-11-01T17:51:30.0897828Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/flatten.py line=60.
2024-11-01T17:51:30.0898293Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0898390Z     
2024-11-01T17:51:30.0898870Z     Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`.
2024-11-01T17:51:30.0898967Z     
2024-11-01T17:51:30.0899377Z     * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can
2024-11-01T17:51:30.0899719Z       be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
2024-11-01T17:51:30.0899858Z     
2024-11-01T17:51:30.0900350Z     * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
2024-11-01T17:51:30.0900758Z       a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape`
2024-11-01T17:51:30.0901007Z       (tuple of `(name, size)` tuples) for `NamedTensor` input.
2024-11-01T17:51:30.0901104Z     
2024-11-01T17:51:30.0901224Z     Shape:
2024-11-01T17:51:30.0901662Z         - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at
2024-11-01T17:51:30.0902050Z           dimension :attr:`dim` and :math:`*` means any number of dimensions including none.
2024-11-01T17:51:30.0902482Z         - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and
2024-11-01T17:51:30.0902668Z           :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`.
2024-11-01T17:51:30.0902779Z     
2024-11-01T17:51:30.0902883Z     Args:
2024-11-01T17:51:30.0903111Z         dim (Union[int, str]): Dimension to be unflattened
2024-11-01T17:51:30.0903602Z         unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
2024-11-01T17:51:30.0903714Z     
2024-11-01T17:51:30.0903821Z     Examples:
2024-11-01T17:51:30.0903964Z         >>> input = torch.randn(2, 50)
2024-11-01T17:51:30.0904107Z         >>> # With tuple of ints
2024-11-01T17:51:30.0904231Z         >>> m = nn.Sequential(
2024-11-01T17:51:30.0904374Z         >>>     nn.Linear(50, 50),
2024-11-01T17:51:30.0904516Z         >>>     nn.Unflatten(1, (2, 5, 5))
2024-11-01T17:51:30.0904620Z         >>> )
2024-11-01T17:51:30.0904755Z         >>> output = m(input)
2024-11-01T17:51:30.0904905Z         >>> output.size()
2024-11-01T17:51:30.0905047Z         torch.Size([2, 2, 5, 5])
2024-11-01T17:51:30.0905167Z         >>> # With torch.Size
2024-11-01T17:51:30.0905306Z         >>> m = nn.Sequential(
2024-11-01T17:51:30.0905435Z         >>>     nn.Linear(50, 50),
2024-11-01T17:51:30.0905615Z         >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
2024-11-01T17:51:30.0905736Z         >>> )
2024-11-01T17:51:30.0905858Z         >>> output = m(input)
2024-11-01T17:51:30.0905989Z         >>> output.size()
2024-11-01T17:51:30.0906112Z         torch.Size([2, 2, 5, 5])
2024-11-01T17:51:30.0906282Z         >>> # With namedshape (tuple of tuples)
2024-11-01T17:51:30.0906816Z         >>> input = torch.randn(2, 50, names=('N', 'features'))
2024-11-01T17:51:30.0907314Z         >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
2024-11-01T17:51:30.0907473Z         >>> output = unflatten(input)
2024-11-01T17:51:30.0907591Z         >>> output.size()
2024-11-01T17:51:30.0907734Z         torch.Size([2, 2, 5, 5])
2024-11-01T17:51:30.0907829Z     
2024-11-01T17:51:30.0908257Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0908368Z     
2024-11-01T17:51:30.0908491Z       warnings.warn(msg)
2024-11-01T17:51:30.0908604Z     
2024-11-01T17:51:30.0908834Z --- Parse Warning: 81 / 103 ---
2024-11-01T17:51:30.0910423Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/loss.py line=1696.
2024-11-01T17:51:30.0910869Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0911151Z     Creates a criterion that measures the triplet loss given input
2024-11-01T17:51:30.0911445Z         tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor,
2024-11-01T17:51:30.0911753Z         positive, and negative examples, respectively), and a nonnegative,
2024-11-01T17:51:30.0912200Z         real-valued function ("distance function") used to compute the relationship
2024-11-01T17:51:30.0912517Z         between the anchor and positive example ("positive distance") and the
2024-11-01T17:51:30.0912795Z         anchor and negative example ("negative distance").
2024-11-01T17:51:30.0912928Z     
2024-11-01T17:51:30.0913317Z         The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``)
2024-11-01T17:51:30.0913455Z         can be described as:
2024-11-01T17:51:30.0913550Z     
2024-11-01T17:51:30.0913679Z         .. math::
2024-11-01T17:51:30.0913988Z             \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad
2024-11-01T17:51:30.0914330Z             l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}
2024-11-01T17:51:30.0914429Z     
2024-11-01T17:51:30.0914900Z         where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function
2024-11-01T17:51:30.0915333Z         quantifying the closeness of two tensors, referred to as the :attr:`distance_function`;
2024-11-01T17:51:30.0915692Z         and :math:`margin` is a nonnegative margin representing the minimum difference
2024-11-01T17:51:30.0916064Z         between the positive and negative distances that is required for the loss to
2024-11-01T17:51:30.0916410Z         be 0.  The input tensors have :math:`N` elements each and can be of any shape
2024-11-01T17:51:30.0916592Z         that the distance function can handle.
2024-11-01T17:51:30.0916689Z     
2024-11-01T17:51:30.0916909Z         If :attr:`reduction` is not ``'none'``
2024-11-01T17:51:30.0917101Z         (default ``'mean'``), then:
2024-11-01T17:51:30.0917195Z     
2024-11-01T17:51:30.0917317Z         .. math::
2024-11-01T17:51:30.0917432Z             \ell(x, y) =
2024-11-01T17:51:30.0917561Z             \begin{cases}
2024-11-01T17:51:30.0917945Z                 \operatorname{mean}(L), &  \text{if reduction} = \text{`mean';}\\
2024-11-01T17:51:30.0918358Z                 \operatorname{sum}(L),  &  \text{if reduction} = \text{`sum'.}
2024-11-01T17:51:30.0918486Z             \end{cases}
2024-11-01T17:51:30.0918583Z     
2024-11-01T17:51:30.0918942Z         See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet
2024-11-01T17:51:30.0919310Z         loss for input tensors using the :math:`l_p` distance as the distance function.
2024-11-01T17:51:30.0919421Z     
2024-11-01T17:51:30.0919524Z         Args:
2024-11-01T17:51:30.0919992Z             distance_function (Callable, optional): A nonnegative, real-valued function that
2024-11-01T17:51:30.0920276Z                 quantifies the closeness of two tensors. If not specified,
2024-11-01T17:51:30.0920521Z                 `nn.PairwiseDistance` will be used.  Default: ``None``
2024-11-01T17:51:30.0920946Z             margin (float, optional): A nonnegative margin representing the minimum difference
2024-11-01T17:51:30.0921343Z                 between the positive and negative distances required for the loss to be 0. Larger
2024-11-01T17:51:30.0921764Z                 margins penalize cases where the negative examples are not distant enough from the
2024-11-01T17:51:30.0922015Z                 anchors, relative to the positives. Default: :math:`1`.
2024-11-01T17:51:30.0922378Z             swap (bool, optional): Whether to use the distance swap described in the paper
2024-11-01T17:51:30.0922760Z                 `Learning shallow convolutional feature descriptors with triplet losses` by
2024-11-01T17:51:30.0923135Z                 V. Balntas, E. Riba et al. If True, and if the positive example is closer to the
2024-11-01T17:51:30.0923549Z                 negative example than the anchor is, swaps the positive example and the anchor in
2024-11-01T17:51:30.0923742Z                 the loss computation. Default: ``False``.
2024-11-01T17:51:30.0924153Z             reduction (str, optional): Specifies the (optional) reduction to apply to the output:
2024-11-01T17:51:30.0924542Z                 ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
2024-11-01T17:51:30.0924920Z                 ``'mean'``: the sum of the output will be divided by the number of
2024-11-01T17:51:30.0925371Z                 elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'``
2024-11-01T17:51:30.0925498Z     
2024-11-01T17:51:30.0925607Z     
2024-11-01T17:51:30.0925737Z         Shape:
2024-11-01T17:51:30.0926208Z             - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions
2024-11-01T17:51:30.0926389Z               as supported by the distance function.
2024-11-01T17:51:30.0926883Z             - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar
2024-11-01T17:51:30.0926998Z               otherwise.
2024-11-01T17:51:30.0927094Z     
2024-11-01T17:51:30.0927222Z         Examples::
2024-11-01T17:51:30.0927319Z     
2024-11-01T17:51:30.0927466Z         >>> # Initialize embeddings
2024-11-01T17:51:30.0927633Z         >>> embedding = nn.Embedding(1000, 128)
2024-11-01T17:51:30.0927830Z         >>> anchor_ids = torch.randint(0, 1000, (1,))
2024-11-01T17:51:30.0928017Z         >>> positive_ids = torch.randint(0, 1000, (1,))
2024-11-01T17:51:30.0928199Z         >>> negative_ids = torch.randint(0, 1000, (1,))
2024-11-01T17:51:30.0928368Z         >>> anchor = embedding(anchor_ids)
2024-11-01T17:51:30.0928538Z         >>> positive = embedding(positive_ids)
2024-11-01T17:51:30.0928715Z         >>> negative = embedding(negative_ids)
2024-11-01T17:51:30.0928819Z         >>>
2024-11-01T17:51:30.0929018Z         >>> # Built-in Distance Function
2024-11-01T17:51:30.0929153Z         >>> triplet_loss = \
2024-11-01T17:51:30.0929539Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance())
2024-11-01T17:51:30.0929773Z         >>> output = triplet_loss(anchor, positive, negative)
2024-11-01T17:51:30.0929900Z         >>> output.backward()
2024-11-01T17:51:30.0930018Z         >>>
2024-11-01T17:51:30.0930159Z         >>> # Custom Distance Function
2024-11-01T17:51:30.0930363Z         >>> def l_infinity(x1, x2):
2024-11-01T17:51:30.0930678Z         >>>     return torch.max(torch.abs(x1 - x2), dim=1).values
2024-11-01T17:51:30.0930779Z         >>>
2024-11-01T17:51:30.0931068Z         >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time")
2024-11-01T17:51:30.0931190Z         >>> triplet_loss = (
2024-11-01T17:51:30.0931590Z         >>>     nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5))
2024-11-01T17:51:30.0931806Z         >>> output = triplet_loss(anchor, positive, negative)
2024-11-01T17:51:30.0931932Z         >>> output.backward()
2024-11-01T17:51:30.0932047Z         >>>
2024-11-01T17:51:30.0932214Z         >>> # Custom Distance Function (Lambda)
2024-11-01T17:51:30.0932378Z         >>> triplet_loss = (
2024-11-01T17:51:30.0932568Z         >>>     nn.TripletMarginWithDistanceLoss(
2024-11-01T17:51:30.0932970Z         >>>         distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y)))
2024-11-01T17:51:30.0933188Z         >>> output = triplet_loss(anchor, positive, negative)
2024-11-01T17:51:30.0933314Z         >>> output.backward()
2024-11-01T17:51:30.0933426Z     
2024-11-01T17:51:30.0933535Z         Reference:
2024-11-01T17:51:30.0933995Z             V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses:
2024-11-01T17:51:30.0934264Z             http://www.bmva.org/bmvc/2016/papers/paper119/index.html
2024-11-01T17:51:30.0934379Z         
2024-11-01T17:51:30.0934817Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17))
2024-11-01T17:51:30.0934914Z     
2024-11-01T17:51:30.0935048Z       warnings.warn(msg)
2024-11-01T17:51:30.0935144Z     
2024-11-01T17:51:30.0935390Z --- Parse Warning: 82 / 103 ---
2024-11-01T17:51:30.0936863Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/pooling.py line=395.
2024-11-01T17:51:30.0937324Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0937534Z     Computes a partial inverse of :class:`MaxPool2d`.
2024-11-01T17:51:30.0937663Z     
2024-11-01T17:51:30.0938181Z         :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost.
2024-11-01T17:51:30.0938280Z     
2024-11-01T17:51:30.0938619Z         :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d`
2024-11-01T17:51:30.0938971Z         including the indices of the maximal values and computes a partial inverse
2024-11-01T17:51:30.0939255Z         in which all non-maximal values are set to zero.
2024-11-01T17:51:30.0939353Z     
2024-11-01T17:51:30.0939456Z         Note:
2024-11-01T17:51:30.0939913Z             This operation may behave nondeterministically when the input indices has repeat values.
2024-11-01T17:51:30.0940450Z             See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information.
2024-11-01T17:51:30.0940561Z     
2024-11-01T17:51:30.0940892Z         .. note:: :class:`MaxPool2d` can map several input sizes to the same output
2024-11-01T17:51:30.0941158Z                   sizes. Hence, the inversion process can get ambiguous.
2024-11-01T17:51:30.0941440Z                   To accommodate this, you can provide the needed output size
2024-11-01T17:51:30.0941747Z                   as an additional argument :attr:`output_size` in the forward call.
2024-11-01T17:51:30.0941932Z                   See the Inputs and Example below.
2024-11-01T17:51:30.0942027Z     
2024-11-01T17:51:30.0942146Z         Args:
2024-11-01T17:51:30.0942409Z             kernel_size (int or tuple): Size of the max pooling window.
2024-11-01T17:51:30.0942672Z             stride (int or tuple): Stride of the max pooling window.
2024-11-01T17:51:30.0942869Z                 It is set to :attr:`kernel_size` by default.
2024-11-01T17:51:30.0943176Z             padding (int or tuple): Padding that was added to the input
2024-11-01T17:51:30.0943287Z     
2024-11-01T17:51:30.0943392Z         Inputs:
2024-11-01T17:51:30.0943632Z             - `input`: the input Tensor to invert
2024-11-01T17:51:30.0944005Z             - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
2024-11-01T17:51:30.0944310Z             - `output_size` (optional): the targeted output size
2024-11-01T17:51:30.0944406Z     
2024-11-01T17:51:30.0944510Z         Shape:
2024-11-01T17:51:30.0944888Z             - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
2024-11-01T17:51:30.0945304Z             - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
2024-11-01T17:51:30.0945445Z     
2024-11-01T17:51:30.0945558Z               .. math::
2024-11-01T17:51:30.0946127Z                 H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}
2024-11-01T17:51:30.0946224Z     
2024-11-01T17:51:30.0946339Z               .. math::
2024-11-01T17:51:30.0946897Z                 W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}
2024-11-01T17:51:30.0946991Z     
2024-11-01T17:51:30.0947247Z               or as given by :attr:`output_size` in the call operator
2024-11-01T17:51:30.0947345Z     
2024-11-01T17:51:30.0947455Z         Example::
2024-11-01T17:51:30.0947567Z     
2024-11-01T17:51:30.0947804Z             >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
2024-11-01T17:51:30.0948000Z             >>> unpool = nn.MaxUnpool2d(2, stride=2)
2024-11-01T17:51:30.0948193Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
2024-11-01T17:51:30.0948375Z                                         [ 5.,  6.,  7.,  8.],
2024-11-01T17:51:30.0948541Z                                         [ 9., 10., 11., 12.],
2024-11-01T17:51:30.0948711Z                                         [13., 14., 15., 16.]]]])
2024-11-01T17:51:30.0948885Z             >>> output, indices = pool(input)
2024-11-01T17:51:30.0949028Z             >>> unpool(output, indices)
2024-11-01T17:51:30.0949196Z             tensor([[[[  0.,   0.,   0.,   0.],
2024-11-01T17:51:30.0949339Z                       [  0.,   6.,   0.,   8.],
2024-11-01T17:51:30.0949545Z                       [  0.,   0.,   0.,   0.],
2024-11-01T17:51:30.0949714Z                       [  0.,  14.,   0.,  16.]]]])
2024-11-01T17:51:30.0950028Z             >>> # Now using output_size to resolve an ambiguous size for the inverse
2024-11-01T17:51:30.0950251Z             >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.,  5.],
2024-11-01T17:51:30.0950421Z                                         [ 6.,  7.,  8.,  9., 10.],
2024-11-01T17:51:30.0950604Z                                         [11., 12., 13., 14., 15.],
2024-11-01T17:51:30.0950782Z                                         [16., 17., 18., 19., 20.]]]])
2024-11-01T17:51:30.0950957Z             >>> output, indices = pool(input)
2024-11-01T17:51:30.0951208Z             >>> # This call will not work without specifying output_size
2024-11-01T17:51:30.0951444Z             >>> unpool(output, indices, output_size=input.size())
2024-11-01T17:51:30.0951600Z             tensor([[[[ 0.,  0.,  0.,  0.,  0.],
2024-11-01T17:51:30.0951747Z                       [ 0.,  7.,  0.,  9.,  0.],
2024-11-01T17:51:30.0951903Z                       [ 0.,  0.,  0.,  0.,  0.],
2024-11-01T17:51:30.0952053Z                       [ 0., 17.,  0., 19.,  0.]]]])
2024-11-01T17:51:30.0952162Z     
2024-11-01T17:51:30.0952258Z     
2024-11-01T17:51:30.0952358Z         
2024-11-01T17:51:30.0952806Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0952902Z     
2024-11-01T17:51:30.0953037Z       warnings.warn(msg)
2024-11-01T17:51:30.0953135Z     
2024-11-01T17:51:30.0953378Z --- Parse Warning: 83 / 103 ---
2024-11-01T17:51:30.0955025Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/modules/sparse.py line=270.
2024-11-01T17:51:30.0955479Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0956064Z     Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
2024-11-01T17:51:30.0956164Z     
2024-11-01T17:51:30.0956656Z         For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
2024-11-01T17:51:30.0956800Z         and with 2D inputs, this class
2024-11-01T17:51:30.0956911Z     
2024-11-01T17:51:30.0957375Z             * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
2024-11-01T17:51:30.0957890Z             * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
2024-11-01T17:51:30.0958346Z             * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
2024-11-01T17:51:30.0958444Z     
2024-11-01T17:51:30.0958986Z         However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
2024-11-01T17:51:30.0959098Z         operations.
2024-11-01T17:51:30.0959208Z     
2024-11-01T17:51:30.0959656Z         EmbeddingBag also supports per-sample weights as an argument to the forward
2024-11-01T17:51:30.0960020Z         pass. This scales the output of the Embedding before performing a weighted
2024-11-01T17:51:30.0960387Z         reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
2024-11-01T17:51:30.0960736Z         only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
2024-11-01T17:51:30.0960887Z         :attr:`per_sample_weights`.
2024-11-01T17:51:30.0960982Z     
2024-11-01T17:51:30.0961099Z         Args:
2024-11-01T17:51:30.0961359Z             num_embeddings (int): size of the dictionary of embeddings
2024-11-01T17:51:30.0961613Z             embedding_dim (int): the size of each embedding vector
2024-11-01T17:51:30.0962077Z             max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
2024-11-01T17:51:30.0962309Z                                         is renormalized to have norm :attr:`max_norm`.
2024-11-01T17:51:30.0963020Z             norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
2024-11-01T17:51:30.0963505Z             scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
2024-11-01T17:51:30.0963869Z                                                     the words in the mini-batch. Default ``False``.
2024-11-01T17:51:30.0964147Z                                                     Note: this option is not supported when ``mode="max"``.
2024-11-01T17:51:30.0964539Z             mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
2024-11-01T17:51:30.0964866Z                                      ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
2024-11-01T17:51:30.0965199Z                                      into consideration. ``"mean"`` computes the average of the values
2024-11-01T17:51:30.0965481Z                                      in the bag, ``"max"`` computes the max value over each bag.
2024-11-01T17:51:30.0965649Z                                      Default: ``"mean"``
2024-11-01T17:51:30.0966151Z             sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
2024-11-01T17:51:30.0966530Z                                      Notes for more details regarding sparse gradients. Note: this option is not
2024-11-01T17:51:30.0966735Z                                      supported when ``mode="max"``.
2024-11-01T17:51:30.0967274Z             include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
2024-11-01T17:51:30.0967667Z                                           is equivalent to the size of `indices`. This matches the CSR format.
2024-11-01T17:51:30.0968152Z             padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
2024-11-01T17:51:30.0968565Z                                          gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
2024-11-01T17:51:30.0968935Z                                          during training, i.e. it remains as a fixed "pad". For a newly constructed
2024-11-01T17:51:30.0969347Z                                          EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
2024-11-01T17:51:30.0969757Z                                          zeros, but can be updated to another value to be used as the padding vector.
2024-11-01T17:51:30.0970144Z                                          Note that the embedding vector at :attr:`padding_idx` is excluded from the
2024-11-01T17:51:30.0970297Z                                          reduction.
2024-11-01T17:51:30.0970394Z     
2024-11-01T17:51:30.0970520Z         Attributes:
2024-11-01T17:51:30.0970980Z             weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
2024-11-01T17:51:30.0971211Z                              initialized from :math:`\mathcal{N}(0, 1)`.
2024-11-01T17:51:30.0971308Z     
2024-11-01T17:51:30.0971423Z         Examples::
2024-11-01T17:51:30.0971531Z     
2024-11-01T17:51:30.0971786Z             >>> # an EmbeddingBag module containing 10 tensors of size 3
2024-11-01T17:51:30.0972108Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
2024-11-01T17:51:30.0972295Z             >>> # a batch of 2 samples of 4 indices each
2024-11-01T17:51:30.0972591Z             >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
2024-11-01T17:51:30.0972806Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2024-11-01T17:51:30.0973084Z             >>> # xdoctest: +IGNORE_WANT("non-deterministic")
2024-11-01T17:51:30.0973254Z             >>> embedding_sum(input, offsets)
2024-11-01T17:51:30.0973469Z             tensor([[-0.8861, -5.4350, -0.0523],
2024-11-01T17:51:30.0973726Z                     [ 1.1306, -2.5798, -1.0044]])
2024-11-01T17:51:30.0973823Z     
2024-11-01T17:51:30.0974011Z             >>> # Example with padding_idx
2024-11-01T17:51:30.0974397Z             >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
2024-11-01T17:51:30.0974675Z             >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
2024-11-01T17:51:30.0974902Z             >>> offsets = torch.tensor([0, 4], dtype=torch.long)
2024-11-01T17:51:30.0975061Z             >>> embedding_sum(input, offsets)
2024-11-01T17:51:30.0975223Z             tensor([[ 0.0000,  0.0000,  0.0000],
2024-11-01T17:51:30.0975430Z                     [-0.7082,  3.2145, -2.6251]])
2024-11-01T17:51:30.0975539Z     
2024-11-01T17:51:30.0975801Z             >>> # An EmbeddingBag can be loaded from an Embedding like so
2024-11-01T17:51:30.0976014Z             >>> embedding = nn.Embedding(10, 3, padding_idx=2)
2024-11-01T17:51:30.0976252Z             >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
2024-11-01T17:51:30.0976388Z                     embedding.weight,
2024-11-01T17:51:30.0976587Z                     padding_idx=embedding.padding_idx,
2024-11-01T17:51:30.0976750Z                     mode='sum')
2024-11-01T17:51:30.0976865Z         
2024-11-01T17:51:30.0977294Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.0977391Z     
2024-11-01T17:51:30.0977524Z       warnings.warn(msg)
2024-11-01T17:51:30.0977620Z     
2024-11-01T17:51:30.0977867Z --- Parse Warning: 84 / 103 ---
2024-11-01T17:51:30.0979536Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=1748.
2024-11-01T17:51:30.0980003Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.0980104Z     
2024-11-01T17:51:30.0980442Z     Context manager for training with uneven inputs across processes in DDP.
2024-11-01T17:51:30.0980558Z     
2024-11-01T17:51:30.0980956Z     This context manager will keep track of already-joined DDP processes,
2024-11-01T17:51:30.0981279Z     and "shadow" the forward and backward passes by inserting collective
2024-11-01T17:51:30.0981680Z     communication operations to match with the ones created by non-joined
2024-11-01T17:51:30.0982031Z     DDP processes. This will ensure each collective call has a corresponding
2024-11-01T17:51:30.0982462Z     call by already-joined DDP processes, preventing hangs or errors that
2024-11-01T17:51:30.0982740Z     would otherwise happen when training with uneven inputs across
2024-11-01T17:51:30.0983080Z     processes. Alternatively, if the flag ``throw_on_early_termination`` is
2024-11-01T17:51:30.0983390Z     specified to be ``True``, all trainers will throw an error once one rank
2024-11-01T17:51:30.0983696Z     runs out of inputs, allowing these errors to be caught and handled
2024-11-01T17:51:30.0983841Z     according to application logic.
2024-11-01T17:51:30.0983953Z     
2024-11-01T17:51:30.0984270Z     Once all DDP processes have joined, the context manager will broadcast
2024-11-01T17:51:30.0984606Z     the model corresponding to the last joined process to all processes to
2024-11-01T17:51:30.0984812Z     ensure the model is the same across all processes
2024-11-01T17:51:30.0984949Z     (which is guaranteed by DDP).
2024-11-01T17:51:30.0985060Z     
2024-11-01T17:51:30.0985363Z     To use this to enable training with uneven inputs across processes,
2024-11-01T17:51:30.0985695Z     simply wrap this context manager around your training loop. No further
2024-11-01T17:51:30.0985933Z     modifications to the model or data loading is required.
2024-11-01T17:51:30.0986031Z     
2024-11-01T17:51:30.0986159Z     .. warning::
2024-11-01T17:51:30.0986476Z         If the model or training loop this context manager is wrapped around
2024-11-01T17:51:30.0986749Z         has additional distributed collective operations, such as
2024-11-01T17:51:30.0987149Z         ``SyncBatchNorm`` in the model's forward pass, then the flag
2024-11-01T17:51:30.0987456Z         ``throw_on_early_termination`` must be enabled. This is because this
2024-11-01T17:51:30.0987830Z         context manager is not aware of non-DDP collective communication.
2024-11-01T17:51:30.0988094Z         This flag will cause all ranks to throw when any one rank
2024-11-01T17:51:30.0988383Z         exhausts inputs, allowing these errors to be caught and recovered
2024-11-01T17:51:30.0988512Z         from across all ranks.
2024-11-01T17:51:30.0988622Z     
2024-11-01T17:51:30.0988723Z     Args:
2024-11-01T17:51:30.0988997Z         divide_by_initial_world_size (bool): If ``True``, will divide
2024-11-01T17:51:30.0989296Z             gradients by the initial ``world_size`` DDP training was launched
2024-11-01T17:51:30.0989538Z             with. If ``False``, will compute the effective world size
2024-11-01T17:51:30.0989825Z             (number of ranks that have not depleted their inputs yet) and
2024-11-01T17:51:30.0990036Z             divide gradients by that during allreduce. Set
2024-11-01T17:51:30.0990304Z             ``divide_by_initial_world_size=True`` to ensure every input
2024-11-01T17:51:30.0990601Z             sample including the uneven inputs have equal weight in terms of
2024-11-01T17:51:30.0990866Z             how much they contribute to the global gradient. This is
2024-11-01T17:51:30.0991114Z             achieved by always dividing the gradient by the initial
2024-11-01T17:51:30.0991399Z             ``world_size`` even when we encounter uneven inputs. If you set
2024-11-01T17:51:30.0991651Z             this to ``False``, we divide the gradient by the remaining
2024-11-01T17:51:30.0991973Z             number of nodes. This ensures parity with training on a smaller
2024-11-01T17:51:30.0992251Z             ``world_size`` although it also means the uneven inputs would
2024-11-01T17:51:30.0992527Z             contribute more towards the global gradient. Typically, you
2024-11-01T17:51:30.0992824Z             would want to set this to ``True`` for cases where the last few
2024-11-01T17:51:30.0993110Z             inputs of your training job are uneven. In extreme cases, where
2024-11-01T17:51:30.0993401Z             there is a large discrepancy in the number of inputs, setting
2024-11-01T17:51:30.0993606Z             this to ``False`` might provide better results.
2024-11-01T17:51:30.0994043Z         enable (bool): Whether to enable uneven input detection or not. Pass
2024-11-01T17:51:30.0994359Z             in ``enable=False`` to disable in cases where you know that
2024-11-01T17:51:30.0994632Z             inputs are even across participating processes. Default is
2024-11-01T17:51:30.0994757Z             ``True``.
2024-11-01T17:51:30.0995019Z         throw_on_early_termination (bool): Whether to throw an error
2024-11-01T17:51:30.0995288Z             or continue training when at least one rank has exhausted
2024-11-01T17:51:30.0995567Z             inputs. If ``True``, will throw upon the first rank reaching end
2024-11-01T17:51:30.0995843Z             of data. If ``False``, will continue training with a smaller
2024-11-01T17:51:30.0996121Z             effective world size until all ranks are joined. Note that if
2024-11-01T17:51:30.0996292Z             this flag is specified, then the flag
2024-11-01T17:51:30.0996554Z             ``divide_by_initial_world_size`` would be ignored. Default
2024-11-01T17:51:30.0996669Z             is ``False``.
2024-11-01T17:51:30.0996781Z     
2024-11-01T17:51:30.0996878Z     
2024-11-01T17:51:30.0996992Z     Example::
2024-11-01T17:51:30.0997102Z     
2024-11-01T17:51:30.0997263Z         >>> # xdoctest: +SKIP("Distributed")
2024-11-01T17:51:30.0997395Z         >>> import torch
2024-11-01T17:51:30.0997558Z         >>> import torch.distributed as dist
2024-11-01T17:51:30.0997682Z         >>> import os
2024-11-01T17:51:30.0997858Z         >>> import torch.multiprocessing as mp
2024-11-01T17:51:30.0998021Z         >>> import torch.nn as nn
2024-11-01T17:51:30.0998169Z         >>> # On each spawned worker
2024-11-01T17:51:30.0998336Z         >>> def worker(rank):
2024-11-01T17:51:30.0998607Z         >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
2024-11-01T17:51:30.0998759Z         >>>     torch.cuda.set_device(rank)
2024-11-01T17:51:30.0998969Z         >>>     model = nn.Linear(1, 1, bias=False).to(rank)
2024-11-01T17:51:30.0999230Z         >>>     model = torch.nn.parallel.DistributedDataParallel(
2024-11-01T17:51:30.0999435Z         >>>         model, device_ids=[rank], output_device=rank
2024-11-01T17:51:30.0999555Z         >>>     )
2024-11-01T17:51:30.0999744Z         >>>     # Rank 1 gets one more input than rank 0.
2024-11-01T17:51:30.1000034Z         >>>     inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
2024-11-01T17:51:30.1000162Z         >>>     with model.join():
2024-11-01T17:51:30.1000293Z         >>>         for _ in range(5):
2024-11-01T17:51:30.1000447Z         >>>             for inp in inputs:
2024-11-01T17:51:30.1000610Z         >>>                 loss = model(inp).sum()
2024-11-01T17:51:30.1000767Z         >>>                 loss.backward()
2024-11-01T17:51:30.1001046Z         >>>     # Without the join() API, the below synchronization will hang
2024-11-01T17:51:30.1001350Z         >>>     # blocking for rank 1's allreduce to complete.
2024-11-01T17:51:30.1001527Z         >>>     torch.cuda.synchronize(device=rank)
2024-11-01T17:51:30.1001627Z     
2024-11-01T17:51:30.1002069Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1002166Z     
2024-11-01T17:51:30.1002298Z       warnings.warn(msg)
2024-11-01T17:51:30.1002393Z     
2024-11-01T17:51:30.1002673Z --- Parse Warning: 85 / 103 ---
2024-11-01T17:51:30.1004410Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/parallel/distributed.py line=2039.
2024-11-01T17:51:30.1004878Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1004975Z     
2024-11-01T17:51:30.1005421Z     Register an optimizer in DDP to optimize parameter immediately after its gradient reduction.
2024-11-01T17:51:30.1005532Z     
2024-11-01T17:51:30.1005828Z     Registers an optimizer with DDP such that the optimization for a
2024-11-01T17:51:30.1006245Z     parameter will run immediately when that parameter's gradient is
2024-11-01T17:51:30.1006796Z     finished with reduction, instead of waiting for all parameters'
2024-11-01T17:51:30.1007124Z     gradients to finish reduction. This can result in a training speedup
2024-11-01T17:51:30.1007443Z     depending on your workload since the optimizer can run while gradient
2024-11-01T17:51:30.1007771Z     reduction for other parameters are still ongoing. In addition, this has
2024-11-01T17:51:30.1008106Z     the potential to reduce peak memory consumption during training, as it
2024-11-01T17:51:30.1008478Z     only needs to load the per-parameter optimizer states of a single
2024-11-01T17:51:30.1008873Z     parameter at a time, instead of loading all per-parameter optimizer
2024-11-01T17:51:30.1008987Z     states at once.
2024-11-01T17:51:30.1009097Z     
2024-11-01T17:51:30.1009199Z     Args:
2024-11-01T17:51:30.1009485Z         optim (Type): a ``torch.optim.Optimizer`` class to be registered
2024-11-01T17:51:30.1009627Z         as a fused optimizer.
2024-11-01T17:51:30.1009860Z         *args (Sequence[Any]): Arguments to forward to `optim`.
2024-11-01T17:51:30.1010173Z         optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters
2024-11-01T17:51:30.1010495Z         to optimize, similar to `params` argument of traditional `torch.optim`
2024-11-01T17:51:30.1010805Z         Optimizers. If this is omitted, all DDP model parameters will be
2024-11-01T17:51:30.1010917Z         optimized.
2024-11-01T17:51:30.1011324Z         **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`.
2024-11-01T17:51:30.1011434Z     
2024-11-01T17:51:30.1011590Z     .. warning ::
2024-11-01T17:51:30.1011905Z         _register_fused_optim should only be called once on a DDP instance,
2024-11-01T17:51:30.1012198Z         and registering multiple fused optimizers for the same DDP model
2024-11-01T17:51:30.1012390Z         is not currently supported. Please ping
2024-11-01T17:51:30.1012719Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2024-11-01T17:51:30.1012844Z         for your use case.
2024-11-01T17:51:30.1012955Z     
2024-11-01T17:51:30.1013065Z     .. warning ::
2024-11-01T17:51:30.1013347Z         _register_fused_optim and register_comm_hook currently do not
2024-11-01T17:51:30.1013649Z         compose together, meaning that custom DDP communication hooks are
2024-11-01T17:51:30.1013889Z         not supported with overlapped optimizers. Please ping
2024-11-01T17:51:30.1014224Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2024-11-01T17:51:30.1014348Z         for your use case.
2024-11-01T17:51:30.1014457Z     
2024-11-01T17:51:30.1014568Z     .. warning ::
2024-11-01T17:51:30.1014894Z         Gradient accumulation and DDP `no_sync` are currently not supported
2024-11-01T17:51:30.1015069Z         with overlapped optimizer. Please ping
2024-11-01T17:51:30.1015385Z         https://github.com/pytorch/pytorch/issues/71595 if this is necessary
2024-11-01T17:51:30.1015519Z         for your use case.
2024-11-01T17:51:30.1015613Z     
2024-11-01T17:51:30.1015738Z     Example::
2024-11-01T17:51:30.1015834Z     
2024-11-01T17:51:30.1016039Z         >>> # xdoctest: +SKIP("No rendezvous handler")
2024-11-01T17:51:30.1016596Z         >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
2024-11-01T17:51:30.1016895Z         >>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
2024-11-01T17:51:30.1017041Z         >>> lr = 1e-2
2024-11-01T17:51:30.1017164Z         >>> betas = (0.9, 0.99)
2024-11-01T17:51:30.1017308Z         >>> eps = 1e-6
2024-11-01T17:51:30.1017625Z         >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps)
2024-11-01T17:51:30.1017811Z         >>> # Example with subset of parameters
2024-11-01T17:51:30.1018005Z         >>> params_to_opt = [list(net.parameters())[0]]
2024-11-01T17:51:30.1018143Z         >>> net._register_fused_optim(
2024-11-01T17:51:30.1018498Z         ...   torch.optim.Adam, lr, optim_params=params_to_opt,  betas=betas, eps=eps
2024-11-01T17:51:30.1018650Z         ... )
2024-11-01T17:51:30.1018761Z     
2024-11-01T17:51:30.1019198Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1019312Z     
2024-11-01T17:51:30.1019433Z       warnings.warn(msg)
2024-11-01T17:51:30.1019528Z     
2024-11-01T17:51:30.1019774Z --- Parse Warning: 86 / 103 ---
2024-11-01T17:51:30.1021388Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=6.
2024-11-01T17:51:30.1021851Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1022157Z     Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``.
2024-11-01T17:51:30.1022267Z     
2024-11-01T17:51:30.1022661Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2024-11-01T17:51:30.1023068Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2024-11-01T17:51:30.1023467Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2024-11-01T17:51:30.1023917Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2024-11-01T17:51:30.1024029Z     
2024-11-01T17:51:30.1024170Z         .. note::
2024-11-01T17:51:30.1024549Z             Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive
2024-11-01T17:51:30.1024873Z             than the utility function ``convert_conv2d_weight_memory_format``. Any
2024-11-01T17:51:30.1025194Z             layer with 4d weight will be affected by ``model.to``, which does not
2024-11-01T17:51:30.1025521Z             necessarily benefit from conversion to specified ``memory_format``.
2024-11-01T17:51:30.1025857Z             One place we are confident in is that NHWC(channels_last) conversion for
2024-11-01T17:51:30.1026191Z             convolution in cuDNN, as it is beneficial to run convolution in NHWC,
2024-11-01T17:51:30.1026497Z             even in cases where we have to apply permutation to input tensors.
2024-11-01T17:51:30.1026611Z     
2024-11-01T17:51:30.1026944Z             Hence our strategy here is to convert only the weight of convolution to
2024-11-01T17:51:30.1027119Z             channels_last. This ensures that;
2024-11-01T17:51:30.1027433Z             1. Fast convolution kernels will be used, the benefit of which could
2024-11-01T17:51:30.1027765Z             outweigh overhead of permutation (if input is not in the same format).
2024-11-01T17:51:30.1028117Z             2. No unnecessary permutations are applied on layers that do not benefit
2024-11-01T17:51:30.1028266Z             from memory_format conversion.
2024-11-01T17:51:30.1028376Z     
2024-11-01T17:51:30.1028715Z             The optimal case is that, layers between convolution layers are channels
2024-11-01T17:51:30.1029070Z             last compatible. Input tensor would be permuted to channels last when it
2024-11-01T17:51:30.1029397Z             encounters the first convolution layer and stay in that memory format.
2024-11-01T17:51:30.1029770Z             Hence following convolutions will not need to permute its input tensor.
2024-11-01T17:51:30.1029882Z     
2024-11-01T17:51:30.1030215Z             In case where a channels last incompatible layer is between convolution
2024-11-01T17:51:30.1030550Z             layers, we need to permute the input tensor back to contiguous format
2024-11-01T17:51:30.1030889Z             for that layer. The input tensor will go through the remaining layers in
2024-11-01T17:51:30.1031229Z             contiguous format and be permuted to channels last when it encounters
2024-11-01T17:51:30.1031609Z             another convolution layer. There's no point in propagating that
2024-11-01T17:51:30.1031929Z             permutation to an earlier layer, as most layers are quite agnostic to
2024-11-01T17:51:30.1032096Z             ``memory_format``.
2024-11-01T17:51:30.1032192Z     
2024-11-01T17:51:30.1032547Z             This claim might change when PyTorch supports fusion of permutation, as
2024-11-01T17:51:30.1032879Z             there might have been a better spot to fuse the permutation other than
2024-11-01T17:51:30.1033057Z             immediately before a convolution.
2024-11-01T17:51:30.1033154Z     
2024-11-01T17:51:30.1033261Z         Args:
2024-11-01T17:51:30.1033591Z             module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container
2024-11-01T17:51:30.1033737Z                                 ``nn.Module``
2024-11-01T17:51:30.1034076Z             memory_format: user specified ``memory_format``,
2024-11-01T17:51:30.1034343Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2024-11-01T17:51:30.1034455Z     
2024-11-01T17:51:30.1034560Z         Returns:
2024-11-01T17:51:30.1034770Z             The original module with updated ``nn.Conv2d``
2024-11-01T17:51:30.1034879Z     
2024-11-01T17:51:30.1034983Z         Example:
2024-11-01T17:51:30.1035200Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:30.1035425Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2024-11-01T17:51:30.1035791Z             >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda")
2024-11-01T17:51:30.1035933Z             >>> model = nn.Sequential(
2024-11-01T17:51:30.1036146Z             >>>     nn.Conv2d(8, 4, 3)).cuda().half()
2024-11-01T17:51:30.1036327Z             >>> # This is identical to:
2024-11-01T17:51:30.1036673Z             >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2024-11-01T17:51:30.1037065Z             >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last)
2024-11-01T17:51:30.1037193Z             >>> out = model(input)
2024-11-01T17:51:30.1037309Z         
2024-11-01T17:51:30.1037751Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1037847Z     
2024-11-01T17:51:30.1037983Z       warnings.warn(msg)
2024-11-01T17:51:30.1038078Z     
2024-11-01T17:51:30.1038326Z --- Parse Warning: 87 / 103 ---
2024-11-01T17:51:30.1039944Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/memory_format.py line=81.
2024-11-01T17:51:30.1040410Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1040707Z     Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format``
2024-11-01T17:51:30.1041095Z         The conversion recursively applies to nested ``nn.Module``, including ``module``.
2024-11-01T17:51:30.1041521Z         Note that it only changes the memory_format, but not the semantics of each dimensions.
2024-11-01T17:51:30.1041905Z         This function is used to facilitate the computation to adopt NHWC kernels, which
2024-11-01T17:51:30.1042368Z         provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0
2024-11-01T17:51:30.1042563Z     
2024-11-01T17:51:30.1042694Z         .. note::
2024-11-01T17:51:30.1043046Z             Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive
2024-11-01T17:51:30.1043383Z             than the utility function ``convert_conv3d_weight_memory_format``. Any
2024-11-01T17:51:30.1043707Z             layer with 4d weight will be affected by ``model.to``, which does not
2024-11-01T17:51:30.1044017Z             necessarily benefit from conversion to specified ``memory_format``.
2024-11-01T17:51:30.1044390Z             One place we are confident in is that NDHWC(channels_last_3d) conversion for
2024-11-01T17:51:30.1044714Z             convolution in cuDNN, as it is beneficial to run convolution in NDHWC,
2024-11-01T17:51:30.1045070Z             even in cases where we have to apply permutation to input tensors.
2024-11-01T17:51:30.1045166Z     
2024-11-01T17:51:30.1045516Z             Hence our strategy here is to convert only the weight of convolution to
2024-11-01T17:51:30.1045686Z             channels_last_3d. This ensures that;
2024-11-01T17:51:30.1045998Z             1. Fast convolution kernels will be used, the benefit of which could
2024-11-01T17:51:30.1046342Z             outweigh overhead of permutation (if input is not in the same format).
2024-11-01T17:51:30.1046684Z             2. No unnecessary permutations are applied on layers that do not benefit
2024-11-01T17:51:30.1046849Z             from memory_format conversion.
2024-11-01T17:51:30.1046946Z     
2024-11-01T17:51:30.1047301Z             The optimal case is that, layers between convolution layers are channels
2024-11-01T17:51:30.1047641Z             last compatible. Input tensor would be permuted to channels last when it
2024-11-01T17:51:30.1047983Z             encounters the first convolution layer and stay in that memory format.
2024-11-01T17:51:30.1048320Z             Hence following convolutions will not need to permute its input tensor.
2024-11-01T17:51:30.1048417Z     
2024-11-01T17:51:30.1048764Z             In case where a channels last incompatible layer is between convolution
2024-11-01T17:51:30.1049081Z             layers, we need to permute the input tensor back to contiguous format
2024-11-01T17:51:30.1049429Z             for that layer. The input tensor will go through the remaining layers in
2024-11-01T17:51:30.1049842Z             contiguous format and be permuted to channels last when it encounters
2024-11-01T17:51:30.1050237Z             another convolution layer. There's no point in propagating that
2024-11-01T17:51:30.1050559Z             permutation to an earlier layer, as most layers are quite agnostic to
2024-11-01T17:51:30.1050686Z             ``memory_format``.
2024-11-01T17:51:30.1050799Z     
2024-11-01T17:51:30.1051142Z             This claim might change when PyTorch supports fusion of permutation, as
2024-11-01T17:51:30.1051485Z             there might have been a better spot to fuse the permutation other than
2024-11-01T17:51:30.1051652Z             immediately before a convolution.
2024-11-01T17:51:30.1051764Z     
2024-11-01T17:51:30.1051868Z         Args:
2024-11-01T17:51:30.1052187Z             module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container
2024-11-01T17:51:30.1052344Z                                 ``nn.Module``
2024-11-01T17:51:30.1052558Z             memory_format: user specified ``memory_format``,
2024-11-01T17:51:30.1052839Z                 e.g. ``torch.channels_last`` or ``torch.contiguous_format``
2024-11-01T17:51:30.1052937Z     
2024-11-01T17:51:30.1053058Z         Returns:
2024-11-01T17:51:30.1053268Z             The original module with updated ``nn.Conv3d``
2024-11-01T17:51:30.1053365Z     
2024-11-01T17:51:30.1053486Z         Example:
2024-11-01T17:51:30.1053691Z             >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
2024-11-01T17:51:30.1053929Z             >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG)
2024-11-01T17:51:30.1054292Z             >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda")
2024-11-01T17:51:30.1054496Z             >>> model = nn.Sequential(
2024-11-01T17:51:30.1054682Z             >>>     nn.Conv3d(8, 4, 3)).cuda().half()
2024-11-01T17:51:30.1054821Z             >>> # This is identical to:
2024-11-01T17:51:30.1055200Z             >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2024-11-01T17:51:30.1055593Z             >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d)
2024-11-01T17:51:30.1055734Z             >>> out = model(input)
2024-11-01T17:51:30.1055836Z         
2024-11-01T17:51:30.1056271Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1056381Z     
2024-11-01T17:51:30.1056542Z       warnings.warn(msg)
2024-11-01T17:51:30.1056654Z     
2024-11-01T17:51:30.1056887Z --- Parse Warning: 88 / 103 ---
2024-11-01T17:51:30.1058400Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=936.
2024-11-01T17:51:30.1058848Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1059196Z     Prune tensor by removing random channels along the specified dimension.
2024-11-01T17:51:30.1059297Z     
2024-11-01T17:51:30.1059622Z         Prunes tensor corresponding to parameter called ``name`` in ``module``
2024-11-01T17:51:30.1059949Z         by removing the specified ``amount`` of (currently unpruned) channels
2024-11-01T17:51:30.1060148Z         along the specified ``dim`` selected at random.
2024-11-01T17:51:30.1060446Z         Modifies module in place (and also return the modified module)
2024-11-01T17:51:30.1060553Z         by:
2024-11-01T17:51:30.1060667Z     
2024-11-01T17:51:30.1061059Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:30.1061376Z            binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:30.1061699Z         2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:30.1061994Z            original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:30.1062214Z            ``name+'_orig'``.
2024-11-01T17:51:30.1062310Z     
2024-11-01T17:51:30.1062460Z         Args:
2024-11-01T17:51:30.1062719Z             module (nn.Module): module containing the tensor to prune
2024-11-01T17:51:30.1062987Z             name (str): parameter name within ``module`` on which pruning
2024-11-01T17:51:30.1063116Z                     will act.
2024-11-01T17:51:30.1063362Z             amount (int or float): quantity of parameters to prune.
2024-11-01T17:51:30.1063640Z                 If ``float``, should be between 0.0 and 1.0 and represent the
2024-11-01T17:51:30.1063929Z                 fraction of parameters to prune. If ``int``, it represents the
2024-11-01T17:51:30.1064133Z                 absolute number of parameters to prune.
2024-11-01T17:51:30.1064435Z             dim (int): index of the dim along which we define channels to prune.
2024-11-01T17:51:30.1064533Z     
2024-11-01T17:51:30.1064655Z         Returns:
2024-11-01T17:51:30.1064979Z             module (nn.Module): modified (i.e. pruned) version of the input module
2024-11-01T17:51:30.1065088Z     
2024-11-01T17:51:30.1065200Z         Examples:
2024-11-01T17:51:30.1065330Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:30.1065502Z             >>> m = prune.random_structured(
2024-11-01T17:51:30.1065772Z             ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
2024-11-01T17:51:30.1065892Z             ... )
2024-11-01T17:51:30.1066151Z             >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
2024-11-01T17:51:30.1066303Z             >>> print(columns_pruned)
2024-11-01T17:51:30.1066407Z             3
2024-11-01T17:51:30.1066507Z         
2024-11-01T17:51:30.1066988Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1067085Z     
2024-11-01T17:51:30.1067222Z       warnings.warn(msg)
2024-11-01T17:51:30.1067319Z     
2024-11-01T17:51:30.1067549Z --- Parse Warning: 89 / 103 ---
2024-11-01T17:51:30.1069004Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=977.
2024-11-01T17:51:30.1069448Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1070010Z     Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension.
2024-11-01T17:51:30.1070147Z     
2024-11-01T17:51:30.1070490Z         Prunes tensor corresponding to parameter called ``name`` in ``module``
2024-11-01T17:51:30.1070808Z         by removing the specified ``amount`` of (currently unpruned) channels
2024-11-01T17:51:30.1071148Z         along the specified ``dim`` with the lowest L\ ``n``-norm.
2024-11-01T17:51:30.1071434Z         Modifies module in place (and also return the modified module)
2024-11-01T17:51:30.1071540Z         by:
2024-11-01T17:51:30.1071650Z     
2024-11-01T17:51:30.1072040Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:30.1072372Z            binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:30.1072685Z         2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:30.1072988Z            original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:30.1073148Z            ``name+'_orig'``.
2024-11-01T17:51:30.1073245Z     
2024-11-01T17:51:30.1073365Z         Args:
2024-11-01T17:51:30.1073622Z             module (nn.Module): module containing the tensor to prune
2024-11-01T17:51:30.1074012Z             name (str): parameter name within ``module`` on which pruning
2024-11-01T17:51:30.1074134Z                     will act.
2024-11-01T17:51:30.1074392Z             amount (int or float): quantity of parameters to prune.
2024-11-01T17:51:30.1074651Z                 If ``float``, should be between 0.0 and 1.0 and represent the
2024-11-01T17:51:30.1074987Z                 fraction of parameters to prune. If ``int``, it represents the
2024-11-01T17:51:30.1075219Z                 absolute number of parameters to prune.
2024-11-01T17:51:30.1075598Z             n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
2024-11-01T17:51:30.1075833Z                 entries for argument ``p`` in :func:`torch.norm`.
2024-11-01T17:51:30.1076140Z             dim (int): index of the dim along which we define channels to prune.
2024-11-01T17:51:30.1076488Z             importance_scores (torch.Tensor): tensor of importance scores (of same
2024-11-01T17:51:30.1076767Z                 shape as module parameter) used to compute mask for pruning.
2024-11-01T17:51:30.1077113Z                 The values in this tensor indicate the importance of the corresponding
2024-11-01T17:51:30.1077301Z                 elements in the parameter being pruned.
2024-11-01T17:51:30.1077641Z                 If unspecified or None, the module parameter will be used in its place.
2024-11-01T17:51:30.1077756Z     
2024-11-01T17:51:30.1077863Z         Returns:
2024-11-01T17:51:30.1078208Z             module (nn.Module): modified (i.e. pruned) version of the input module
2024-11-01T17:51:30.1078305Z     
2024-11-01T17:51:30.1078412Z         Examples:
2024-11-01T17:51:30.1078597Z             >>> from torch.nn.utils import prune
2024-11-01T17:51:30.1078742Z             >>> m = prune.ln_structured(
2024-11-01T17:51:30.1079129Z             ...     nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf')
2024-11-01T17:51:30.1079236Z             ... )
2024-11-01T17:51:30.1079356Z         
2024-11-01T17:51:30.1079785Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1079885Z     
2024-11-01T17:51:30.1080061Z       warnings.warn(msg)
2024-11-01T17:51:30.1080158Z     
2024-11-01T17:51:30.1080406Z --- Parse Warning: 90 / 103 ---
2024-11-01T17:51:30.1081891Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1024.
2024-11-01T17:51:30.1082354Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1082452Z     
2024-11-01T17:51:30.1083045Z     Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``.
2024-11-01T17:51:30.1083155Z     
2024-11-01T17:51:30.1083330Z     Modifies modules in place by:
2024-11-01T17:51:30.1083440Z     
2024-11-01T17:51:30.1083827Z     1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:30.1084159Z        binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:30.1084463Z     2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:30.1084756Z        original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:30.1084927Z        ``name+'_orig'``.
2024-11-01T17:51:30.1085024Z     
2024-11-01T17:51:30.1085139Z     Args:
2024-11-01T17:51:30.1085419Z         parameters (Iterable of (module, name) tuples): parameters of
2024-11-01T17:51:30.1085722Z             the model to prune in a global fashion, i.e. by aggregating all
2024-11-01T17:51:30.1086017Z             weights prior to deciding which ones to prune. module must be of
2024-11-01T17:51:30.1086235Z             type :class:`nn.Module`, and name must be a string.
2024-11-01T17:51:30.1086564Z         pruning_method (function): a valid pruning function from this module,
2024-11-01T17:51:30.1086870Z             or a custom one implemented by the user that satisfies the
2024-11-01T17:51:30.1087283Z             implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
2024-11-01T17:51:30.1087603Z         importance_scores (dict): a dictionary mapping (module, name) tuples to
2024-11-01T17:51:30.1088005Z             the corresponding parameter's importance scores tensor. The tensor
2024-11-01T17:51:30.1088361Z             should be the same shape as the parameter, and is used for computing
2024-11-01T17:51:30.1088526Z             mask for pruning.
2024-11-01T17:51:30.1088833Z             If unspecified or None, the parameter will be used in place of its
2024-11-01T17:51:30.1088960Z             importance scores.
2024-11-01T17:51:30.1089150Z         kwargs: other keyword arguments such as:
2024-11-01T17:51:30.1089443Z             amount (int or float): quantity of parameters to prune across the
2024-11-01T17:51:30.1089588Z             specified parameters.
2024-11-01T17:51:30.1089847Z             If ``float``, should be between 0.0 and 1.0 and represent the
2024-11-01T17:51:30.1090154Z             fraction of parameters to prune. If ``int``, it represents the
2024-11-01T17:51:30.1090332Z             absolute number of parameters to prune.
2024-11-01T17:51:30.1090442Z     
2024-11-01T17:51:30.1090543Z     Raises:
2024-11-01T17:51:30.1090820Z         TypeError: if ``PRUNING_TYPE != 'unstructured'``
2024-11-01T17:51:30.1090929Z     
2024-11-01T17:51:30.1091029Z     Note:
2024-11-01T17:51:30.1091420Z         Since global structured pruning doesn't make much sense unless the
2024-11-01T17:51:30.1091716Z         norm is normalized by the size of the parameter, we now limit the
2024-11-01T17:51:30.1091927Z         scope of global pruning to unstructured methods.
2024-11-01T17:51:30.1092088Z     
2024-11-01T17:51:30.1092194Z     Examples:
2024-11-01T17:51:30.1092370Z         >>> from torch.nn.utils import prune
2024-11-01T17:51:30.1092543Z         >>> from collections import OrderedDict
2024-11-01T17:51:30.1092718Z         >>> net = nn.Sequential(OrderedDict([
2024-11-01T17:51:30.1092921Z         ...     ('first', nn.Linear(10, 4)),
2024-11-01T17:51:30.1093159Z         ...     ('second', nn.Linear(4, 1)),
2024-11-01T17:51:30.1093272Z         ... ]))
2024-11-01T17:51:30.1093405Z         >>> parameters_to_prune = (
2024-11-01T17:51:30.1093599Z         ...     (net.first, 'weight'),
2024-11-01T17:51:30.1093782Z         ...     (net.second, 'weight'),
2024-11-01T17:51:30.1093894Z         ... )
2024-11-01T17:51:30.1094037Z         >>> prune.global_unstructured(
2024-11-01T17:51:30.1094167Z         ...     parameters_to_prune,
2024-11-01T17:51:30.1094366Z         ...     pruning_method=prune.L1Unstructured,
2024-11-01T17:51:30.1094479Z         ...     amount=10,
2024-11-01T17:51:30.1094591Z         ... )
2024-11-01T17:51:30.1094912Z         >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
2024-11-01T17:51:30.1095077Z         tensor(10)
2024-11-01T17:51:30.1095192Z     
2024-11-01T17:51:30.1095290Z     
2024-11-01T17:51:30.1095735Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1095836Z     
2024-11-01T17:51:30.1095970Z       warnings.warn(msg)
2024-11-01T17:51:30.1096065Z     
2024-11-01T17:51:30.1096294Z --- Parse Warning: 91 / 103 ---
2024-11-01T17:51:30.1097776Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/utils/prune.py line=1143.
2024-11-01T17:51:30.1098222Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1098934Z     Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``.
2024-11-01T17:51:30.1099031Z     
2024-11-01T17:51:30.1099352Z         Modifies module in place (and also return the modified module) by:
2024-11-01T17:51:30.1099450Z     
2024-11-01T17:51:30.1099841Z         1) adding a named buffer called ``name+'_mask'`` corresponding to the
2024-11-01T17:51:30.1100173Z            binary mask applied to the parameter ``name`` by the pruning method.
2024-11-01T17:51:30.1100481Z         2) replacing the parameter ``name`` by its pruned version, while the
2024-11-01T17:51:30.1100790Z            original (unpruned) parameter is stored in a new parameter named
2024-11-01T17:51:30.1100988Z            ``name+'_orig'``.
2024-11-01T17:51:30.1101099Z     
2024-11-01T17:51:30.1101232Z         Args:
2024-11-01T17:51:30.1101490Z             module (nn.Module): module containing the tensor to prune
2024-11-01T17:51:30.1101768Z             name (str): parameter name within ``module`` on which pruning
2024-11-01T17:51:30.1101883Z                 will act.
2024-11-01T17:51:30.1102157Z             mask (Tensor): binary mask to be applied to the parameter.
2024-11-01T17:51:30.1102256Z     
2024-11-01T17:51:30.1102377Z         Returns:
2024-11-01T17:51:30.1102704Z             module (nn.Module): modified (i.e. pruned) version of the input module
2024-11-01T17:51:30.1102829Z     
2024-11-01T17:51:30.1102955Z         Examples:
2024-11-01T17:51:30.1103158Z             >>> from torch.nn.utils import prune
2024-11-01T17:51:30.1103322Z             >>> m = prune.custom_from_mask(
2024-11-01T17:51:30.1103668Z             ...     nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0])
2024-11-01T17:51:30.1103775Z             ... )
2024-11-01T17:51:30.1103917Z             >>> print(m.bias_mask)
2024-11-01T17:51:30.1104036Z             tensor([0., 1., 0.])
2024-11-01T17:51:30.1104147Z     
2024-11-01T17:51:30.1104248Z         
2024-11-01T17:51:30.1104686Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1104784Z     
2024-11-01T17:51:30.1104905Z       warnings.warn(msg)
2024-11-01T17:51:30.1105016Z     
2024-11-01T17:51:30.1105243Z --- Parse Warning: 92 / 103 ---
2024-11-01T17:51:30.1107107Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=116.
2024-11-01T17:51:30.1107562Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1108085Z     Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).
2024-11-01T17:51:30.1108185Z     
2024-11-01T17:51:30.1108529Z         Stochastic Weight Averaging was proposed in `Averaging Weights Leads to
2024-11-01T17:51:30.1108855Z         Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii
2024-11-01T17:51:30.1109158Z         Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson
2024-11-01T17:51:30.1109282Z         (UAI 2018).
2024-11-01T17:51:30.1109379Z     
2024-11-01T17:51:30.1109749Z         Exponential Moving Average is a variation of `Polyak averaging`_,
2024-11-01T17:51:30.1110094Z         but using exponential weights instead of equal weights across iterations.
2024-11-01T17:51:30.1110188Z     
2024-11-01T17:51:30.1110539Z         AveragedModel class creates a copy of the provided module :attr:`model`
2024-11-01T17:51:30.1110874Z         on the device :attr:`device` and allows to compute running averages of the
2024-11-01T17:51:30.1111038Z         parameters of the :attr:`model`.
2024-11-01T17:51:30.1111134Z     
2024-11-01T17:51:30.1111250Z         Args:
2024-11-01T17:51:30.1111476Z             model (torch.nn.Module): model to use with SWA/EMA
2024-11-01T17:51:30.1111815Z             device (torch.device, optional): if provided, the averaged model will be
2024-11-01T17:51:30.1111979Z                 stored on the :attr:`device`
2024-11-01T17:51:30.1112273Z             avg_fn (function, optional): the averaging function used to update
2024-11-01T17:51:30.1112576Z                 parameters; the function must take in the current value of the
2024-11-01T17:51:30.1112882Z                 :class:`AveragedModel` parameter, the current value of :attr:`model`
2024-11-01T17:51:30.1113183Z                 parameter, and the number of models already averaged; if None,
2024-11-01T17:51:30.1113412Z                 an equally weighted average is used (default: None)
2024-11-01T17:51:30.1113734Z             multi_avg_fn (function, optional): the averaging function used to update
2024-11-01T17:51:30.1114227Z                 parameters inplace; the function must take in the current values of the
2024-11-01T17:51:30.1114662Z                 :class:`AveragedModel` parameters as a list, the current values of :attr:`model`
2024-11-01T17:51:30.1115026Z                 parameters as a list, and the number of models already averaged; if None,
2024-11-01T17:51:30.1115259Z                 an equally weighted average is used (default: None)
2024-11-01T17:51:30.1115578Z             use_buffers (bool): if ``True``, it will compute running averages for
2024-11-01T17:51:30.1115911Z                 both the parameters and the buffers of the model. (default: ``False``)
2024-11-01T17:51:30.1116017Z     
2024-11-01T17:51:30.1116125Z         Example:
2024-11-01T17:51:30.1121626Z             >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.1121869Z             >>> loader, optimizer, model, loss_fn = ...
2024-11-01T17:51:30.1122128Z             >>> swa_model = torch.optim.swa_utils.AveragedModel(model)
2024-11-01T17:51:30.1122471Z             >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
2024-11-01T17:51:30.1122649Z             >>>                                     T_max=300)
2024-11-01T17:51:30.1122775Z             >>> swa_start = 160
2024-11-01T17:51:30.1122998Z             >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05)
2024-11-01T17:51:30.1123130Z             >>> for i in range(300):
2024-11-01T17:51:30.1123309Z             >>>      for input, target in loader:
2024-11-01T17:51:30.1123472Z             >>>          optimizer.zero_grad()
2024-11-01T17:51:30.1123684Z             >>>          loss_fn(model(input), target).backward()
2024-11-01T17:51:30.1123826Z             >>>          optimizer.step()
2024-11-01T17:51:30.1124046Z             >>>      if i > swa_start:
2024-11-01T17:51:30.1124252Z             >>>          swa_model.update_parameters(model)
2024-11-01T17:51:30.1124409Z             >>>          swa_scheduler.step()
2024-11-01T17:51:30.1124538Z             >>>      else:
2024-11-01T17:51:30.1124682Z             >>>          scheduler.step()
2024-11-01T17:51:30.1124786Z             >>>
2024-11-01T17:51:30.1125035Z             >>> # Update bn statistics for the swa_model at the end
2024-11-01T17:51:30.1125269Z             >>> torch.optim.swa_utils.update_bn(loader, swa_model)
2024-11-01T17:51:30.1125384Z     
2024-11-01T17:51:30.1125833Z         You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters.
2024-11-01T17:51:30.1126177Z         If no averaging function is provided, the default is to compute
2024-11-01T17:51:30.1126503Z         equally-weighted average of the weights (SWA).
2024-11-01T17:51:30.1126601Z     
2024-11-01T17:51:30.1126725Z         Example:
2024-11-01T17:51:30.1126919Z             >>> # xdoctest: +SKIP("undefined variables")
2024-11-01T17:51:30.1127229Z             >>> # Compute exponential moving averages of the weights and buffers
2024-11-01T17:51:30.1127478Z             >>> ema_model = torch.optim.swa_utils.AveragedModel(model,
2024-11-01T17:51:30.1127818Z             >>>             torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True)
2024-11-01T17:51:30.1127916Z     
2024-11-01T17:51:30.1128050Z         .. note::
2024-11-01T17:51:30.1128393Z             When using SWA/EMA with models containing Batch Normalization you may
2024-11-01T17:51:30.1128696Z             need to update the activation statistics for Batch Normalization.
2024-11-01T17:51:30.1129074Z             This can be done either by using the :meth:`torch.optim.swa_utils.update_bn`
2024-11-01T17:51:30.1129420Z             or by setting :attr:`use_buffers` to `True`. The first approach updates the
2024-11-01T17:51:30.1129877Z             statistics in a post-training step by passing data through the model. The
2024-11-01T17:51:30.1130230Z             second does it during the parameter update phase by averaging all buffers.
2024-11-01T17:51:30.1130599Z             Empirical evidence has shown that updating the statistics in normalization
2024-11-01T17:51:30.1130966Z             layers increases accuracy, but you may wish to empirically test which
2024-11-01T17:51:30.1131282Z             approach yields the best results in your problem.
2024-11-01T17:51:30.1131394Z     
2024-11-01T17:51:30.1131501Z         .. note::
2024-11-01T17:51:30.1131904Z             :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model.
2024-11-01T17:51:30.1132002Z     
2024-11-01T17:51:30.1132123Z         .. note::
2024-11-01T17:51:30.1132417Z             When :meth:`update_parameters` is called for the first time (i.e.
2024-11-01T17:51:30.1132687Z             :attr:`n_averaged` is `0`) the parameters of `model` are copied
2024-11-01T17:51:30.1133007Z             to the parameters of :class:`AveragedModel`. For every subsequent
2024-11-01T17:51:30.1133284Z             call of :meth:`update_parameters` the function `avg_fn` is used
2024-11-01T17:51:30.1133441Z             to update the parameters.
2024-11-01T17:51:30.1133537Z     
2024-11-01T17:51:30.1133869Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2024-11-01T17:51:30.1134039Z             https://arxiv.org/abs/1803.05407
2024-11-01T17:51:30.1134377Z         .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should
2024-11-01T17:51:30.1134501Z             Average:
2024-11-01T17:51:30.1134659Z             https://arxiv.org/abs/1806.05594
2024-11-01T17:51:30.1135046Z         .. _SWALP: Stochastic Weight Averaging in Low-Precision Training:
2024-11-01T17:51:30.1135206Z             https://arxiv.org/abs/1904.11943
2024-11-01T17:51:30.1135620Z         .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That
2024-11-01T17:51:30.1135748Z             Generalizes Well:
2024-11-01T17:51:30.1135942Z             https://arxiv.org/abs/2001.02312
2024-11-01T17:51:30.1136085Z         .. _Polyak averaging:
2024-11-01T17:51:30.1136398Z             https://paperswithcode.com/method/polyak-averaging
2024-11-01T17:51:30.1136513Z         
2024-11-01T17:51:30.1136950Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1137061Z     
2024-11-01T17:51:30.1137183Z       warnings.warn(msg)
2024-11-01T17:51:30.1137279Z     
2024-11-01T17:51:30.1137529Z --- Parse Warning: 93 / 103 ---
2024-11-01T17:51:30.1138939Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/optim/swa_utils.py line=368.
2024-11-01T17:51:30.1139441Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1139751Z     Anneals the learning rate in each parameter group to a fixed value.
2024-11-01T17:51:30.1139866Z     
2024-11-01T17:51:30.1140201Z         This learning rate scheduler is meant to be used with Stochastic Weight
2024-11-01T17:51:30.1140508Z         Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`).
2024-11-01T17:51:30.1140622Z     
2024-11-01T17:51:30.1140728Z         Args:
2024-11-01T17:51:30.1140986Z             optimizer (torch.optim.Optimizer): wrapped optimizer
2024-11-01T17:51:30.1141303Z             swa_lrs (float or list): the learning rate value for all param groups
2024-11-01T17:51:30.1141505Z                 together or separately for each group.
2024-11-01T17:51:30.1141791Z             annealing_epochs (int): number of epochs in the annealing phase
2024-11-01T17:51:30.1141911Z                 (default: 10)
2024-11-01T17:51:30.1142231Z             annealing_strategy (str): "cos" or "linear"; specifies the annealing
2024-11-01T17:51:30.1142532Z                 strategy: "cos" for cosine annealing, "linear" for linear annealing
2024-11-01T17:51:30.1142672Z                 (default: "cos")
2024-11-01T17:51:30.1143013Z             last_epoch (int): the index of the last epoch (default: -1)
2024-11-01T17:51:30.1143128Z     
2024-11-01T17:51:30.1143392Z         The :class:`SWALR` scheduler can be used together with other
2024-11-01T17:51:30.1143751Z         schedulers to switch to a constant learning rate late in the training
2024-11-01T17:51:30.1143938Z         as in the example below.
2024-11-01T17:51:30.1144036Z     
2024-11-01T17:51:30.1144157Z         Example:
2024-11-01T17:51:30.1144347Z             >>> # xdoctest: +SKIP("Undefined variables")
2024-11-01T17:51:30.1144512Z             >>> loader, optimizer, model = ...
2024-11-01T17:51:30.1144683Z             >>> lr_lambda = lambda epoch: 0.9
2024-11-01T17:51:30.1145002Z             >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer,
2024-11-01T17:51:30.1145161Z             >>>        lr_lambda=lr_lambda)
2024-11-01T17:51:30.1145406Z             >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer,
2024-11-01T17:51:30.1145676Z             >>>        anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05)
2024-11-01T17:51:30.1145797Z             >>> swa_start = 160
2024-11-01T17:51:30.1145929Z             >>> for i in range(300):
2024-11-01T17:51:30.1146112Z             >>>      for input, target in loader:
2024-11-01T17:51:30.1146273Z             >>>          optimizer.zero_grad()
2024-11-01T17:51:30.1146487Z             >>>          loss_fn(model(input), target).backward()
2024-11-01T17:51:30.1146630Z             >>>          optimizer.step()
2024-11-01T17:51:30.1146779Z             >>>      if i > swa_start:
2024-11-01T17:51:30.1146934Z             >>>          swa_scheduler.step()
2024-11-01T17:51:30.1147046Z             >>>      else:
2024-11-01T17:51:30.1147244Z             >>>          scheduler.step()
2024-11-01T17:51:30.1147341Z     
2024-11-01T17:51:30.1147670Z         .. _Averaging Weights Leads to Wider Optima and Better Generalization:
2024-11-01T17:51:30.1147897Z             https://arxiv.org/abs/1803.05407
2024-11-01T17:51:30.1148018Z         
2024-11-01T17:51:30.1148459Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1148556Z     
2024-11-01T17:51:30.1148694Z       warnings.warn(msg)
2024-11-01T17:51:30.1148789Z     
2024-11-01T17:51:30.1149037Z --- Parse Warning: 94 / 103 ---
2024-11-01T17:51:30.1150522Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_comparison.py line=1274.
2024-11-01T17:51:30.1150985Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1151236Z     Asserts that ``actual`` and ``expected`` are close.
2024-11-01T17:51:30.1151334Z     
2024-11-01T17:51:30.1151995Z         If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
2024-11-01T17:51:30.1152096Z     
2024-11-01T17:51:30.1152227Z         .. math::
2024-11-01T17:51:30.1152326Z     
2024-11-01T17:51:30.1153036Z             \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
2024-11-01T17:51:30.1153138Z     
2024-11-01T17:51:30.1153767Z         Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
2024-11-01T17:51:30.1154236Z         only considered equal to each other if ``equal_nan`` is ``True``.
2024-11-01T17:51:30.1154333Z     
2024-11-01T17:51:30.1154641Z         In addition, they are only considered close if they have the same
2024-11-01T17:51:30.1154738Z     
2024-11-01T17:51:30.1155108Z         - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
2024-11-01T17:51:30.1155354Z         - ``dtype`` (if ``check_dtype`` is ``True``),
2024-11-01T17:51:30.1155618Z         - ``layout`` (if ``check_layout`` is ``True``), and
2024-11-01T17:51:30.1155871Z         - stride (if ``check_stride`` is ``True``).
2024-11-01T17:51:30.1155970Z     
2024-11-01T17:51:30.1156445Z         If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
2024-11-01T17:51:30.1156634Z     
2024-11-01T17:51:30.1157227Z         If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
2024-11-01T17:51:30.1157764Z         checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
2024-11-01T17:51:30.1158101Z         or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
2024-11-01T17:51:30.1158680Z         are always checked for equality whereas the values are checked for closeness according to the definition above.
2024-11-01T17:51:30.1158782Z     
2024-11-01T17:51:30.1159220Z         If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
2024-11-01T17:51:30.1159753Z         :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
2024-11-01T17:51:30.1159890Z         definition above.
2024-11-01T17:51:30.1159988Z     
2024-11-01T17:51:30.1160569Z         ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
2024-11-01T17:51:30.1161248Z         :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
2024-11-01T17:51:30.1161852Z         have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
2024-11-01T17:51:30.1162547Z         or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
2024-11-01T17:51:30.1162874Z         their elements are considered close according to the above definition.
2024-11-01T17:51:30.1162988Z     
2024-11-01T17:51:30.1163101Z         .. note::
2024-11-01T17:51:30.1163215Z     
2024-11-01T17:51:30.1163751Z             Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
2024-11-01T17:51:30.1164351Z             :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
2024-11-01T17:51:30.1164777Z             Python scalars of different types can be checked, but require ``check_dtype=False``.
2024-11-01T17:51:30.1164875Z     
2024-11-01T17:51:30.1164998Z         Args:
2024-11-01T17:51:30.1165139Z             actual (Any): Actual input.
2024-11-01T17:51:30.1165310Z             expected (Any): Expected input.
2024-11-01T17:51:30.1165826Z             allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
2024-11-01T17:51:30.1166094Z                 are allowed. Otherwise type equality is required.
2024-11-01T17:51:30.1166648Z             rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
2024-11-01T17:51:30.1167047Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2024-11-01T17:51:30.1167584Z             atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
2024-11-01T17:51:30.1167985Z                 values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
2024-11-01T17:51:30.1168378Z             equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
2024-11-01T17:51:30.1168805Z             check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
2024-11-01T17:51:30.1169186Z                 :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
2024-11-01T17:51:30.1169635Z                 :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
2024-11-01T17:51:30.1170165Z             check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
2024-11-01T17:51:30.1170780Z                 check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
2024-11-01T17:51:30.1171014Z                 :func:`torch.promote_types`) before being compared.
2024-11-01T17:51:30.1171618Z             check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
2024-11-01T17:51:30.1172216Z                 check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
2024-11-01T17:51:30.1172346Z                 compared.
2024-11-01T17:51:30.1172876Z             check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
2024-11-01T17:51:30.1173414Z             msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
2024-11-01T17:51:30.1173954Z                 the comparison. Can also passed as callable in which case it will be called with the generated message and
2024-11-01T17:51:30.1174130Z                 should return the new message.
2024-11-01T17:51:30.1174229Z     
2024-11-01T17:51:30.1174335Z         Raises:
2024-11-01T17:51:30.1174695Z             ValueError: If no :class:`torch.Tensor` can be constructed from an input.
2024-11-01T17:51:30.1174980Z             ValueError: If only ``rtol`` or ``atol`` is specified.
2024-11-01T17:51:30.1175447Z             AssertionError: If corresponding inputs are not Python scalars and are not directly related.
2024-11-01T17:51:30.1175958Z             AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
2024-11-01T17:51:30.1176136Z                 different types.
2024-11-01T17:51:30.1176771Z             AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
2024-11-01T17:51:30.1177476Z             AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
2024-11-01T17:51:30.1177919Z             AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
2024-11-01T17:51:30.1178354Z             AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
2024-11-01T17:51:30.1178538Z                 :attr:`~torch.Tensor.layout`.
2024-11-01T17:51:30.1178849Z             AssertionError: If only one of corresponding tensors is quantized.
2024-11-01T17:51:30.1179518Z             AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
2024-11-01T17:51:30.1179943Z             AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
2024-11-01T17:51:30.1180151Z                 :attr:`~torch.Tensor.device`.
2024-11-01T17:51:30.1180636Z             AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
2024-11-01T17:51:30.1181170Z             AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
2024-11-01T17:51:30.1181683Z             AssertionError: If the values of corresponding tensors are not close according to the definition above.
2024-11-01T17:51:30.1181784Z     
2024-11-01T17:51:30.1182452Z         The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
2024-11-01T17:51:30.1182737Z         ``dtype``'s, the maximum of both tolerances is used.
2024-11-01T17:51:30.1182849Z     
2024-11-01T17:51:30.1183089Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1183330Z         | ``dtype``                 | ``rtol``   | ``atol`` |
2024-11-01T17:51:30.1183491Z         +===========================+============+==========+
2024-11-01T17:51:30.1183768Z         | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
2024-11-01T17:51:30.1184016Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1184286Z         | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
2024-11-01T17:51:30.1184528Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1184844Z         | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:30.1185112Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1185377Z         | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
2024-11-01T17:51:30.1185621Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1185886Z         | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
2024-11-01T17:51:30.1186116Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1186398Z         | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:30.1186623Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1186906Z         | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
2024-11-01T17:51:30.1187133Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1187408Z         | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:30.1187632Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1187898Z         | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:30.1188141Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1188402Z         | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:30.1188644Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1188903Z         | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:30.1189146Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1189405Z         | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
2024-11-01T17:51:30.1189634Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1189863Z         | other                     | ``0.0``    | ``0.0``  |
2024-11-01T17:51:30.1190090Z         +---------------------------+------------+----------+
2024-11-01T17:51:30.1190203Z     
2024-11-01T17:51:30.1190315Z         .. note::
2024-11-01T17:51:30.1190429Z     
2024-11-01T17:51:30.1190980Z             :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
2024-11-01T17:51:30.1191520Z             to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
2024-11-01T17:51:30.1191921Z             define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
2024-11-01T17:51:30.1192317Z     
2024-11-01T17:51:30.1192460Z             >>> import functools
2024-11-01T17:51:30.1192832Z             >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
2024-11-01T17:51:30.1193048Z             >>> assert_equal(1e-9, 1e-10)
2024-11-01T17:51:30.1193220Z             Traceback (most recent call last):
2024-11-01T17:51:30.1193324Z             ...
2024-11-01T17:51:30.1193518Z             AssertionError: Scalars are not equal!
2024-11-01T17:51:30.1193631Z             <BLANKLINE>
2024-11-01T17:51:30.1193983Z             Expected 1e-10 but got 1e-09.
2024-11-01T17:51:30.1194238Z             Absolute difference: 9.000000000000001e-10
2024-11-01T17:51:30.1194385Z             Relative difference: 9.0
2024-11-01T17:51:30.1194482Z     
2024-11-01T17:51:30.1194595Z         Examples:
2024-11-01T17:51:30.1194770Z             >>> # tensor to tensor comparison
2024-11-01T17:51:30.1195031Z             >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
2024-11-01T17:51:30.1195235Z             >>> actual = torch.acos(torch.cos(expected))
2024-11-01T17:51:30.1195450Z             >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:30.1195564Z     
2024-11-01T17:51:30.1195722Z             >>> # scalar to scalar comparison
2024-11-01T17:51:30.1195844Z             >>> import math
2024-11-01T17:51:30.1196002Z             >>> expected = math.sqrt(2.0)
2024-11-01T17:51:30.1196153Z             >>> actual = 2.0 / math.sqrt(2.0)
2024-11-01T17:51:30.1196377Z             >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:30.1196515Z     
2024-11-01T17:51:30.1196702Z             >>> # numpy array to numpy array comparison
2024-11-01T17:51:30.1196895Z             >>> import numpy as np
2024-11-01T17:51:30.1197143Z             >>> expected = np.array([1e0, 1e-1, 1e-2])
2024-11-01T17:51:30.1197334Z             >>> actual = np.arccos(np.cos(expected))
2024-11-01T17:51:30.1197542Z             >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:30.1197653Z     
2024-11-01T17:51:30.1197822Z             >>> # sequence to sequence comparison
2024-11-01T17:51:30.1197952Z             >>> import numpy as np
2024-11-01T17:51:30.1198360Z             >>> # The types of the sequences do not have to match. They only have to have the same
2024-11-01T17:51:30.1198556Z             >>> # length and their elements have to match.
2024-11-01T17:51:30.1198801Z             >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
2024-11-01T17:51:30.1198943Z             >>> actual = tuple(expected)
2024-11-01T17:51:30.1199170Z             >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:30.1199267Z     
2024-11-01T17:51:30.1199437Z             >>> # mapping to mapping comparison
2024-11-01T17:51:30.1199629Z             >>> from collections import OrderedDict
2024-11-01T17:51:30.1199758Z             >>> import numpy as np
2024-11-01T17:51:30.1199910Z             >>> foo = torch.tensor(1.0)
2024-11-01T17:51:30.1200084Z             >>> bar = 2.0
2024-11-01T17:51:30.1200277Z             >>> baz = np.array(3.0)
2024-11-01T17:51:30.1200661Z             >>> # The types and a possible ordering of mappings do not have to match. They only
2024-11-01T17:51:30.1200978Z             >>> # have to have the same set of keys and their elements have to match.
2024-11-01T17:51:30.1201368Z             >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
2024-11-01T17:51:30.1201567Z             >>> actual = {"baz": baz, "bar": bar, "foo": foo}
2024-11-01T17:51:30.1201788Z             >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:30.1201889Z     
2024-11-01T17:51:30.1202087Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2024-11-01T17:51:30.1202234Z             >>> actual = expected.clone()
2024-11-01T17:51:30.1202489Z             >>> # By default, directly related instances can be compared
2024-11-01T17:51:30.1202816Z             >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
2024-11-01T17:51:30.1203106Z             >>> # This check can be made more strict with allow_subclasses=False
2024-11-01T17:51:30.1203320Z             >>> torch.testing.assert_close(
2024-11-01T17:51:30.1203611Z             ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
2024-11-01T17:51:30.1203732Z             ... )
2024-11-01T17:51:30.1203904Z             Traceback (most recent call last):
2024-11-01T17:51:30.1204009Z             ...
2024-11-01T17:51:30.1204319Z             TypeError: No comparison pair was able to handle inputs of type
2024-11-01T17:51:30.1204722Z             <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
2024-11-01T17:51:30.1205081Z             >>> # If the inputs are not directly related, they are never considered close
2024-11-01T17:51:30.1205327Z             >>> torch.testing.assert_close(actual.numpy(), expected)
2024-11-01T17:51:30.1205507Z             Traceback (most recent call last):
2024-11-01T17:51:30.1205614Z             ...
2024-11-01T17:51:30.1206133Z             TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
2024-11-01T17:51:30.1206340Z             and <class 'torch.Tensor'>.
2024-11-01T17:51:30.1207031Z             >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
2024-11-01T17:51:30.1207267Z             >>> # their type if check_dtype=False.
2024-11-01T17:51:30.1207514Z             >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
2024-11-01T17:51:30.1207641Z     
2024-11-01T17:51:30.1207782Z             >>> # NaN != NaN by default.
2024-11-01T17:51:30.1208084Z             >>> expected = torch.tensor(float("Nan"))
2024-11-01T17:51:30.1208285Z             >>> actual = expected.clone()
2024-11-01T17:51:30.1208497Z             >>> torch.testing.assert_close(actual, expected)
2024-11-01T17:51:30.1208678Z             Traceback (most recent call last):
2024-11-01T17:51:30.1208783Z             ...
2024-11-01T17:51:30.1208976Z             AssertionError: Scalars are not close!
2024-11-01T17:51:30.1209091Z             <BLANKLINE>
2024-11-01T17:51:30.1209232Z             Expected nan but got nan.
2024-11-01T17:51:30.1209525Z             Absolute difference: nan (up to 1e-05 allowed)
2024-11-01T17:51:30.1209805Z             Relative difference: nan (up to 1.3e-06 allowed)
2024-11-01T17:51:30.1210106Z             >>> torch.testing.assert_close(actual, expected, equal_nan=True)
2024-11-01T17:51:30.1210206Z     
2024-11-01T17:51:30.1210388Z             >>> expected = torch.tensor([1.0, 2.0, 3.0])
2024-11-01T17:51:30.1210575Z             >>> actual = torch.tensor([1.0, 4.0, 5.0])
2024-11-01T17:51:30.1210792Z             >>> # The default error message can be overwritten.
2024-11-01T17:51:30.1211224Z             >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
2024-11-01T17:51:30.1211391Z             Traceback (most recent call last):
2024-11-01T17:51:30.1211509Z             ...
2024-11-01T17:51:30.1211728Z             AssertionError: Argh, the tensors are not close!
2024-11-01T17:51:30.1212099Z             >>> # If msg is a callable, it can be used to augment the generated message with
2024-11-01T17:51:30.1212233Z             >>> # extra information
2024-11-01T17:51:30.1212387Z             >>> torch.testing.assert_close(
2024-11-01T17:51:30.1212738Z             ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
2024-11-01T17:51:30.1212846Z             ... )
2024-11-01T17:51:30.1213030Z             Traceback (most recent call last):
2024-11-01T17:51:30.1213133Z             ...
2024-11-01T17:51:30.1213270Z             AssertionError: Header
2024-11-01T17:51:30.1213396Z             <BLANKLINE>
2024-11-01T17:51:30.1213592Z             Tensor-likes are not close!
2024-11-01T17:51:30.1213717Z             <BLANKLINE>
2024-11-01T17:51:30.1213881Z             Mismatched elements: 2 / 3 (66.7%)
2024-11-01T17:51:30.1214297Z             Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
2024-11-01T17:51:30.1214706Z             Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
2024-11-01T17:51:30.1214877Z             <BLANKLINE>
2024-11-01T17:51:30.1214996Z             Footer
2024-11-01T17:51:30.1215096Z         
2024-11-01T17:51:30.1215549Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1215649Z     
2024-11-01T17:51:30.1215787Z       warnings.warn(msg)
2024-11-01T17:51:30.1215884Z     
2024-11-01T17:51:30.1216117Z --- Parse Warning: 95 / 103 ---
2024-11-01T17:51:30.1217642Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/_cxx_pytree.py line=111.
2024-11-01T17:51:30.1218090Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1218359Z     Register a container-like type as pytree node.
2024-11-01T17:51:30.1218457Z     
2024-11-01T17:51:30.1218577Z         Args:
2024-11-01T17:51:30.1218863Z             cls (type): A Python type to treat as an internal pytree node.
2024-11-01T17:51:30.1219262Z             flatten_fn (callable): A function to be used during flattening, taking an instance of
2024-11-01T17:51:30.1219662Z                 ``cls`` and returning a pair, with (1) an iterable for the children to be flattened
2024-11-01T17:51:30.1220090Z                 recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
2024-11-01T17:51:30.1220269Z                 passed to the ``unflatten_fn``.
2024-11-01T17:51:30.1220724Z             unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
2024-11-01T17:51:30.1221135Z                 returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
2024-11-01T17:51:30.1221363Z                 The function should return an instance of ``cls``.
2024-11-01T17:51:30.1221757Z             serialized_type_name (str, optional): A keyword argument used to specify the fully
2024-11-01T17:51:30.1221995Z                 qualified name used when serializing the tree spec.
2024-11-01T17:51:30.1222432Z             to_dumpable_context (callable, optional): An optional keyword argument to custom specify how
2024-11-01T17:51:30.1222873Z                 to convert the context of the pytree to a custom json dumpable representation. This is
2024-11-01T17:51:30.1223271Z                 used for json serialization, which is being used in :mod:`torch.export` right now.
2024-11-01T17:51:30.1223709Z             from_dumpable_context (callable, optional): An optional keyword argument to custom specify
2024-11-01T17:51:30.1224109Z                 how to convert the custom json dumpable representation of the context back to the
2024-11-01T17:51:30.1224508Z                 original context. This is used for json deserialization, which is being used in
2024-11-01T17:51:30.1224668Z                 :mod:`torch.export` right now.
2024-11-01T17:51:30.1224810Z     
2024-11-01T17:51:30.1224934Z         Example::
2024-11-01T17:51:30.1225032Z     
2024-11-01T17:51:30.1225176Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:30.1225387Z             >>> # Registry a Python type with lambda functions
2024-11-01T17:51:30.1225542Z             >>> register_pytree_node(
2024-11-01T17:51:30.1225683Z             ...     set,
2024-11-01T17:51:30.1225865Z             ...     lambda s: (sorted(s), None, None),
2024-11-01T17:51:30.1226061Z             ...     lambda children, _: set(children),
2024-11-01T17:51:30.1226167Z             ... )
2024-11-01T17:51:30.1226282Z         
2024-11-01T17:51:30.1226723Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1226835Z     
2024-11-01T17:51:30.1226956Z       warnings.warn(msg)
2024-11-01T17:51:30.1227052Z     
2024-11-01T17:51:30.1227299Z --- Parse Warning: 96 / 103 ---
2024-11-01T17:51:30.1228889Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1201.
2024-11-01T17:51:30.1229383Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1229480Z     
2024-11-01T17:51:30.1229798Z     Context passed to policy function during selective checkpointing.
2024-11-01T17:51:30.1229896Z     
2024-11-01T17:51:30.1230236Z     This class is used to pass relevant metadata to the policy function during
2024-11-01T17:51:30.1230616Z     selective checkpointing. The metadata includes whether the current invocation
2024-11-01T17:51:30.1230851Z     of the policy function is during recomputation or not.
2024-11-01T17:51:30.1230961Z     
2024-11-01T17:51:30.1231070Z     Example:
2024-11-01T17:51:30.1231219Z         >>> # xdoctest: +SKIP(stub)
2024-11-01T17:51:30.1231389Z         >>>
2024-11-01T17:51:30.1231574Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2024-11-01T17:51:30.1231729Z         >>>    print(ctx.is_recompute)
2024-11-01T17:51:30.1231833Z         >>>
2024-11-01T17:51:30.1232222Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2024-11-01T17:51:30.1232324Z         >>>
2024-11-01T17:51:30.1232533Z         >>> out = torch.utils.checkpoint.checkpoint(
2024-11-01T17:51:30.1232730Z         >>>     fn, x, y,
2024-11-01T17:51:30.1232860Z         >>>     use_reentrant=False,
2024-11-01T17:51:30.1233008Z         >>>     context_fn=context_fn,
2024-11-01T17:51:30.1233141Z         >>> )
2024-11-01T17:51:30.1233252Z     
2024-11-01T17:51:30.1233711Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1233808Z     
2024-11-01T17:51:30.1234076Z       warnings.warn(msg)
2024-11-01T17:51:30.1234172Z     
2024-11-01T17:51:30.1234422Z --- Parse Warning: 97 / 103 ---
2024-11-01T17:51:30.1236029Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/checkpoint.py line=1335.
2024-11-01T17:51:30.1236488Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1236588Z     
2024-11-01T17:51:30.1236928Z     Helper to avoid recomputing certain ops during activation checkpointing.
2024-11-01T17:51:30.1237039Z     
2024-11-01T17:51:30.1237358Z     Use this with `torch.utils.checkpoint.checkpoint` to control which
2024-11-01T17:51:30.1237595Z     operations are recomputed during the backward pass.
2024-11-01T17:51:30.1237691Z     
2024-11-01T17:51:30.1237826Z     Args:
2024-11-01T17:51:30.1237989Z         policy_fn_or_list (Callable or List):
2024-11-01T17:51:30.1238294Z           - If a policy function is provided, it should accept a
2024-11-01T17:51:30.1238645Z             :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and
2024-11-01T17:51:30.1238957Z             kwargs to the op, and return a :class:`CheckpointPolicy` enum value
2024-11-01T17:51:30.1239354Z             indicating whether the execution of the op should be recomputed or not.
2024-11-01T17:51:30.1239737Z           - If a list of operations is provided, it is equivalent to a policy
2024-11-01T17:51:30.1240047Z             returning `CheckpointPolicy.MUST_SAVE` for the specified
2024-11-01T17:51:30.1240361Z             operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other
2024-11-01T17:51:30.1240476Z             operations.
2024-11-01T17:51:30.1240798Z         allow_cache_entry_mutation (bool, optional): By default, an error is
2024-11-01T17:51:30.1241112Z             raised if any tensors cached by selective activation checkpoint are
2024-11-01T17:51:30.1241442Z             mutated in order to ensure correctness. If set to `True`, this check
2024-11-01T17:51:30.1241557Z             is disabled.
2024-11-01T17:51:30.1241677Z     Returns:
2024-11-01T17:51:30.1241827Z         A tuple of two context managers.
2024-11-01T17:51:30.1241953Z     
2024-11-01T17:51:30.1242075Z     Example:
2024-11-01T17:51:30.1242222Z         >>> # xdoctest: +REQUIRES(LINUX)
2024-11-01T17:51:30.1242359Z         >>> import functools
2024-11-01T17:51:30.1242463Z         >>>
2024-11-01T17:51:30.1242649Z         >>> x = torch.rand(10, 10, requires_grad=True)
2024-11-01T17:51:30.1242840Z         >>> y = torch.rand(10, 10, requires_grad=True)
2024-11-01T17:51:30.1242940Z         >>>
2024-11-01T17:51:30.1243075Z         >>> ops_to_save = [
2024-11-01T17:51:30.1243233Z         >>>    torch.ops.aten.mm.default,
2024-11-01T17:51:30.1243349Z         >>> ]
2024-11-01T17:51:30.1243451Z         >>>
2024-11-01T17:51:30.1243634Z         >>> def policy_fn(ctx, op, *args, **kwargs):
2024-11-01T17:51:30.1243781Z         >>>    if op in ops_to_save:
2024-11-01T17:51:30.1243965Z         >>>        return CheckpointPolicy.MUST_SAVE
2024-11-01T17:51:30.1244088Z         >>>    else:
2024-11-01T17:51:30.1244298Z         >>>        return CheckpointPolicy.PREFER_RECOMPUTE
2024-11-01T17:51:30.1244417Z         >>>
2024-11-01T17:51:30.1244795Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn)
2024-11-01T17:51:30.1244896Z         >>>
2024-11-01T17:51:30.1245034Z         >>> # or equivalently
2024-11-01T17:51:30.1245422Z         >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save)
2024-11-01T17:51:30.1245536Z         >>>
2024-11-01T17:51:30.1245656Z         >>> def fn(x, y):
2024-11-01T17:51:30.1246005Z         >>>     return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y
2024-11-01T17:51:30.1246121Z         >>>
2024-11-01T17:51:30.1246354Z         >>> out = torch.utils.checkpoint.checkpoint(
2024-11-01T17:51:30.1246487Z         >>>     fn, x, y,
2024-11-01T17:51:30.1246618Z         >>>     use_reentrant=False,
2024-11-01T17:51:30.1246767Z         >>>     context_fn=context_fn,
2024-11-01T17:51:30.1246869Z         >>> )
2024-11-01T17:51:30.1246964Z     
2024-11-01T17:51:30.1247414Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1247514Z     
2024-11-01T17:51:30.1247647Z       warnings.warn(msg)
2024-11-01T17:51:30.1247744Z     
2024-11-01T17:51:30.1247977Z --- Parse Warning: 98 / 103 ---
2024-11-01T17:51:30.1249461Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=957.
2024-11-01T17:51:30.1249910Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1250026Z     
2024-11-01T17:51:30.1250223Z     Create a :class:`setuptools.Extension` for C++.
2024-11-01T17:51:30.1250336Z     
2024-11-01T17:51:30.1250673Z     Convenience method that creates a :class:`setuptools.Extension` with the
2024-11-01T17:51:30.1251011Z     bare minimum (but often sufficient) arguments to build a C++ extension.
2024-11-01T17:51:30.1251110Z     
2024-11-01T17:51:30.1251402Z     All arguments are forwarded to the :class:`setuptools.Extension`
2024-11-01T17:51:30.1251626Z     constructor. Full list arguments can be found at
2024-11-01T17:51:30.1252210Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2024-11-01T17:51:30.1252326Z     
2024-11-01T17:51:30.1252433Z     Example:
2024-11-01T17:51:30.1252561Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.1252787Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:30.1252939Z         >>> from setuptools import setup
2024-11-01T17:51:30.1253274Z         >>> from torch.utils.cpp_extension import BuildExtension, CppExtension
2024-11-01T17:51:30.1253384Z         >>> setup(
2024-11-01T17:51:30.1253572Z         ...     name='extension',
2024-11-01T17:51:30.1253689Z         ...     ext_modules=[
2024-11-01T17:51:30.1253816Z         ...         CppExtension(
2024-11-01T17:51:30.1254015Z         ...             name='extension',
2024-11-01T17:51:30.1254278Z         ...             sources=['extension.cpp'],
2024-11-01T17:51:30.1254511Z         ...             extra_compile_args=['-g'],
2024-11-01T17:51:30.1254801Z         ...             extra_link_args=['-Wl,--no-as-needed', '-lm'])
2024-11-01T17:51:30.1254923Z         ...     ],
2024-11-01T17:51:30.1255039Z         ...     cmdclass={
2024-11-01T17:51:30.1255254Z         ...         'build_ext': BuildExtension
2024-11-01T17:51:30.1255373Z         ...     })
2024-11-01T17:51:30.1255471Z     
2024-11-01T17:51:30.1255919Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1256019Z     
2024-11-01T17:51:30.1256142Z       warnings.warn(msg)
2024-11-01T17:51:30.1256253Z     
2024-11-01T17:51:30.1256482Z --- Parse Warning: 99 / 103 ---
2024-11-01T17:51:30.1257971Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1008.
2024-11-01T17:51:30.1258423Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1258537Z     
2024-11-01T17:51:30.1258765Z     Create a :class:`setuptools.Extension` for CUDA/C++.
2024-11-01T17:51:30.1258877Z     
2024-11-01T17:51:30.1259226Z     Convenience method that creates a :class:`setuptools.Extension` with the
2024-11-01T17:51:30.1259515Z     bare minimum (but often sufficient) arguments to build a CUDA/C++
2024-11-01T17:51:30.1259900Z     extension. This includes the CUDA include path, library path and runtime
2024-11-01T17:51:30.1260035Z     library.
2024-11-01T17:51:30.1260147Z     
2024-11-01T17:51:30.1260440Z     All arguments are forwarded to the :class:`setuptools.Extension`
2024-11-01T17:51:30.1260646Z     constructor. Full list arguments can be found at
2024-11-01T17:51:30.1261204Z     https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference
2024-11-01T17:51:30.1261304Z     
2024-11-01T17:51:30.1261424Z     Example:
2024-11-01T17:51:30.1261551Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.1261780Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:30.1261988Z         >>> from setuptools import setup
2024-11-01T17:51:30.1262312Z         >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension
2024-11-01T17:51:30.1262435Z         >>> setup(
2024-11-01T17:51:30.1262618Z         ...     name='cuda_extension',
2024-11-01T17:51:30.1262754Z         ...     ext_modules=[
2024-11-01T17:51:30.1262882Z         ...         CUDAExtension(
2024-11-01T17:51:30.1263120Z         ...                 name='cuda_extension',
2024-11-01T17:51:30.1263437Z         ...                 sources=['extension.cpp', 'extension_kernel.cu'],
2024-11-01T17:51:30.1263693Z         ...                 extra_compile_args={'cxx': ['-g'],
2024-11-01T17:51:30.1263967Z         ...                                     'nvcc': ['-O2']},
2024-11-01T17:51:30.1264273Z         ...                 extra_link_args=['-Wl,--no-as-needed', '-lcuda'])
2024-11-01T17:51:30.1264394Z         ...     ],
2024-11-01T17:51:30.1264507Z         ...     cmdclass={
2024-11-01T17:51:30.1264736Z         ...         'build_ext': BuildExtension
2024-11-01T17:51:30.1264900Z         ...     })
2024-11-01T17:51:30.1264997Z     
2024-11-01T17:51:30.1265139Z     Compute capabilities:
2024-11-01T17:51:30.1265239Z     
2024-11-01T17:51:30.1265707Z     By default the extension will be compiled to run on all archs of the cards visible during the
2024-11-01T17:51:30.1266141Z     building process of the extension, plus PTX. If down the road a new card is installed the
2024-11-01T17:51:30.1266645Z     extension may need to be recompiled. If a visible card has a compute capability (CC) that's
2024-11-01T17:51:30.1267188Z     newer than the newest version for which your nvcc can build fully-compiled binaries, Pytorch
2024-11-01T17:51:30.1267620Z     will make nvcc fall back to building kernels with the newest version of PTX your nvcc does
2024-11-01T17:51:30.1267832Z     support (see below for details on PTX).
2024-11-01T17:51:30.1267929Z     
2024-11-01T17:51:30.1268408Z     You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which
2024-11-01T17:51:30.1268571Z     CCs you want the extension to support:
2024-11-01T17:51:30.1268682Z     
2024-11-01T17:51:30.1269013Z     ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py``
2024-11-01T17:51:30.1269391Z     ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py``
2024-11-01T17:51:30.1269505Z     
2024-11-01T17:51:30.1269974Z     The +PTX option causes extension kernel binaries to include PTX instructions for the specified
2024-11-01T17:51:30.1270544Z     CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >=
2024-11-01T17:51:30.1271097Z     the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with
2024-11-01T17:51:30.1271650Z     CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to
2024-11-01T17:51:30.1272207Z     provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on
2024-11-01T17:51:30.1272754Z     those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better
2024-11-01T17:51:30.1273223Z     off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6,
2024-11-01T17:51:30.1273821Z     "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but
2024-11-01T17:51:30.1274126Z     "8.0 8.6" would be better.
2024-11-01T17:51:30.1274223Z     
2024-11-01T17:51:30.1274787Z     Note that while it's possible to include all supported archs, the more archs get included the
2024-11-01T17:51:30.1275228Z     slower the building process will be, as it will build a separate kernel image for each arch.
2024-11-01T17:51:30.1275343Z     
2024-11-01T17:51:30.1275944Z     Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows.
2024-11-01T17:51:30.1276256Z     To workaround the issue, move python binding logic to pure C++ file.
2024-11-01T17:51:30.1276368Z     
2024-11-01T17:51:30.1276485Z     Example use:
2024-11-01T17:51:30.1276624Z         #include <ATen/ATen.h>
2024-11-01T17:51:30.1276851Z         at::Tensor SigmoidAlphaBlendForwardCuda(....)
2024-11-01T17:51:30.1276962Z     
2024-11-01T17:51:30.1277074Z     Instead of:
2024-11-01T17:51:30.1277212Z         #include <torch/extension.h>
2024-11-01T17:51:30.1277452Z         torch::Tensor SigmoidAlphaBlendForwardCuda(...)
2024-11-01T17:51:30.1277550Z     
2024-11-01T17:51:30.1277956Z     Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
2024-11-01T17:51:30.1278658Z     Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48
2024-11-01T17:51:30.1278812Z     
2024-11-01T17:51:30.1278953Z     Relocatable device code linking:
2024-11-01T17:51:30.1279049Z     
2024-11-01T17:51:30.1279474Z     If you want to reference device symbols across compilation units (across object files),
2024-11-01T17:51:30.1279987Z     the object files need to be built with `relocatable device code` (-rdc=true or -dc).
2024-11-01T17:51:30.1280530Z     An exception to this rule is "dynamic parallelism" (nested kernel launches)  which is not used a lot anymore.
2024-11-01T17:51:30.1281027Z     `Relocatable device code` is less optimized so it needs to be used only on object files that need it.
2024-11-01T17:51:30.1281605Z     Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step
2024-11-01T17:51:30.1281921Z     help reduce the protentional perf degradation of `-rdc`.
2024-11-01T17:51:30.1282167Z     Note that it needs to be used at both steps to be useful.
2024-11-01T17:51:30.1282276Z     
2024-11-01T17:51:30.1282963Z     If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step.
2024-11-01T17:51:30.1283335Z     There is also a case where `-dlink` is used without `-rdc`:
2024-11-01T17:51:30.1283798Z     when an extension is linked against a static lib containing rdc-compiled objects
2024-11-01T17:51:30.1284114Z     like the [NVSHMEM library](https://developer.nvidia.com/nvshmem).
2024-11-01T17:51:30.1284210Z     
2024-11-01T17:51:30.1284511Z     Note: Ninja is required to build a CUDA Extension with RDC linking.
2024-11-01T17:51:30.1284628Z     
2024-11-01T17:51:30.1284735Z     Example:
2024-11-01T17:51:30.1284877Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.1285087Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:30.1285223Z         >>> CUDAExtension(
2024-11-01T17:51:30.1285414Z         ...        name='cuda_extension',
2024-11-01T17:51:30.1285715Z         ...        sources=['extension.cpp', 'extension_kernel.cu'],
2024-11-01T17:51:30.1285850Z         ...        dlink=True,
2024-11-01T17:51:30.1286015Z         ...        dlink_libraries=["dlink_lib"],
2024-11-01T17:51:30.1286264Z         ...        extra_compile_args={'cxx': ['-g'],
2024-11-01T17:51:30.1286526Z         ...                            'nvcc': ['-O2', '-rdc=true']})
2024-11-01T17:51:30.1286635Z     
2024-11-01T17:51:30.1287060Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1287159Z     
2024-11-01T17:51:30.1287295Z       warnings.warn(msg)
2024-11-01T17:51:30.1287423Z     
2024-11-01T17:51:30.1287674Z --- Parse Warning: 100 / 103 ---
2024-11-01T17:51:30.1289135Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1278.
2024-11-01T17:51:30.1289599Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1289699Z     
2024-11-01T17:51:30.1289966Z     Load a PyTorch C++ extension just-in-time (JIT).
2024-11-01T17:51:30.1290079Z     
2024-11-01T17:51:30.1290386Z     To load an extension, a Ninja build file is emitted, which is used to
2024-11-01T17:51:30.1290697Z     compile the given sources into a dynamic library. This library is
2024-11-01T17:51:30.1291006Z     subsequently loaded into the current Python process as a module and
2024-11-01T17:51:30.1291199Z     returned from this function, ready for use.
2024-11-01T17:51:30.1291300Z     
2024-11-01T17:51:30.1291606Z     By default, the directory to which the build file is emitted and the
2024-11-01T17:51:30.1291953Z     resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
2024-11-01T17:51:30.1292258Z     ``<tmp>`` is the temporary folder on the current platform and ``<name>``
2024-11-01T17:51:30.1292600Z     the name of the extension. This location can be overridden in two ways.
2024-11-01T17:51:30.1292906Z     First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
2024-11-01T17:51:30.1293225Z     replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
2024-11-01T17:51:30.1293536Z     into subfolders of this directory. Second, if the ``build_directory``
2024-11-01T17:51:30.1293906Z     argument to this function is supplied, it overrides the entire path, i.e.
2024-11-01T17:51:30.1294164Z     the library will be compiled into that folder directly.
2024-11-01T17:51:30.1294261Z     
2024-11-01T17:51:30.1294586Z     To compile the sources, the default system compiler (``c++``) is used,
2024-11-01T17:51:30.1294946Z     which can be overridden by setting the ``CXX`` environment variable. To pass
2024-11-01T17:51:30.1295271Z     additional arguments to the compilation process, ``extra_cflags`` or
2024-11-01T17:51:30.1295595Z     ``extra_ldflags`` can be provided. For example, to compile your extension
2024-11-01T17:51:30.1295967Z     with optimizations, pass ``extra_cflags=['-O3']``. You can also use
2024-11-01T17:51:30.1296245Z     ``extra_cflags`` to pass further include directories.
2024-11-01T17:51:30.1296344Z     
2024-11-01T17:51:30.1296698Z     CUDA support with mixed compilation is provided. Simply pass CUDA source
2024-11-01T17:51:30.1296987Z     files (``.cu`` or ``.cuh``) along with other sources. Such files will be
2024-11-01T17:51:30.1297353Z     detected and compiled with nvcc rather than the C++ compiler. This includes
2024-11-01T17:51:30.1297663Z     passing the CUDA lib64 directory as a library directory, and linking
2024-11-01T17:51:30.1297887Z     ``cudart``. You can pass additional flags to nvcc via
2024-11-01T17:51:30.1298200Z     ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various
2024-11-01T17:51:30.1298534Z     heuristics for finding the CUDA install directory are used, which usually
2024-11-01T17:51:30.1298874Z     work fine. If not, setting the ``CUDA_HOME`` environment variable is the
2024-11-01T17:51:30.1298987Z     safest option.
2024-11-01T17:51:30.1299097Z     
2024-11-01T17:51:30.1299203Z     Args:
2024-11-01T17:51:30.1299523Z         name: The name of the extension to build. This MUST be the same as the
2024-11-01T17:51:30.1299684Z             name of the pybind11 module!
2024-11-01T17:51:30.1299982Z         sources: A list of relative or absolute paths to C++ source files.
2024-11-01T17:51:30.1300316Z         extra_cflags: optional list of compiler flags to forward to the build.
2024-11-01T17:51:30.1300625Z         extra_cuda_cflags: optional list of compiler flags to forward to nvcc
2024-11-01T17:51:30.1300814Z             when building CUDA sources.
2024-11-01T17:51:30.1301149Z         extra_ldflags: optional list of linker flags to forward to the build.
2024-11-01T17:51:30.1301466Z         extra_include_paths: optional list of include directories to forward
2024-11-01T17:51:30.1301584Z             to the build.
2024-11-01T17:51:30.1301835Z         build_directory: optional path to use as build workspace.
2024-11-01T17:51:30.1302109Z         verbose: If ``True``, turns on verbose logging of load steps.
2024-11-01T17:51:30.1302422Z         with_cuda: Determines whether CUDA headers and libraries are added to
2024-11-01T17:51:30.1302674Z             the build. If set to ``None`` (default), this value is
2024-11-01T17:51:30.1302957Z             automatically determined based on the existence of ``.cu`` or
2024-11-01T17:51:30.1303237Z             ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers
2024-11-01T17:51:30.1303384Z             and libraries to be included.
2024-11-01T17:51:30.1303679Z         is_python_module: If ``True`` (default), imports the produced shared
2024-11-01T17:51:30.1303973Z             library as a Python module. If ``False``, behavior depends on
2024-11-01T17:51:30.1304096Z             ``is_standalone``.
2024-11-01T17:51:30.1304411Z         is_standalone: If ``False`` (default) loads the constructed extension
2024-11-01T17:51:30.1304704Z             into the process as a plain dynamic library. If ``True``, build a
2024-11-01T17:51:30.1304853Z             standalone executable.
2024-11-01T17:51:30.1304951Z     
2024-11-01T17:51:30.1305057Z     Returns:
2024-11-01T17:51:30.1305229Z         If ``is_python_module`` is ``True``:
2024-11-01T17:51:30.1305482Z             Returns the loaded PyTorch extension as a Python module.
2024-11-01T17:51:30.1305625Z     
2024-11-01T17:51:30.1305925Z         If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
2024-11-01T17:51:30.1306244Z             Returns nothing. (The shared library is loaded into the process as
2024-11-01T17:51:30.1306366Z             a side effect.)
2024-11-01T17:51:30.1306464Z     
2024-11-01T17:51:30.1306858Z         If ``is_standalone`` is ``True``.
2024-11-01T17:51:30.1307159Z             Return the path to the executable. (On Windows, TORCH_LIB_PATH is
2024-11-01T17:51:30.1307434Z             added to the PATH environment variable as a side effect.)
2024-11-01T17:51:30.1307530Z     
2024-11-01T17:51:30.1307637Z     Example:
2024-11-01T17:51:30.1307774Z         >>> # xdoctest: +SKIP
2024-11-01T17:51:30.1308070Z         >>> from torch.utils.cpp_extension import load
2024-11-01T17:51:30.1308204Z         >>> module = load(
2024-11-01T17:51:30.1308391Z         ...     name='extension',
2024-11-01T17:51:30.1308704Z         ...     sources=['extension.cpp', 'extension_kernel.cu'],
2024-11-01T17:51:30.1308886Z         ...     extra_cflags=['-O2'],
2024-11-01T17:51:30.1309005Z         ...     verbose=True)
2024-11-01T17:51:30.1309116Z     
2024-11-01T17:51:30.1309542Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1309654Z     
2024-11-01T17:51:30.1309777Z       warnings.warn(msg)
2024-11-01T17:51:30.1309873Z     
2024-11-01T17:51:30.1310122Z --- Parse Warning: 101 / 103 ---
2024-11-01T17:51:30.1311592Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/cpp_extension.py line=1567.
2024-11-01T17:51:30.1312054Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1312151Z     
2024-11-01T17:51:30.1312548Z     Load a PyTorch C++ extension just-in-time (JIT) from string sources.
2024-11-01T17:51:30.1312649Z     
2024-11-01T17:51:30.1312998Z     This function behaves exactly like :func:`load`, but takes its sources as
2024-11-01T17:51:30.1313331Z     strings rather than filenames. These strings are stored to files in the
2024-11-01T17:51:30.1313680Z     build directory, after which the behavior of :func:`load_inline` is
2024-11-01T17:51:30.1313954Z     identical to :func:`load`.
2024-11-01T17:51:30.1314058Z     
2024-11-01T17:51:30.1314178Z     See `the
2024-11-01T17:51:30.1314636Z     tests <https://github.com/pytorch/pytorch/blob/master/test/test_cpp_extensions_jit.py>`_
2024-11-01T17:51:30.1314827Z     for good examples of using this function.
2024-11-01T17:51:30.1314925Z     
2024-11-01T17:51:30.1315357Z     Sources may omit two required parts of a typical non-inline C++ extension:
2024-11-01T17:51:30.1315721Z     the necessary header includes, as well as the (pybind11) binding code. More
2024-11-01T17:51:30.1316060Z     precisely, strings passed to ``cpp_sources`` are first concatenated into a
2024-11-01T17:51:30.1316354Z     single ``.cpp`` file. This file is then prepended with ``#include
2024-11-01T17:51:30.1316480Z     <torch/extension.h>``.
2024-11-01T17:51:30.1316591Z     
2024-11-01T17:51:30.1316912Z     Furthermore, if the ``functions`` argument is supplied, bindings will be
2024-11-01T17:51:30.1317240Z     automatically generated for each function specified. ``functions`` can
2024-11-01T17:51:30.1317585Z     either be a list of function names, or a dictionary mapping from function
2024-11-01T17:51:30.1317920Z     names to docstrings. If a list is given, the name of each function is used
2024-11-01T17:51:30.1318051Z     as its docstring.
2024-11-01T17:51:30.1318150Z     
2024-11-01T17:51:30.1318486Z     The sources in ``cuda_sources`` are concatenated into a separate ``.cu``
2024-11-01T17:51:30.1318739Z     file and  prepended with ``torch/types.h``, ``cuda.h`` and
2024-11-01T17:51:30.1319050Z     ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled
2024-11-01T17:51:30.1319418Z     separately, but ultimately linked into a single library. Note that no
2024-11-01T17:51:30.1319755Z     bindings are generated for functions in ``cuda_sources`` per  se. To bind
2024-11-01T17:51:30.1320101Z     to a CUDA kernel, you must create a C++ function that calls it, and either
2024-11-01T17:51:30.1320419Z     declare or define this C++ function in one of the ``cpp_sources`` (and
2024-11-01T17:51:30.1320579Z     include its name in ``functions``).
2024-11-01T17:51:30.1320676Z     
2024-11-01T17:51:30.1320945Z     See :func:`load` for a description of arguments omitted below.
2024-11-01T17:51:30.1321058Z     
2024-11-01T17:51:30.1321160Z     Args:
2024-11-01T17:51:30.1321488Z         cpp_sources: A string, or list of strings, containing C++ source code.
2024-11-01T17:51:30.1321845Z         cuda_sources: A string, or list of strings, containing CUDA source code.
2024-11-01T17:51:30.1322156Z         functions: A list of function names for which to generate function
2024-11-01T17:51:30.1322470Z             bindings. If a dictionary is given, it should map function names to
2024-11-01T17:51:30.1322724Z             docstrings (which are otherwise just the function names).
2024-11-01T17:51:30.1323056Z         with_cuda: Determines whether CUDA headers and libraries are added to
2024-11-01T17:51:30.1323296Z             the build. If set to ``None`` (default), this value is
2024-11-01T17:51:30.1323593Z             automatically determined based on whether ``cuda_sources`` is
2024-11-01T17:51:30.1323812Z             provided. Set it to ``True`` to force CUDA headers
2024-11-01T17:51:30.1323977Z             and libraries to be included.
2024-11-01T17:51:30.1324268Z         with_pytorch_error_handling: Determines whether pytorch error and
2024-11-01T17:51:30.1324560Z             warning macros are handled by pytorch instead of pybind. To do
2024-11-01T17:51:30.1324898Z             this, each function ``foo`` is called via an intermediary ``_safe_foo``
2024-11-01T17:51:30.1325183Z             function. This redirection might cause issues in obscure cases
2024-11-01T17:51:30.1325482Z             of cpp. This flag should be set to ``False`` when this redirect
2024-11-01T17:51:30.1325603Z             causes issues.
2024-11-01T17:51:30.1325746Z     
2024-11-01T17:51:30.1325855Z     Example:
2024-11-01T17:51:30.1326099Z         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT)
2024-11-01T17:51:30.1326344Z         >>> from torch.utils.cpp_extension import load_inline
2024-11-01T17:51:30.1326463Z         >>> source = """
2024-11-01T17:51:30.1326689Z         at::Tensor sin_add(at::Tensor x, at::Tensor y) {
2024-11-01T17:51:30.1326821Z           return x.sin() + y.sin();
2024-11-01T17:51:30.1326939Z         }
2024-11-01T17:51:30.1327042Z         """
2024-11-01T17:51:30.1327314Z         >>> module = load_inline(name='inline_extension',
2024-11-01T17:51:30.1327503Z         ...                      cpp_sources=[source],
2024-11-01T17:51:30.1327732Z         ...                      functions=['sin_add'])
2024-11-01T17:51:30.1327848Z     
2024-11-01T17:51:30.1327954Z     .. note::
2024-11-01T17:51:30.1328265Z         By default, the Ninja backend uses #CPUS + 2 workers to build the
2024-11-01T17:51:30.1328567Z         extension. This may use up too many resources on some systems. One
2024-11-01T17:51:30.1328897Z         can control the number of workers by setting the `MAX_JOBS` environment
2024-11-01T17:51:30.1329118Z         variable to a non-negative number.
2024-11-01T17:51:30.1329219Z     
2024-11-01T17:51:30.1329667Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1329765Z     
2024-11-01T17:51:30.1329889Z       warnings.warn(msg)
2024-11-01T17:51:30.1329999Z     
2024-11-01T17:51:30.1330235Z --- Parse Warning: 102 / 103 ---
2024-11-01T17:51:30.1331859Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/throughput_benchmark.py line=61.
2024-11-01T17:51:30.1332308Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1332418Z     
2024-11-01T17:51:30.1332855Z     This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
2024-11-01T17:51:30.1332969Z     
2024-11-01T17:51:30.1333390Z     This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
2024-11-01T17:51:30.1333747Z     for executing a PyTorch module (nn.Module or ScriptModule) under an inference
2024-11-01T17:51:30.1334116Z     server like load. It can emulate multiple calling threads to a single module
2024-11-01T17:51:30.1334479Z     provided. In the future we plan to enhance this component to support inter and
2024-11-01T17:51:30.1334964Z     intra-op parallelism as well as multiple models running in a single process.
2024-11-01T17:51:30.1335061Z     
2024-11-01T17:51:30.1335449Z     Please note that even though nn.Module is supported, it might incur an overhead
2024-11-01T17:51:30.1335790Z     from the need to hold GIL every time we execute Python code or pass around
2024-11-01T17:51:30.1336155Z     inputs as Python objects. As soon as you have a ScriptModule version of your
2024-11-01T17:51:30.1336496Z     model for inference deployment it is better to switch to using it in this
2024-11-01T17:51:30.1336608Z     benchmark.
2024-11-01T17:51:30.1336722Z     
2024-11-01T17:51:30.1336836Z     Example::
2024-11-01T17:51:30.1336946Z     
2024-11-01T17:51:30.1337117Z         >>> # xdoctest: +SKIP("undefined vars")
2024-11-01T17:51:30.1337319Z         >>> from torch.utils import ThroughputBenchmark
2024-11-01T17:51:30.1337513Z         >>> bench = ThroughputBenchmark(my_module)
2024-11-01T17:51:30.1337817Z         >>> # Pre-populate benchmark's data set with the inputs
2024-11-01T17:51:30.1337961Z         >>> for input in inputs:
2024-11-01T17:51:30.1338291Z         ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
2024-11-01T17:51:30.1338491Z         ...     bench.add_input(input[0], x2=input[1])
2024-11-01T17:51:30.1338769Z         >>> # Inputs supplied above are randomly used during the execution
2024-11-01T17:51:30.1338908Z         >>> stats = bench.benchmark(
2024-11-01T17:51:30.1339144Z         ...     num_calling_threads=4,
2024-11-01T17:51:30.1339309Z         ...     num_warmup_iters = 100,
2024-11-01T17:51:30.1339447Z         ...     num_iters = 1000,
2024-11-01T17:51:30.1339550Z         ... )
2024-11-01T17:51:30.1339820Z         >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
2024-11-01T17:51:30.1340075Z         >>> print("Number of iterations: {}".format(stats.num_iters))
2024-11-01T17:51:30.1340171Z     
2024-11-01T17:51:30.1340621Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1340716Z     
2024-11-01T17:51:30.1340851Z       warnings.warn(msg)
2024-11-01T17:51:30.1340946Z     
2024-11-01T17:51:30.1341184Z --- Parse Warning: 103 / 103 ---
2024-11-01T17:51:30.1342728Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/utils/data/distributed.py line=17.
2024-11-01T17:51:30.1343180Z     Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines')
2024-11-01T17:51:30.1343478Z     Sampler that restricts data loading to a subset of the dataset.
2024-11-01T17:51:30.1343576Z     
2024-11-01T17:51:30.1343780Z         It is especially useful in conjunction with
2024-11-01T17:51:30.1344150Z         :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each
2024-11-01T17:51:30.1344558Z         process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
2024-11-01T17:51:30.1344893Z         :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
2024-11-01T17:51:30.1345075Z         original dataset that is exclusive to it.
2024-11-01T17:51:30.1345242Z     
2024-11-01T17:51:30.1345354Z         .. note::
2024-11-01T17:51:30.1345726Z             Dataset is assumed to be of constant size and that any instance of it always
2024-11-01T17:51:30.1345923Z             returns the same elements in the same order.
2024-11-01T17:51:30.1346036Z     
2024-11-01T17:51:30.1346143Z         Args:
2024-11-01T17:51:30.1346315Z             dataset: Dataset used for sampling.
2024-11-01T17:51:30.1346634Z             num_replicas (int, optional): Number of processes participating in
2024-11-01T17:51:30.1346986Z                 distributed training. By default, :attr:`world_size` is retrieved from the
2024-11-01T17:51:30.1347149Z                 current distributed group.
2024-11-01T17:51:30.1347534Z             rank (int, optional): Rank of the current process within :attr:`num_replicas`.
2024-11-01T17:51:30.1347849Z                 By default, :attr:`rank` is retrieved from the current distributed
2024-11-01T17:51:30.1347959Z                 group.
2024-11-01T17:51:30.1348288Z             shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
2024-11-01T17:51:30.1348414Z                 indices.
2024-11-01T17:51:30.1348699Z             seed (int, optional): random seed used to shuffle the sampler if
2024-11-01T17:51:30.1349007Z                 :attr:`shuffle=True`. This number should be identical across all
2024-11-01T17:51:30.1349246Z                 processes in the distributed group. Default: ``0``.
2024-11-01T17:51:30.1349577Z             drop_last (bool, optional): if ``True``, then the sampler will drop the
2024-11-01T17:51:30.1349879Z                 tail of the data to make it evenly divisible across the number of
2024-11-01T17:51:30.1350179Z                 replicas. If ``False``, the sampler will add extra indices to make
2024-11-01T17:51:30.1350502Z                 the data evenly divisible across the replicas. Default: ``False``.
2024-11-01T17:51:30.1350599Z     
2024-11-01T17:51:30.1350727Z         .. warning::
2024-11-01T17:51:30.1351002Z             In distributed mode, calling the :meth:`set_epoch` method at
2024-11-01T17:51:30.1351396Z             the beginning of each epoch **before** creating the :class:`DataLoader` iterator
2024-11-01T17:51:30.1351807Z             is necessary to make shuffling work properly across multiple epochs. Otherwise,
2024-11-01T17:51:30.1352028Z             the same ordering will be always used.
2024-11-01T17:51:30.1352125Z     
2024-11-01T17:51:30.1352238Z         Example::
2024-11-01T17:51:30.1352351Z     
2024-11-01T17:51:30.1352477Z             >>> # xdoctest: +SKIP
2024-11-01T17:51:30.1352801Z             >>> sampler = DistributedSampler(dataset) if is_distributed else None
2024-11-01T17:51:30.1353048Z             >>> loader = DataLoader(dataset, shuffle=(sampler is None),
2024-11-01T17:51:30.1353221Z             ...                     sampler=sampler)
2024-11-01T17:51:30.1353432Z             >>> for epoch in range(start_epoch, n_epochs):
2024-11-01T17:51:30.1353568Z             ...     if is_distributed:
2024-11-01T17:51:30.1353743Z             ...         sampler.set_epoch(epoch)
2024-11-01T17:51:30.1353968Z             ...     train(loader)
2024-11-01T17:51:30.1354085Z         
2024-11-01T17:51:30.1354531Z     Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0))
2024-11-01T17:51:30.1354631Z     
2024-11-01T17:51:30.1354769Z       warnings.warn(msg)
2024-11-01T17:51:30.1354868Z     
2024-11-01T17:51:30.1355006Z 
2024-11-01T17:51:30.1355223Z === Found 9 run-time warnings ===
2024-11-01T17:51:30.1355446Z --- Runtime Warning: 1 / 9 ---
2024-11-01T17:51:30.1355825Z example = <DocTest(torch._tensor Tensor.refine_names:0 ln 1334) at 0x7fe60e4ac380>
2024-11-01T17:51:30.1358097Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/_tensor.py:1352: 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.)
2024-11-01T17:51:30.1358267Z       return super().refine_names(names)
2024-11-01T17:51:30.1358365Z     
2024-11-01T17:51:30.1358606Z --- Runtime Warning: 2 / 9 ---
2024-11-01T17:51:30.1359039Z example = <DocTest(torch.library Library._impl_with_aoti_compile:0 ln 242) at 0x7fe60d7adeb0>
2024-11-01T17:51:30.1360117Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/library.py:279: UserWarning: Warning only once for all operators,  other operators may also be overridden.
2024-11-01T17:51:30.1360550Z       Overriding a previously registered kernel for the same operator and the same dispatch key
2024-11-01T17:51:30.1360929Z       operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor
2024-11-01T17:51:30.1361382Z         registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6
2024-11-01T17:51:30.1361503Z       dispatch key: CPU
2024-11-01T17:51:30.1362127Z       previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079
2024-11-01T17:51:30.1362916Z            new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:162.)
2024-11-01T17:51:30.1363223Z       impl_fn(self.ns, name.split("::")[-1], dispatch_key)
2024-11-01T17:51:30.1363323Z     
2024-11-01T17:51:30.1363564Z --- Runtime Warning: 3 / 9 ---
2024-11-01T17:51:30.1363904Z example = <DocTest(torch.nested as_nested_tensor:0 ln 57) at 0x7fe60d84d070>
2024-11-01T17:51:30.1365733Z     /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:180.)
2024-11-01T17:51:30.1366073Z       return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
2024-11-01T17:51:30.1366186Z     
2024-11-01T17:51:30.1366414Z --- Runtime Warning: 4 / 9 ---
2024-11-01T17:51:30.1366773Z example = <DocTest(torch.sparse as_sparse_gradcheck:0 ln 561) at 0x7fe60d84f440>
2024-11-01T17:51:30.1369514Z     <doctest:/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0>: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.)
2024-11-01T17:51:30.1369664Z     
2024-11-01T17:51:30.1369908Z --- Runtime Warning: 5 / 9 ---
2024-11-01T17:51:30.1370333Z example = <DocTest(torch._functorch.eager_transforms linearize:0 ln 1748) at 0x7fe60d84da00>
2024-11-01T17:51:30.1372775Z     /opt/conda/envs/py_3.12/lib/python3.12/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
2024-11-01T17:51:30.1373011Z       new_node = root_const_gm.graph.get_attr(in_node.target)
2024-11-01T17:51:30.1373127Z     
2024-11-01T17:51:30.1373350Z --- Runtime Warning: 6 / 9 ---
2024-11-01T17:51:30.1373778Z example = <DocTest(torch.nn.modules.transformer Transformer:0 ln 86) at 0x7fe606955b80>
2024-11-01T17:51:30.1375542Z     /opt/conda/envs/py_3.12/lib/python3.12/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)
2024-11-01T17:51:30.1375679Z       warnings.warn(
2024-11-01T17:51:30.1375776Z     
2024-11-01T17:51:30.1376033Z --- Runtime Warning: 7 / 9 ---
2024-11-01T17:51:30.1376514Z example = <DocTest(torch.nn.modules.transformer TransformerEncoder:0 ln 319) at 0x7fe606956060>
2024-11-01T17:51:30.1378289Z     /opt/conda/envs/py_3.12/lib/python3.12/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)
2024-11-01T17:51:30.1378425Z       warnings.warn(
2024-11-01T17:51:30.1378520Z     
2024-11-01T17:51:30.1378760Z --- Runtime Warning: 8 / 9 ---
2024-11-01T17:51:30.1379155Z example = <DocTest(torch.nn.utils.weight_norm weight_norm:0 ln 133) at 0x7fe6071387a0>
2024-11-01T17:51:30.1380544Z     /opt/conda/envs/py_3.12/lib/python3.12/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`.
2024-11-01T17:51:30.1380707Z       WeightNorm.apply(module, name, dim)
2024-11-01T17:51:30.1380804Z     
2024-11-01T17:51:30.1381040Z --- Runtime Warning: 9 / 9 ---
2024-11-01T17:51:30.1381475Z example = <DocTest(torch.nn.utils.weight_norm remove_weight_norm:0 ln 155) at 0x7fe607138ad0>
2024-11-01T17:51:30.1382841Z     /opt/conda/envs/py_3.12/lib/python3.12/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`.
2024-11-01T17:51:30.1383001Z       WeightNorm.apply(module, name, dim)
2024-11-01T17:51:30.1383110Z     
2024-11-01T17:51:30.1383507Z === 338 passed, 366 skipped, 112 warnings in 13.31 seconds ===
2024-11-01T17:51:30.1383855Z Running test_autoload_disable 1/1 ... [2024-11-01 17:51:29.880253]
2024-11-01T17:51:32.9905019Z running install
2024-11-01T17:51:32.9907880Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2024-11-01T17:51:32.9910055Z !!
2024-11-01T17:51:32.9910249Z 
2024-11-01T17:51:32.9910456Z         ********************************************************************************
2024-11-01T17:51:32.9911065Z         Please avoid running ``setup.py`` directly.
2024-11-01T17:51:32.9911915Z         Instead, use pypa/build, pypa/installer or other
2024-11-01T17:51:32.9912617Z         standards-based tools.
2024-11-01T17:51:32.9912901Z 
2024-11-01T17:51:32.9913478Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2024-11-01T17:51:32.9914413Z         ********************************************************************************
2024-11-01T17:51:32.9914824Z 
2024-11-01T17:51:32.9914927Z !!
2024-11-01T17:51:32.9915230Z   self.initialize_options()
2024-11-01T17:51:33.0056192Z running build
2024-11-01T17:51:33.0056649Z running build_py
2024-11-01T17:51:33.0143295Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:51:33.0145229Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:51:33.0148694Z running build_ext
2024-11-01T17:51:33.1039689Z building 'torch_test_cpp_extension.cpp' extension
2024-11-01T17:51:33.1040743Z creating build/temp.linux-x86_64-cpython-312
2024-11-01T17:51:33.1049734Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:51:34.4113505Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2024-11-01T17:51:34.4116431Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2024-11-01T17:51:34.4118084Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9,
2024-11-01T17:51:34.4118913Z                  from extension.cpp:1:
2024-11-01T17:51:34.4120403Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2024-11-01T17:51:34.4121906Z extension.cpp:45:53:   required from here
2024-11-01T17:51:34.4124406Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2024-11-01T17:51:34.4126289Z  1539 | class class_ : public detail::generic_type {
2024-11-01T17:51:34.4126876Z       |       ^~~~~~
2024-11-01T17:51:34.4129277Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2024-11-01T17:51:34.4131446Z extension.cpp:45:53:   required from here
2024-11-01T17:51:34.4136274Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2024-11-01T17:51:34.4140830Z  1599 |             with_internals([&](internals &internals) {
2024-11-01T17:51:34.4141458Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:34.4142353Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2024-11-01T17:51:34.4143312Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:34.4144086Z  1601 |                                                       : internals.registered_types_cpp;
2024-11-01T17:51:34.4144786Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:34.4145448Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2024-11-01T17:51:34.4146112Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:34.4146802Z  1603 |                     = instances[std::type_index(typeid(type))];
2024-11-01T17:51:34.4147473Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:34.4148014Z  1604 |             });
2024-11-01T17:51:34.4148343Z       |             ~               
2024-11-01T17:51:34.4154117Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:51:34.9585007Z building 'torch_test_cpp_extension.maia' extension
2024-11-01T17:51:34.9591974Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:51:36.0985276Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:51:36.5924763Z building 'torch_test_cpp_extension.rng' extension
2024-11-01T17:51:36.5931835Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:51:37.9090654Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:51:37.9092552Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:51:37.9093887Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:51:37.9095165Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:51:37.9096623Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:51:37.9097582Z                  from rng_extension.cpp:6:
2024-11-01T17:51:37.9099115Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1119: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:37.9100357Z  1119 | # pragma unroll
2024-11-01T17:51:37.9100783Z       | 
2024-11-01T17:51:37.9101971Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1159,
2024-11-01T17:51:37.9103537Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:51:37.9105133Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:51:37.9106930Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:51:37.9108343Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:51:37.9110120Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:51:37.9111168Z                  from rng_extension.cpp:6:
2024-11-01T17:51:37.9112609Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:37.9113950Z    59 | #pragma unroll
2024-11-01T17:51:37.9114295Z       | 
2024-11-01T17:51:37.9115579Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:37.9116774Z    72 | #pragma unroll
2024-11-01T17:51:37.9117184Z       | 
2024-11-01T17:51:37.9118384Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:37.9119656Z    87 | #pragma unroll
2024-11-01T17:51:37.9119969Z       | 
2024-11-01T17:51:37.9120958Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1160,
2024-11-01T17:51:37.9122712Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:51:37.9124338Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:51:37.9126011Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:51:37.9127482Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:51:37.9129096Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:51:37.9130129Z                  from rng_extension.cpp:6:
2024-11-01T17:51:37.9131596Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:37.9132868Z   153 | #pragma unroll
2024-11-01T17:51:37.9133236Z       | 
2024-11-01T17:51:37.9139031Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:51:38.4673623Z running install_lib
2024-11-01T17:51:38.4761626Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:51:38.4865770Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:51:38.4967840Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:51:38.5079022Z running install_egg_info
2024-11-01T17:51:38.5277787Z running egg_info
2024-11-01T17:51:38.5354483Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2024-11-01T17:51:38.5357997Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2024-11-01T17:51:38.5360417Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2024-11-01T17:51:38.5362639Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2024-11-01T17:51:38.5444315Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:51:38.5453489Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:51:38.5455928Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it)
2024-11-01T17:51:38.5457940Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info
2024-11-01T17:51:38.5464693Z running install_scripts
2024-11-01T17:51:42.3690271Z 
2024-11-01T17:51:42.3691017Z Running tests...
2024-11-01T17:51:42.3691788Z ----------------------------------------------------------------------
2024-11-01T17:51:42.4900303Z .
2024-11-01T17:51:42.4901216Z ----------------------------------------------------------------------
2024-11-01T17:51:42.4901772Z Ran 1 test in 0.121s
2024-11-01T17:51:42.4902003Z 
2024-11-01T17:51:42.4902113Z OK
2024-11-01T17:51:42.4902269Z 
2024-11-01T17:51:42.4902404Z Generating XML reports...
2024-11-01T17:51:43.2443756Z Running test_autoload_enable 1/1 ... [2024-11-01 17:51:43.243929]
2024-11-01T17:51:46.3610355Z running install
2024-11-01T17:51:46.3613092Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
2024-11-01T17:51:46.3614261Z !!
2024-11-01T17:51:46.3614444Z 
2024-11-01T17:51:46.3614665Z         ********************************************************************************
2024-11-01T17:51:46.3615278Z         Please avoid running ``setup.py`` directly.
2024-11-01T17:51:46.3615883Z         Instead, use pypa/build, pypa/installer or other
2024-11-01T17:51:46.3616534Z         standards-based tools.
2024-11-01T17:51:46.3616820Z 
2024-11-01T17:51:46.3617364Z         See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
2024-11-01T17:51:46.3618191Z         ********************************************************************************
2024-11-01T17:51:46.3618581Z 
2024-11-01T17:51:46.3618696Z !!
2024-11-01T17:51:46.3618991Z   self.initialize_options()
2024-11-01T17:51:46.3759244Z running build
2024-11-01T17:51:46.3759621Z running build_py
2024-11-01T17:51:46.3847605Z creating build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:51:46.3850047Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension
2024-11-01T17:51:46.3854560Z running build_ext
2024-11-01T17:51:46.4730383Z building 'torch_test_cpp_extension.cpp' extension
2024-11-01T17:51:46.4731944Z creating build/temp.linux-x86_64-cpython-312
2024-11-01T17:51:46.4742209Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:51:47.5975340Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/Exceptions.h:12,
2024-11-01T17:51:47.5977307Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11,
2024-11-01T17:51:47.5978698Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/extension.h:9,
2024-11-01T17:51:47.5979499Z                  from extension.cpp:1:
2024-11-01T17:51:47.5980985Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_<MatrixMultiplier>’:
2024-11-01T17:51:47.5982185Z extension.cpp:45:53:   required from here
2024-11-01T17:51:47.5986244Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_<MatrixMultiplier>’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes]
2024-11-01T17:51:47.5988004Z  1539 | class class_ : public detail::generic_type {
2024-11-01T17:51:47.5988483Z       |       ^~~~~~
2024-11-01T17:51:47.5990705Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’:
2024-11-01T17:51:47.5992629Z extension.cpp:45:53:   required from here
2024-11-01T17:51:47.5997185Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>’ declared with greater visibility than the type of its field ‘pybind11::class_< <template-parameter-1-1>, <template-parameter-1-2> >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::<lambda(pybind11::detail::internals&)>::<record capture>’ [-Wattributes]
2024-11-01T17:51:47.6000954Z  1599 |             with_internals([&](internals &internals) {
2024-11-01T17:51:47.6001517Z       |                            ^~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:47.6002287Z  1600 |                 auto &instances = record.module_local ? get_local_internals().registered_types_cpp
2024-11-01T17:51:47.6003122Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:47.6003816Z  1601 |                                                       : internals.registered_types_cpp;
2024-11-01T17:51:47.6004446Z       |                                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:47.6005086Z  1602 |                 instances[std::type_index(typeid(type_alias))]
2024-11-01T17:51:47.6005691Z       |                 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:47.6006282Z  1603 |                     = instances[std::type_index(typeid(type))];
2024-11-01T17:51:47.6007170Z       |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2024-11-01T17:51:47.6007640Z  1604 |             });
2024-11-01T17:51:47.6007967Z       |             ~               
2024-11-01T17:51:47.6013319Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:51:48.1357644Z building 'torch_test_cpp_extension.maia' extension
2024-11-01T17:51:48.1365143Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:51:49.2308050Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/maia_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:51:49.7265713Z building 'torch_test_cpp_extension.rng' extension
2024-11-01T17:51:49.7273006Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/TH -I/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.12/include/python3.12 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-312/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
2024-11-01T17:51:51.0628036Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:51:51.0630430Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:51:51.0631758Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:51:51.0633723Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:51:51.0635361Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:51:51.0636339Z                  from rng_extension.cpp:6:
2024-11-01T17:51:51.0637682Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1119: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:51.0638903Z  1119 | # pragma unroll
2024-11-01T17:51:51.0639313Z       | 
2024-11-01T17:51:51.0640541Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1159,
2024-11-01T17:51:51.0642270Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:51:51.0643900Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:51:51.0645208Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:51:51.0646479Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:51:51.0647928Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:51:51.0648895Z                  from rng_extension.cpp:6:
2024-11-01T17:51:51.0650158Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:51.0651265Z    59 | #pragma unroll
2024-11-01T17:51:51.0651595Z       | 
2024-11-01T17:51:51.0652694Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:51.0653797Z    72 | #pragma unroll
2024-11-01T17:51:51.0654122Z       | 
2024-11-01T17:51:51.0655215Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:51.0656310Z    87 | #pragma unroll
2024-11-01T17:51:51.0656741Z       | 
2024-11-01T17:51:51.0657708Z In file included from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1160,
2024-11-01T17:51:51.0659242Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128_float_neon.h:7,
2024-11-01T17:51:51.0660698Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec128/vec128.h:7,
2024-11-01T17:51:51.0662006Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec.h:6,
2024-11-01T17:51:51.0663267Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/Loops.h:37,
2024-11-01T17:51:51.0664719Z                  from /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9,
2024-11-01T17:51:51.0665684Z                  from rng_extension.cpp:6:
2024-11-01T17:51:51.0666986Z /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll  [-Wunknown-pragmas]
2024-11-01T17:51:51.0668118Z   153 | #pragma unroll
2024-11-01T17:51:51.0668445Z       | 
2024-11-01T17:51:51.0673695Z g++ -pthread -B /opt/conda/envs/py_3.12/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -fPIC -O2 -isystem /opt/conda/envs/py_3.12/include -pthread -B /opt/conda/envs/py_3.12/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib -Wl,-rpath,/opt/conda/envs/py_3.12/lib -Wl,-rpath-link,/opt/conda/envs/py_3.12/lib -L/opt/conda/envs/py_3.12/lib build/temp.linux-x86_64-cpython-312/rng_extension.o -L/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so
2024-11-01T17:51:51.6062023Z running install_lib
2024-11-01T17:51:51.6150244Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/cpp.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:51:51.6256574Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/maia.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:51:51.6361783Z copying build/lib.linux-x86_64-cpython-312/torch_test_cpp_extension/rng.cpython-312-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension
2024-11-01T17:51:51.6471637Z running install_egg_info
2024-11-01T17:51:51.6673325Z running egg_info
2024-11-01T17:51:51.6753075Z writing torch_test_cpp_extension.egg-info/PKG-INFO
2024-11-01T17:51:51.6757060Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt
2024-11-01T17:51:51.6759602Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt
2024-11-01T17:51:51.6762164Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt
2024-11-01T17:51:51.6846597Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:51:51.6856376Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt'
2024-11-01T17:51:51.6858953Z removing './install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info' (and everything under it)
2024-11-01T17:51:51.6861202Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch_test_cpp_extension-0.0.0-py3.12.egg-info
2024-11-01T17:51:51.6868577Z running install_scripts
2024-11-01T17:51:55.5301124Z 
2024-11-01T17:51:55.5301643Z Running tests...
2024-11-01T17:51:55.5302413Z ----------------------------------------------------------------------
2024-11-01T17:51:55.6499065Z .
2024-11-01T17:51:55.6499969Z ----------------------------------------------------------------------
2024-11-01T17:51:55.6500604Z Ran 1 test in 0.120s
2024-11-01T17:51:55.6500820Z 
2024-11-01T17:51:55.6500932Z OK
2024-11-01T17:51:55.6501071Z 
2024-11-01T17:51:55.6501207Z Generating XML reports...
2024-11-01T17:51:56.3925092Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-11-01 17:51:56.392132]
2024-11-01T17:51:56.3925766Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:51:56.3928431Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.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'] ... [2024-11-01 17:51:56.392543]
2024-11-01T17:52:01.6084644Z 
2024-11-01T17:52:01.6086616Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_644550c975865836_.log
2024-11-01T17:52:01.6088285Z 
2024-11-01T17:52:01.6089288Z Running dynamo/test_config 1/1 ... [2024-11-01 17:52:01.608618]
2024-11-01T17:52:01.6090208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:01.6094276Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_config.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'] ... [2024-11-01 17:52:01.609009]
2024-11-01T17:52:05.4300544Z 
2024-11-01T17:52:05.4302165Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_a02dfdbbf4609c9d_.log
2024-11-01T17:52:05.4303365Z 
2024-11-01T17:52:05.4303988Z Running dynamo/test_interop 1/1 ... [2024-11-01 17:52:05.430190]
2024-11-01T17:52:05.4304613Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:05.4308664Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_interop.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'] ... [2024-11-01 17:52:05.430523]
2024-11-01T17:52:09.2305289Z 
2024-11-01T17:52:09.2307712Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_f7e47bc584599b7b_.log
2024-11-01T17:52:09.2309297Z 
2024-11-01T17:52:09.2310022Z Running dynamo/test_after_aot 1/1 ... [2024-11-01 17:52:09.230406]
2024-11-01T17:52:09.2311460Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:09.2313697Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_after_aot.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'] ... [2024-11-01 17:52:09.230865]
2024-11-01T17:52:13.0501634Z 
2024-11-01T17:52:13.0503782Z dynamo/test_after_aot 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_after_aot_1.1_b2be89674ad9940c_.log
2024-11-01T17:52:13.0505501Z 
2024-11-01T17:52:13.0506263Z Running dynamo/test_export_mutations 1/1 ... [2024-11-01 17:52:13.050273]
2024-11-01T17:52:13.0507520Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:13.0511737Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export_mutations.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'] ... [2024-11-01 17:52:13.050753]
2024-11-01T17:52:16.8434897Z 
2024-11-01T17:52:16.8436688Z dynamo/test_export_mutations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_mutations_1.1_0e3bdf4643cb7642_.log
2024-11-01T17:52:16.8437979Z 
2024-11-01T17:52:16.8438335Z Running dynamo/test_misc 1/1 ... [2024-11-01 17:52:16.843633]
2024-11-01T17:52:16.8438908Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:16.8443687Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:52:16.844032]
2024-11-01T17:52:21.7611043Z 
2024-11-01T17:52:21.7612753Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_c73d52cd65f7a842_.log
2024-11-01T17:52:21.7614085Z 
2024-11-01T17:52:21.7614755Z Running dynamo/test_export 1/1 ... [2024-11-01 17:52:21.761242]
2024-11-01T17:52:21.7615353Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:21.7619300Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export.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'] ... [2024-11-01 17:52:21.761608]
2024-11-01T17:52:25.6132744Z 
2024-11-01T17:52:25.6135269Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_18821a3afa81d9b4_.log
2024-11-01T17:52:25.6137018Z 
2024-11-01T17:52:25.6137612Z Running dynamo/test_modules 1/1 ... [2024-11-01 17:52:25.613491]
2024-11-01T17:52:25.6138545Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:25.6143729Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_modules.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'] ... [2024-11-01 17:52:25.613962]
2024-11-01T17:52:29.4956311Z 
2024-11-01T17:52:29.4958380Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_d112e1df4d670da6_.log
2024-11-01T17:52:29.4959860Z 
2024-11-01T17:52:29.4960613Z Running dynamo/test_verify_correctness 1/1 ... [2024-11-01 17:52:29.495792]
2024-11-01T17:52:29.4961280Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:29.4965410Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_verify_correctness.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'] ... [2024-11-01 17:52:29.496179]
2024-11-01T17:52:33.2774772Z 
2024-11-01T17:52:33.2776816Z dynamo/test_verify_correctness 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_verify_correctness_1.1_7630bdd28c682b36_.log
2024-11-01T17:52:33.2778719Z 
2024-11-01T17:52:33.2779233Z Running dynamo/test_higher_order_ops 1/1 ... [2024-11-01 17:52:33.277633]
2024-11-01T17:52:33.2779873Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:33.2783631Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:52:33.278027]
2024-11-01T17:52:37.1068094Z 
2024-11-01T17:52:37.1070388Z 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_1bd238c29a570549_.log
2024-11-01T17:52:37.1072223Z 
2024-11-01T17:52:37.1072820Z Running dynamo/test_exc 1/1 ... [2024-11-01 17:52:37.106949]
2024-11-01T17:52:37.1073711Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:37.1079022Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exc.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'] ... [2024-11-01 17:52:37.107444]
2024-11-01T17:52:40.9230760Z 
2024-11-01T17:52:40.9232550Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_0a5cd04876624cb5_.log
2024-11-01T17:52:40.9233975Z 
2024-11-01T17:52:40.9235592Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-11-01 17:52:40.923257]
2024-11-01T17:52:40.9236253Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:40.9240431Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:52:40.923674]
2024-11-01T17:52:44.6512874Z 
2024-11-01T17:52:44.6514865Z 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_83347a8f1705555b_.log
2024-11-01T17:52:44.6515945Z 
2024-11-01T17:52:44.6516737Z Running dynamo/test_utils 1/1 ... [2024-11-01 17:52:44.651461]
2024-11-01T17:52:44.6517318Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:44.6522344Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_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'] ... [2024-11-01 17:52:44.651838]
2024-11-01T17:52:48.4322977Z 
2024-11-01T17:52:48.4324834Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_6d9e044f4c604bc7_.log
2024-11-01T17:52:48.4325838Z 
2024-11-01T17:52:48.4327173Z Running dynamo/test_sdpa 1/1 ... [2024-11-01 17:52:48.432480]
2024-11-01T17:52:48.4327874Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:48.4332794Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:52:48.432907]
2024-11-01T17:52:52.2063605Z 
2024-11-01T17:52:52.2065554Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_8f0f20a46993c272_.log
2024-11-01T17:52:52.2066545Z 
2024-11-01T17:52:52.2067757Z Running dynamo/test_view 1/1 ... [2024-11-01 17:52:52.206556]
2024-11-01T17:52:52.2068333Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:52.2073031Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_view.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'] ... [2024-11-01 17:52:52.206973]
2024-11-01T17:52:56.0012156Z 
2024-11-01T17:52:56.0014446Z dynamo/test_view 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_view_1.1_8a757e978ffee5e1_.log
2024-11-01T17:52:56.0015949Z 
2024-11-01T17:52:56.0017156Z Running dynamo/test_profiler 1/1 ... [2024-11-01 17:52:56.001474]
2024-11-01T17:52:56.0018085Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:56.0023759Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.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'] ... [2024-11-01 17:52:56.001943]
2024-11-01T17:52:59.7813552Z 
2024-11-01T17:52:59.7815134Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_b219a47ebf067313_.log
2024-11-01T17:52:59.7816289Z 
2024-11-01T17:52:59.7817137Z Running dynamo/test_deviceguard 1/1 ... [2024-11-01 17:52:59.781518]
2024-11-01T17:52:59.7817847Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:52:59.7822326Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:52:59.781899]
2024-11-01T17:53:03.6267992Z 
2024-11-01T17:53:03.6270689Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_43245597333b6b8f_.log
2024-11-01T17:53:03.6272120Z 
2024-11-01T17:53:03.6272557Z Running dynamo/test_model_output 1/1 ... [2024-11-01 17:53:03.626906]
2024-11-01T17:53:03.6273175Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:03.6276701Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_model_output.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'] ... [2024-11-01 17:53:03.627292]
2024-11-01T17:53:07.4448909Z 
2024-11-01T17:53:07.4450940Z dynamo/test_model_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_model_output_1.1_db95e63ce0edde20_.log
2024-11-01T17:53:07.4452346Z 
2024-11-01T17:53:07.4453210Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2024-11-01 17:53:07.445032]
2024-11-01T17:53:07.4453935Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:07.4457974Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_cudagraphs_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'] ... [2024-11-01 17:53:07.445441]
2024-11-01T17:53:11.2070085Z 
2024-11-01T17:53:11.2072239Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_adcd1d951466b710_.log
2024-11-01T17:53:11.2073769Z 
2024-11-01T17:53:11.2074299Z Running dynamo/test_bytecode_utils 1/1 ... [2024-11-01 17:53:11.207171]
2024-11-01T17:53:11.2074933Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:11.2079146Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:53:11.207575]
2024-11-01T17:53:14.9978557Z 
2024-11-01T17:53:14.9980594Z 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_b4bfd2b61efd2e47_.log
2024-11-01T17:53:14.9982604Z 
2024-11-01T17:53:14.9983084Z Running test_model_exports_to_core_aten 1/1 ... [2024-11-01 17:53:14.998010]
2024-11-01T17:53:14.9983760Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:14.9988011Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_model_exports_to_core_aten.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'] ... [2024-11-01 17:53:14.998398]
2024-11-01T17:53:19.1695606Z 
2024-11-01T17:53:19.1697659Z test_model_exports_to_core_aten 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_model_exports_to_core_aten_1.1_d6f8e39b5e988d74_.log
2024-11-01T17:53:19.1698885Z Running 0 items in this shard:
2024-11-01T17:53:19.1699223Z 
2024-11-01T17:53:19.1700697Z Running test_namedtensor 1/1 ... [2024-11-01 17:53:19.169858]
2024-11-01T17:53:19.1701358Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:19.1705395Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_namedtensor.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'] ... [2024-11-01 17:53:19.170230]
2024-11-01T17:53:23.0908994Z 
2024-11-01T17:53:23.0910730Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_44c9e058dc41e6a6_.log
2024-11-01T17:53:23.0912149Z Running 0 items in this shard:
2024-11-01T17:53:23.0912504Z 
2024-11-01T17:53:23.0914146Z Running higher_order_ops/test_invoke_subgraph 1/1 ... [2024-11-01 17:53:23.091091]
2024-11-01T17:53:23.0915298Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:23.0918859Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_invoke_subgraph.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'] ... [2024-11-01 17:53:23.091475]
2024-11-01T17:53:27.1123902Z 
2024-11-01T17:53:27.1125993Z higher_order_ops/test_invoke_subgraph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_subgraph_1.1_3dddb2d9cf1aaafb_.log
2024-11-01T17:53:27.1127623Z Running 0 items in this shard:
2024-11-01T17:53:27.1127966Z 
2024-11-01T17:53:27.1128475Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2024-11-01 17:53:27.112548]
2024-11-01T17:53:27.1129166Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:27.1133383Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.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'] ... [2024-11-01 17:53:27.112926]
2024-11-01T17:53:31.1335154Z 
2024-11-01T17:53:31.1337443Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_3c9e05d83c9add4e_.log
2024-11-01T17:53:31.1338927Z Running 0 items in this shard:
2024-11-01T17:53:31.1339820Z 
2024-11-01T17:53:31.1340210Z Running test_cuda_sanitizer 1/1 ... [2024-11-01 17:53:31.133677]
2024-11-01T17:53:31.1340884Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:31.1343339Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_sanitizer.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'] ... [2024-11-01 17:53:31.134025]
2024-11-01T17:53:35.0061577Z 
2024-11-01T17:53:35.0063413Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_cc43a6b3a6b66341_.log
2024-11-01T17:53:35.0064860Z Running 0 items in this shard:
2024-11-01T17:53:35.0065741Z 
2024-11-01T17:53:35.0066238Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2024-11-01 17:53:35.006327]
2024-11-01T17:53:35.0066924Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:35.0070038Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_backward_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'] ... [2024-11-01 17:53:35.006672]
2024-11-01T17:53:38.7778621Z 
2024-11-01T17:53:38.7780489Z dynamo/test_backward_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backward_higher_order_ops_1.1_01e7e561f34e203d_.log
2024-11-01T17:53:38.7781861Z 
2024-11-01T17:53:38.7782217Z Running test_fx_passes 1/1 ... [2024-11-01 17:53:38.777997]
2024-11-01T17:53:38.7782773Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:38.7786782Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_passes.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'] ... [2024-11-01 17:53:38.778358]
2024-11-01T17:53:42.6987773Z 
2024-11-01T17:53:42.6989506Z test_fx_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_passes_1.1_dfd9034756e46940_.log
2024-11-01T17:53:42.6990745Z Running 0 items in this shard:
2024-11-01T17:53:42.6991374Z 
2024-11-01T17:53:42.6991968Z Running dynamo/test_trace_rules 1/1 ... [2024-11-01 17:53:42.698955]
2024-11-01T17:53:42.6992578Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:42.6996312Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:53:42.699303]
2024-11-01T17:53:46.4509613Z 
2024-11-01T17:53:46.4511201Z 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_5673cf113470352d_.log
2024-11-01T17:53:46.4512399Z 
2024-11-01T17:53:46.4513492Z Running distributions/test_constraints 1/1 ... [2024-11-01 17:53:46.451083]
2024-11-01T17:53:46.4514306Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:46.4517638Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_constraints.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'] ... [2024-11-01 17:53:46.451451]
2024-11-01T17:53:50.3719571Z 
2024-11-01T17:53:50.3721437Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_22d4ad73b4772b5e_.log
2024-11-01T17:53:50.3722848Z Running 0 items in this shard:
2024-11-01T17:53:50.3723303Z 
2024-11-01T17:53:50.3723709Z Running test_fx_reinplace_pass 1/1 ... [2024-11-01 17:53:50.372148]
2024-11-01T17:53:50.3724364Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:50.3728564Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_reinplace_pass.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'] ... [2024-11-01 17:53:50.372523]
2024-11-01T17:53:54.2428560Z 
2024-11-01T17:53:54.2430225Z test_fx_reinplace_pass 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_reinplace_pass_1.1_02245ad1f81ac16f_.log
2024-11-01T17:53:54.2431485Z Running 0 items in this shard:
2024-11-01T17:53:54.2431838Z 
2024-11-01T17:53:54.2432968Z Running higher_order_ops/test_with_effects 1/1 ... [2024-11-01 17:53:54.243094]
2024-11-01T17:53:54.2433635Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:54.2437560Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_with_effects.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'] ... [2024-11-01 17:53:54.243442]
2024-11-01T17:53:58.4643356Z 
2024-11-01T17:53:58.4645390Z higher_order_ops/test_with_effects 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_with_effects_1.1_db9fce67dd4a49b8_.log
2024-11-01T17:53:58.4646763Z Running 0 items in this shard:
2024-11-01T17:53:58.4647124Z 
2024-11-01T17:53:58.4648278Z Running torch_np/numpy_tests/lib/test_type_check 1/1 ... [2024-11-01 17:53:58.464596]
2024-11-01T17:53:58.4648989Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:53:58.4652815Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_type_check.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'] ... [2024-11-01 17:53:58.464974]
2024-11-01T17:54:02.4354648Z 
2024-11-01T17:54:02.4356805Z torch_np/numpy_tests/lib/test_type_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_0fc9610f3586d480_.log
2024-11-01T17:54:02.4358569Z Running 0 items in this shard:
2024-11-01T17:54:02.4359099Z 
2024-11-01T17:54:02.4359880Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2024-11-01 17:54:02.435667]
2024-11-01T17:54:02.4360657Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:02.4363780Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.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'] ... [2024-11-01 17:54:02.436052]
2024-11-01T17:54:06.4065955Z 
2024-11-01T17:54:06.4068160Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_1451bae8edc16be8_.log
2024-11-01T17:54:06.4070235Z Running 0 items in this shard:
2024-11-01T17:54:06.4070662Z 
2024-11-01T17:54:06.4071065Z Running dynamo/test_recompile_ux 1/1 ... [2024-11-01 17:54:06.406802]
2024-11-01T17:54:06.4071680Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:06.4075360Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:54:06.407149]
2024-11-01T17:54:10.1794383Z 
2024-11-01T17:54:10.1796029Z 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_2bfa1fc048066ea7_.log
2024-11-01T17:54:10.1797451Z 
2024-11-01T17:54:10.1798286Z Running torch_np/numpy_tests/core/test_indexing 1/1 ... [2024-11-01 17:54:10.179598]
2024-11-01T17:54:10.1799065Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:10.1802954Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_indexing.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'] ... [2024-11-01 17:54:10.179960]
2024-11-01T17:54:14.1004160Z 
2024-11-01T17:54:14.1006421Z torch_np/numpy_tests/core/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_6a58abcf69c2d912_.log
2024-11-01T17:54:14.1008524Z Running 0 items in this shard:
2024-11-01T17:54:14.1008898Z 
2024-11-01T17:54:14.1009600Z Running torch_np/numpy_tests/lib/test_function_base 1/1 ... [2024-11-01 17:54:14.100547]
2024-11-01T17:54:14.1010846Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:14.1013504Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_function_base.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'] ... [2024-11-01 17:54:14.100923]
2024-11-01T17:54:18.1719890Z 
2024-11-01T17:54:18.1721945Z torch_np/numpy_tests/lib/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_694e5bf70be670da_.log
2024-11-01T17:54:18.1735020Z Running 0 items in this shard:
2024-11-01T17:54:18.1735592Z 
2024-11-01T17:54:18.1736054Z Running test_legacy_vmap 1/1 ... [2024-11-01 17:54:18.172151]
2024-11-01T17:54:18.1736620Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:18.1738464Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_legacy_vmap.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'] ... [2024-11-01 17:54:18.172550]
2024-11-01T17:54:21.9930125Z 
2024-11-01T17:54:21.9932007Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_d6d3c96427965199_.log
2024-11-01T17:54:21.9933153Z Running 0 items in this shard:
2024-11-01T17:54:21.9933637Z 
2024-11-01T17:54:21.9934609Z Running dynamo/test_hooks 1/1 ... [2024-11-01 17:54:21.993194]
2024-11-01T17:54:21.9935310Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:21.9939019Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_hooks.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'] ... [2024-11-01 17:54:21.993561]
2024-11-01T17:54:25.7421714Z 
2024-11-01T17:54:25.7423647Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_88060e1a7da33e9c_.log
2024-11-01T17:54:25.7424865Z 
2024-11-01T17:54:25.7425894Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2024-11-01 17:54:25.742308]
2024-11-01T17:54:25.7426630Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:25.7429920Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.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'] ... [2024-11-01 17:54:25.742676]
2024-11-01T17:54:29.6633357Z 
2024-11-01T17:54:29.6635555Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_a3ef93eb0891d207_.log
2024-11-01T17:54:29.6637290Z Running 0 items in this shard:
2024-11-01T17:54:29.6637583Z 
2024-11-01T17:54:29.6638187Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2024-11-01 17:54:29.663527]
2024-11-01T17:54:29.6638994Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:29.6642950Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.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'] ... [2024-11-01 17:54:29.663896]
2024-11-01T17:54:33.6346982Z 
2024-11-01T17:54:33.6349517Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_ef1f9ae00107b612_.log
2024-11-01T17:54:33.6351499Z Running 0 items in this shard:
2024-11-01T17:54:33.6351805Z 
2024-11-01T17:54:33.6352172Z Running test_cuda_multigpu 1/1 ... [2024-11-01 17:54:33.634878]
2024-11-01T17:54:33.6353032Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:33.6355926Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.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'] ... [2024-11-01 17:54:33.635249]
2024-11-01T17:54:37.5540848Z 
2024-11-01T17:54:37.5543119Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_11e846f664f1dc32_.log
2024-11-01T17:54:37.5545003Z Running 0 items in this shard:
2024-11-01T17:54:37.5545482Z 
2024-11-01T17:54:37.5546165Z Running profiler/test_profiler_tree 1/1 ... [2024-11-01 17:54:37.554296]
2024-11-01T17:54:37.5547236Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:37.5551914Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_profiler_tree.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'] ... [2024-11-01 17:54:37.554738]
2024-11-01T17:54:41.4753571Z 
2024-11-01T17:54:41.4755352Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_e21832d15e757e50_.log
2024-11-01T17:54:41.4756668Z Running 0 items in this shard:
2024-11-01T17:54:41.4756976Z 
2024-11-01T17:54:41.4757570Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2024-11-01 17:54:41.475526]
2024-11-01T17:54:41.4758574Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:41.4762434Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_helper.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'] ... [2024-11-01 17:54:41.475884]
2024-11-01T17:54:45.3964477Z 
2024-11-01T17:54:45.3966370Z torch_np/numpy_tests/fft/test_helper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_245c4edfe5557cfc_.log
2024-11-01T17:54:45.3968014Z Running 0 items in this shard:
2024-11-01T17:54:45.3968357Z 
2024-11-01T17:54:45.3968835Z Running torch_np/test_scalars_0D_arrays 1/1 ... [2024-11-01 17:54:45.396568]
2024-11-01T17:54:45.3969562Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:45.3972446Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_scalars_0D_arrays.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'] ... [2024-11-01 17:54:45.396912]
2024-11-01T17:54:49.2672468Z 
2024-11-01T17:54:49.2674537Z torch_np/test_scalars_0D_arrays 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_scalars_0D_arrays_1.1_f0f9103f04f86261_.log
2024-11-01T17:54:49.2676033Z Running 0 items in this shard:
2024-11-01T17:54:49.2676334Z 
2024-11-01T17:54:49.2676856Z Running profiler/test_memory_profiler 1/1 ... [2024-11-01 17:54:49.267435]
2024-11-01T17:54:49.2677582Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:49.2681783Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_memory_profiler.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'] ... [2024-11-01 17:54:49.267801]
2024-11-01T17:54:53.1883911Z 
2024-11-01T17:54:53.1885905Z profiler/test_memory_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_memory_profiler_1.1_6e3993150becfdf8_.log
2024-11-01T17:54:53.1887413Z Running 0 items in this shard:
2024-11-01T17:54:53.1887775Z 
2024-11-01T17:54:53.1888342Z Running torch_np/numpy_tests/core/test_scalar_ctors 1/1 ... [2024-11-01 17:54:53.188536]
2024-11-01T17:54:53.1889387Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:53.1892387Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_ctors.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'] ... [2024-11-01 17:54:53.188900]
2024-11-01T17:54:57.1095793Z 
2024-11-01T17:54:57.1097714Z torch_np/numpy_tests/core/test_scalar_ctors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_851e81f08eac54ff_.log
2024-11-01T17:54:57.1099514Z Running 0 items in this shard:
2024-11-01T17:54:57.1099803Z 
2024-11-01T17:54:57.1100287Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2024-11-01 17:54:57.109740]
2024-11-01T17:54:57.1100980Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:54:57.1104483Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraypad.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'] ... [2024-11-01 17:54:57.110111]
2024-11-01T17:55:01.0311076Z 
2024-11-01T17:55:01.0313168Z torch_np/numpy_tests/lib/test_arraypad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_3966850fb2ea7725_.log
2024-11-01T17:55:01.0315031Z Running 0 items in this shard:
2024-11-01T17:55:01.0315615Z 
2024-11-01T17:55:01.0315963Z Running test_dataloader 1/1 ... [2024-11-01 17:55:01.031269]
2024-11-01T17:55:01.0316616Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:01.0320057Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_dataloader.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'] ... [2024-11-01 17:55:01.031652]
2024-11-01T17:55:05.1526322Z 
2024-11-01T17:55:05.1528093Z test_dataloader 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dataloader_1.1_1a8033082f3785f7_.log
2024-11-01T17:55:05.1529164Z Running 0 items in this shard:
2024-11-01T17:55:05.1529475Z 
2024-11-01T17:55:05.1639985Z Running dynamo/test_dynamic_shapes 1/1 ... [2024-11-01 17:55:05.163588]
2024-11-01T17:55:05.1640657Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:05.1644058Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_dynamic_shapes.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'] ... [2024-11-01 17:55:05.164050]
2024-11-01T17:55:05.1657724Z Running dynamo/test_config 1/1 ... [2024-11-01 17:55:05.165385]
2024-11-01T17:55:05.1658351Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:05.1659005Z Running dynamo/test_interop 1/1 ... [2024-11-01 17:55:05.165497]
2024-11-01T17:55:05.1659582Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:05.1662784Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_config.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'] ... [2024-11-01 17:55:05.165918]
2024-11-01T17:55:05.1666217Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_interop.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'] ... [2024-11-01 17:55:05.165936]
2024-11-01T17:55:09.1128860Z 
2024-11-01T17:55:09.1131081Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_dc6d79e692c45197_.log
2024-11-01T17:55:09.1132824Z 
2024-11-01T17:55:09.1359053Z 
2024-11-01T17:55:09.1361569Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_eb8c41a9484cfd77_.log
2024-11-01T17:55:09.1363196Z 
2024-11-01T17:55:09.1904947Z Failed to upload artifacts: [Errno 2] No such file or directory: '/var/lib/jenkins/workspace/test/test-reports/dynamo.test_config_1.1_ywfq83gm_toprint.log'
2024-11-01T17:55:09.1907103Z Uploading artifacts took 0.08 seconds
2024-11-01T17:55:09.8941176Z Uploading artifacts took 0.70 seconds
2024-11-01T17:55:10.1425537Z 
2024-11-01T17:55:10.1427492Z dynamo/test_dynamic_shapes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_dynamic_shapes_1.1_62d3222b75545f00_.log
2024-11-01T17:55:10.1428718Z 
2024-11-01T17:55:13.3519729Z Running dynamo/test_after_aot 1/1 ... [2024-11-01 17:55:13.351449]
2024-11-01T17:55:13.3520640Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:13.3523017Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_after_aot.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'] ... [2024-11-01 17:55:13.351888]
2024-11-01T17:55:13.3803160Z Running dynamo/test_export_mutations 1/1 ... [2024-11-01 17:55:13.379842]
2024-11-01T17:55:13.3804015Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:13.3807276Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export_mutations.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'] ... [2024-11-01 17:55:13.380304]
2024-11-01T17:55:14.4099629Z Running dynamo/test_misc 1/1 ... [2024-11-01 17:55:14.409443]
2024-11-01T17:55:14.4100693Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:14.4102812Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:55:14.409831]
2024-11-01T17:55:17.3019958Z 
2024-11-01T17:55:17.3022288Z dynamo/test_after_aot 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_after_aot_1.1_3252f3d956fda34f_.log
2024-11-01T17:55:17.3024115Z 
2024-11-01T17:55:17.3087192Z 
2024-11-01T17:55:17.3089806Z dynamo/test_export_mutations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_mutations_1.1_74029d9841f266e5_.log
2024-11-01T17:55:17.3090913Z 
2024-11-01T17:55:19.4692231Z 
2024-11-01T17:55:19.4693880Z dynamo/test_misc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_misc_1.1_1ba3719d6da5fda1_.log
2024-11-01T17:55:19.4694922Z 
2024-11-01T17:55:21.5620641Z Running dynamo/test_export 1/1 ... [2024-11-01 17:55:21.561449]
2024-11-01T17:55:21.5621358Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:21.5624367Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_export.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'] ... [2024-11-01 17:55:21.561832]
2024-11-01T17:55:21.5991239Z Running dynamo/test_modules 1/1 ... [2024-11-01 17:55:21.598665]
2024-11-01T17:55:21.5991876Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:21.5994922Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_modules.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'] ... [2024-11-01 17:55:21.599117]
2024-11-01T17:55:23.6500363Z Running dynamo/test_verify_correctness 1/1 ... [2024-11-01 17:55:23.649482]
2024-11-01T17:55:23.6501464Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:23.6504626Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_verify_correctness.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'] ... [2024-11-01 17:55:23.649944]
2024-11-01T17:55:25.5517982Z 
2024-11-01T17:55:25.5519592Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_fa3e7a70975dd18e_.log
2024-11-01T17:55:25.5520600Z 
2024-11-01T17:55:25.6502426Z 
2024-11-01T17:55:25.6504302Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_480f5eb9feda628e_.log
2024-11-01T17:55:25.6505298Z 
2024-11-01T17:55:27.5462643Z 
2024-11-01T17:55:27.5464454Z dynamo/test_verify_correctness 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_verify_correctness_1.1_c0f4943a347bf11a_.log
2024-11-01T17:55:27.5465765Z 
2024-11-01T17:55:29.7140868Z Running dynamo/test_higher_order_ops 1/1 ... [2024-11-01 17:55:29.713542]
2024-11-01T17:55:29.7142823Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:29.7146668Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:55:29.713972]
2024-11-01T17:55:29.9210971Z Running dynamo/test_exc 1/1 ... [2024-11-01 17:55:29.920585]
2024-11-01T17:55:29.9212280Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:29.9215567Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_exc.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'] ... [2024-11-01 17:55:29.921059]
2024-11-01T17:55:31.7172604Z Running dynamo/test_fx_passes_pre_grad 1/1 ... [2024-11-01 17:55:31.716680]
2024-11-01T17:55:31.7173831Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:31.7177219Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:55:31.717124]
2024-11-01T17:55:33.7809520Z 
2024-11-01T17:55:33.7812638Z 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_bdcde1b03d966990_.log
2024-11-01T17:55:33.7815260Z 
2024-11-01T17:55:33.9654097Z 
2024-11-01T17:55:33.9655590Z dynamo/test_exc 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_exc_1.1_a455295925d55e8b_.log
2024-11-01T17:55:33.9656498Z 
2024-11-01T17:55:35.6802721Z 
2024-11-01T17:55:35.6805433Z 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_7429d6c83c02abe3_.log
2024-11-01T17:55:35.6806807Z 
2024-11-01T17:55:37.9749206Z Running dynamo/test_utils 1/1 ... [2024-11-01 17:55:37.974368]
2024-11-01T17:55:37.9750343Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:37.9753777Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_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'] ... [2024-11-01 17:55:37.974821]
2024-11-01T17:55:38.2597139Z Running dynamo/test_sdpa 1/1 ... [2024-11-01 17:55:38.259252]
2024-11-01T17:55:38.2598180Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:38.2602077Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:55:38.259658]
2024-11-01T17:55:39.9220313Z Running dynamo/test_view 1/1 ... [2024-11-01 17:55:39.921517]
2024-11-01T17:55:39.9221284Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:39.9224406Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_view.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'] ... [2024-11-01 17:55:39.921980]
2024-11-01T17:55:41.9498115Z 
2024-11-01T17:55:41.9500482Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_8a37e00b575a0b45_.log
2024-11-01T17:55:41.9502272Z 
2024-11-01T17:55:42.2275257Z 
2024-11-01T17:55:42.2277229Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_5383843b9137c19e_.log
2024-11-01T17:55:42.2278365Z 
2024-11-01T17:55:43.8696735Z 
2024-11-01T17:55:43.8699211Z dynamo/test_view 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_view_1.1_9d7d9fb7ae3b6c12_.log
2024-11-01T17:55:43.8700944Z 
2024-11-01T17:55:46.1491888Z Running dynamo/test_profiler 1/1 ... [2024-11-01 17:55:46.148632]
2024-11-01T17:55:46.1492943Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:46.1495884Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_profiler.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'] ... [2024-11-01 17:55:46.149063]
2024-11-01T17:55:46.4876568Z Running dynamo/test_deviceguard 1/1 ... [2024-11-01 17:55:46.487154]
2024-11-01T17:55:46.4877642Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:46.4881162Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:55:46.487650]
2024-11-01T17:55:48.0882905Z Running dynamo/test_model_output 1/1 ... [2024-11-01 17:55:48.087755]
2024-11-01T17:55:48.0883971Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:48.0886223Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_model_output.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'] ... [2024-11-01 17:55:48.088162]
2024-11-01T17:55:50.1714241Z 
2024-11-01T17:55:50.1716429Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_6e2b32e36d71abe4_.log
2024-11-01T17:55:50.1717923Z 
2024-11-01T17:55:50.4583345Z 
2024-11-01T17:55:50.4585917Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_feb50e1e6376e79c_.log
2024-11-01T17:55:50.4587886Z 
2024-11-01T17:55:52.1627785Z 
2024-11-01T17:55:52.1630574Z dynamo/test_model_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_model_output_1.1_51b7ed3a83f3ff35_.log
2024-11-01T17:55:52.1632447Z 
2024-11-01T17:55:54.3874505Z Running dynamo/test_cudagraphs_expandable_segments 1/1 ... [2024-11-01 17:55:54.386927]
2024-11-01T17:55:54.3875794Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:54.3878654Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_cudagraphs_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'] ... [2024-11-01 17:55:54.387356]
2024-11-01T17:55:54.7248628Z Running dynamo/test_bytecode_utils 1/1 ... [2024-11-01 17:55:54.724337]
2024-11-01T17:55:54.7249851Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:54.7252443Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:55:54.724819]
2024-11-01T17:55:56.3399584Z Running test_model_exports_to_core_aten 1/1 ... [2024-11-01 17:55:56.339431]
2024-11-01T17:55:56.3400461Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:55:56.3403075Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_model_exports_to_core_aten.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'] ... [2024-11-01 17:55:56.339888]
2024-11-01T17:55:58.3445782Z 
2024-11-01T17:55:58.3448096Z dynamo/test_cudagraphs_expandable_segments 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_4aa278471685c744_.log
2024-11-01T17:55:58.3449528Z 
2024-11-01T17:55:58.7026309Z 
2024-11-01T17:55:58.7029408Z 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_4765fe43d6ace71d_.log
2024-11-01T17:55:58.7031968Z 
2024-11-01T17:56:00.8121944Z 
2024-11-01T17:56:00.8124827Z test_model_exports_to_core_aten 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_model_exports_to_core_aten_1.1_4fa52d9ffad64a17_.log
2024-11-01T17:56:00.8127919Z Running 1 items in this shard: test/test_model_exports_to_core_aten.py::TestQuantizePT2EModels::test_vit_aten_export
2024-11-01T17:56:00.8129314Z 
2024-11-01T17:56:02.5857591Z Running test_namedtensor 1/1 ... [2024-11-01 17:56:02.585209]
2024-11-01T17:56:02.5858546Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:02.5861673Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_namedtensor.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'] ... [2024-11-01 17:56:02.585711]
2024-11-01T17:56:02.9802009Z Running higher_order_ops/test_invoke_subgraph 1/1 ... [2024-11-01 17:56:02.979759]
2024-11-01T17:56:02.9803006Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:02.9805746Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_invoke_subgraph.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'] ... [2024-11-01 17:56:02.980185]
2024-11-01T17:56:05.0400453Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2024-11-01 17:56:05.039491]
2024-11-01T17:56:05.0401621Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:05.0404830Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.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'] ... [2024-11-01 17:56:05.039970]
2024-11-01T17:56:07.2534294Z 
2024-11-01T17:56:07.2536900Z higher_order_ops/test_invoke_subgraph 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_subgraph_1.1_4576481b33443eaf_.log
2024-11-01T17:56:07.2543973Z Running 10 items in this shard: test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraph::test_aot_function, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraph::test_differing_strides_for_grad_outs, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraph::test_multiple, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraph::test_simple, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_dedupe, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_aliasing, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_input_mutation, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_nonlocal_update, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_normalize_gm, test/higher_order_ops/test_invoke_subgraph.py::TestInvokeSubgraphCompile::test_simple
2024-11-01T17:56:07.2550005Z 
2024-11-01T17:56:11.3771368Z Running test_cuda_sanitizer 1/1 ... [2024-11-01 17:56:11.376607]
2024-11-01T17:56:11.3772422Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:11.3775657Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_sanitizer.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'] ... [2024-11-01 17:56:11.377023]
2024-11-01T17:56:15.3885168Z 
2024-11-01T17:56:15.3887509Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_2f4f4399b1285656_.log
2024-11-01T17:56:15.3888896Z Running 0 items in this shard:
2024-11-01T17:56:15.3889178Z 
2024-11-01T17:56:16.8375291Z 
2024-11-01T17:56:16.8378228Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_d91d3fd216cab141_.log
2024-11-01T17:56:16.8455457Z Running 88 items in this shard: test/test_namedtensor.py::TestNamedTensor::test_aaa_must_run_first_check_experimental_warning, test/test_namedtensor.py::TestNamedTensor::test_addcmul_addcdiv, test/test_namedtensor.py::TestNamedTensor::test_addmm, test/test_namedtensor.py::TestNamedTensor::test_addmv, test/test_namedtensor.py::TestNamedTensor::test_align_as, test/test_namedtensor.py::TestNamedTensor::test_align_tensors, test/test_namedtensor.py::TestNamedTensor::test_align_tensors_two_inputs, test/test_namedtensor.py::TestNamedTensor::test_align_to, test/test_namedtensor.py::TestNamedTensor::test_align_to_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_any_all, test/test_namedtensor.py::TestNamedTensor::test_as_strided, test/test_namedtensor.py::TestNamedTensor::test_as_strided_cuda, test/test_namedtensor.py::TestNamedTensor::test_autograd_ignores_names, test/test_namedtensor.py::TestNamedTensor::test_autograd_smoke, test/test_namedtensor.py::TestNamedTensor::test_autograd_warns_named_grad, test/test_namedtensor.py::TestNamedTensor::test_bernoulli, test/test_namedtensor.py::TestNamedTensor::test_big_tensor_repr_has_names, test/test_namedtensor.py::TestNamedTensor::test_binary_ops, test/test_namedtensor.py::TestNamedTensor::test_bitwise_not, test/test_namedtensor.py::TestNamedTensor::test_bmm, test/test_namedtensor.py::TestNamedTensor::test_cat, test/test_namedtensor.py::TestNamedTensor::test_cdist, test/test_namedtensor.py::TestNamedTensor::test_comparison_ops, test/test_namedtensor.py::TestNamedTensor::test_copy_transpose, test/test_namedtensor.py::TestNamedTensor::test_cummax_cummin, test/test_namedtensor.py::TestNamedTensor::test_detach, test/test_namedtensor.py::TestNamedTensor::test_diagonal, test/test_namedtensor.py::TestNamedTensor::test_dot, test/test_namedtensor.py::TestNamedTensor::test_equal, test/test_namedtensor.py::TestNamedTensor::test_expand, test/test_namedtensor.py::TestNamedTensor::test_factory_coverage, test/test_namedtensor.py::TestNamedTensor::test_factory_edge_cases, test/test_namedtensor.py::TestNamedTensor::test_flatten, test/test_namedtensor.py::TestNamedTensor::test_flatten_index_error, test/test_namedtensor.py::TestNamedTensor::test_flatten_nodims, test/test_namedtensor.py::TestNamedTensor::test_has_names, test/test_namedtensor.py::TestNamedTensor::test_index_fill, test/test_namedtensor.py::TestNamedTensor::test_info_smoke, test/test_namedtensor.py::TestNamedTensor::test_logcumsumexp, test/test_namedtensor.py::TestNamedTensor::test_logical_not, test/test_namedtensor.py::TestNamedTensor::test_logical_ops, test/test_namedtensor.py::TestNamedTensor::test_masked_fill, test/test_namedtensor.py::TestNamedTensor::test_masked_select, test/test_namedtensor.py::TestNamedTensor::test_matmul, test/test_namedtensor.py::TestNamedTensor::test_max_pooling, test/test_namedtensor.py::TestNamedTensor::test_max_pooling_without_names_does_not_warn, test/test_namedtensor.py::TestNamedTensor::test_mm, test/test_namedtensor.py::TestNamedTensor::test_mv, test/test_namedtensor.py::TestNamedTensor::test_no_jit_script_support, test/test_namedtensor.py::TestNamedTensor::test_no_jit_tracer_support, test/test_namedtensor.py::TestNamedTensor::test_no_multiprocessing_support, test/test_namedtensor.py::TestNamedTensor::test_no_pickle_support, test/test_namedtensor.py::TestNamedTensor::test_no_save_support, test/test_namedtensor.py::TestNamedTensor::test_noncontig_contiguous, test/test_namedtensor.py::TestNamedTensor::test_none_names_refcount, test/test_namedtensor.py::TestNamedTensor::test_nyi_dimname_overload_msg, test/test_namedtensor.py::TestNamedTensor::test_out_fn_semantics, test/test_namedtensor.py::TestNamedTensor::test_pow_special, test/test_namedtensor.py::TestNamedTensor::test_py3_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_reduction_fns, test/test_namedtensor.py::TestNamedTensor::test_refine_names, test/test_namedtensor.py::TestNamedTensor::test_rename, test/test_namedtensor.py::TestNamedTensor::test_rename_, test/test_namedtensor.py::TestNamedTensor::test_rename_globber, test/test_namedtensor.py::TestNamedTensor::test_rename_rename_map, test/test_namedtensor.py::TestNamedTensor::test_repr, test/test_namedtensor.py::TestNamedTensor::test_resize, test/test_namedtensor.py::TestNamedTensor::test_select, test/test_namedtensor.py::TestNamedTensor::test_select_cuda, test/test_namedtensor.py::TestNamedTensor::test_set_names_property, test/test_namedtensor.py::TestNamedTensor::test_size, test/test_namedtensor.py::TestNamedTensor::test_split_fns_propagates_names, test/test_namedtensor.py::TestNamedTensor::test_squeeze, test/test_namedtensor.py::TestNamedTensor::test_stride, test/test_namedtensor.py::TestNamedTensor::test_support_device_named_grad, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_lists, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_named_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_numpy, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_grad_is_unnamed, test/test_namedtensor.py::TestNamedTensor::test_transpose_variants, test/test_namedtensor.py::TestNamedTensor::test_trivial, test/test_namedtensor.py::TestNamedTensor::test_unary_propagate_names_fns, test/test_namedtensor.py::TestNamedTensor::test_unflatten, test/test_namedtensor.py::TestNamedTensor::test_unsupported_op_error_msg, test/test_namedtensor.py::TestNamedTensor::test_using_seen_interned_string_doesnt_bump_refcount, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_interned_string_bumps_refcount_permanently, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_uninterned_string_refcounts
2024-11-01T17:56:16.8530266Z 
2024-11-01T17:56:19.6711470Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2024-11-01 17:56:19.670578]
2024-11-01T17:56:19.6712967Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:19.6716761Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_backward_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'] ... [2024-11-01 17:56:19.671184]
2024-11-01T17:56:20.9852758Z Running test_fx_passes 1/1 ... [2024-11-01 17:56:20.984760]
2024-11-01T17:56:20.9853796Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:20.9857373Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_passes.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'] ... [2024-11-01 17:56:20.985246]
2024-11-01T17:56:23.5785446Z 
2024-11-01T17:56:23.5787885Z dynamo/test_backward_higher_order_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backward_higher_order_ops_1.1_c31efdf6ac707ba0_.log
2024-11-01T17:56:23.5789077Z 
2024-11-01T17:56:27.6756936Z Running dynamo/test_trace_rules 1/1 ... [2024-11-01 17:56:27.675181]
2024-11-01T17:56:27.6758014Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:27.6760936Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:56:27.675617]
2024-11-01T17:56:31.5895659Z 
2024-11-01T17:56:31.5898058Z 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_e5f7c82b129025a7_.log
2024-11-01T17:56:31.5899712Z 
2024-11-01T17:56:32.8203461Z 
2024-11-01T17:56:32.8205580Z test_fx_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_passes_1.1_8ca683ab8dcea69e_.log
2024-11-01T17:56:32.8235609Z Running 53 items in this shard: test/test_fx_passes.py::TestFXGraphPasses::test_fuser_pass_deep_model, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition0, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition1, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition10, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition11, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition2, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition3, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition4, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition5, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition6, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition7, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition8, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_partition9, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition0, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition1, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition2, test/test_fx_passes.py::TestFXGraphPasses::test_fuser_util_xfail_partition3, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn0_expected_partition0_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn10_expected_partition10_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn11_expected_partition11_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn12_expected_partition12_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn13_expected_partition13_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn14_expected_partition14_bookend_non_compute_pass_True, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn15_expected_partition15_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn16_expected_partition16_bookend_non_compute_pass_True, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn17_expected_partition17_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn18_expected_partition18_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn1_expected_partition1_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn2_expected_partition2_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn3_expected_partition3_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn4_expected_partition4_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn5_expected_partition5_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn6_expected_partition6_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn7_expected_partition7_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn8_expected_partition8_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_fn9_expected_partition9_bookend_non_compute_pass_False, test/test_fx_passes.py::TestFXGraphPasses::test_partitioner_independent_output_fn0_expected_partition0, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model0, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model1, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model10, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model11, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model12, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model13, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model14, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model15, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model2, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model3, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model4, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model5, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model6, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model7, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model8, test/test_fx_passes.py::TestFXMatcherUtils::test_subgraph_matcher_test_model9
2024-11-01T17:56:32.8262239Z 
2024-11-01T17:56:35.7530181Z Running distributions/test_constraints 1/1 ... [2024-11-01 17:56:35.752543]
2024-11-01T17:56:35.7531420Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:35.7534967Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'distributions/test_constraints.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'] ... [2024-11-01 17:56:35.752961]
2024-11-01T17:56:36.9624013Z Running test_fx_reinplace_pass 1/1 ... [2024-11-01 17:56:36.961872]
2024-11-01T17:56:36.9625129Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:36.9627119Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_fx_reinplace_pass.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'] ... [2024-11-01 17:56:36.962240]
2024-11-01T17:56:40.1749320Z 
2024-11-01T17:56:40.1753264Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_b63b931d18739096_.log
2024-11-01T17:56:40.1832104Z Running 136 items in this shard: test/distributions/test_constraints.py::test_constraint[False-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[False-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[False-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[False-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[False-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[False-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[False-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[False-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[False-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[False-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[False-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[False-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[False-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[False-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[False-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[False-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[False-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[False-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_constraint[True-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[True-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[True-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[True-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[True-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[True-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[True-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[True-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[True-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[True-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[True-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[True-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[True-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[True-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[True-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[True-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[True-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[True-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn24-args24]
2024-11-01T17:56:40.1898700Z 
2024-11-01T17:56:43.7394130Z 
2024-11-01T17:56:43.7396662Z test_fx_reinplace_pass 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_reinplace_pass_1.1_a172072e0db705c6_.log
2024-11-01T17:56:43.7410594Z Running 12 items in this shard: test/test_fx_reinplace_pass.py::TestReinplacePass::test_out_node_updated, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_basic, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_different_metadata, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_index_mutation, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_overlapping_memory, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_op, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice_with_different_view_op_invalid, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice_with_different_view_op_invalid2, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_scatter_twice_with_different_view_op_valid, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_sym_input, test/test_fx_reinplace_pass.py::TestReinplacePass::test_reinplace_with_view
2024-11-01T17:56:43.7422138Z 
2024-11-01T17:56:44.3132178Z Running higher_order_ops/test_with_effects 1/1 ... [2024-11-01 17:56:44.312674]
2024-11-01T17:56:44.3133377Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:44.3138120Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'higher_order_ops/test_with_effects.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'] ... [2024-11-01 17:56:44.313158]
2024-11-01T17:56:47.9023278Z Running torch_np/numpy_tests/lib/test_type_check 1/1 ... [2024-11-01 17:56:47.901763]
2024-11-01T17:56:47.9024503Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:56:47.9027794Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_type_check.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'] ... [2024-11-01 17:56:47.902131]
2024-11-01T17:56:56.0816063Z 
2024-11-01T17:56:56.0818523Z torch_np/numpy_tests/lib/test_type_check 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_f4023aa3157d0967_.log
2024-11-01T17:56:56.0853707Z Running 50 items in this shard: test/torch_np/numpy_tests/lib/test_type_check.py::TestCommonType::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_1, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_2, test/torch_np/numpy_tests/lib/test_type_check.py::TestMintypecode::test_default_3, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsscalar::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestReal::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_cmplx, test/torch_np/numpy_tests/lib/test_type_check.py::TestImag::test_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplex::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_fail, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_isreal_real, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsreal::test_pass, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_list, test/torch_np/numpy_tests/lib/test_type_check.py::TestIscomplexobj::test_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsrealobj::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsnan::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_complex1, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsfinite::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_goodvalues, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_ind, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_neginf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsinf::test_posinf_scalar, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsposinf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestIsneginf::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_array, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_bad2, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_complex_good, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_do_not_rewrite_previous_keyword, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_float, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_generic, test/torch_np/numpy_tests/lib/test_type_check.py::TestNanToNum::test_integer, test/torch_np/numpy_tests/lib/test_type_check.py::TestRealIfClose::test_basic, test/torch_np/numpy_tests/lib/test_type_check.py::TestArrayConversion::test_asfarray
2024-11-01T17:56:56.0886996Z 
2024-11-01T17:57:00.4176235Z Running torch_np/numpy_tests/lib/test_histograms 1/1 ... [2024-11-01 17:57:00.417067]
2024-11-01T17:57:00.4177650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:00.4181891Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_histograms.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'] ... [2024-11-01 17:57:00.417512]
2024-11-01T17:57:03.1615357Z 
2024-11-01T17:57:03.1618126Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_6fa5c12d23a1cb2a_.log
2024-11-01T17:57:03.1899430Z Running 275 items in this shard: test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_copies, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_negative_resize, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_repeats, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_reshape_from_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_zeroresize, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_choose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_clip, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_compress, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_count_nonzero, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_cumproduct, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_diagonal, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_accuracy, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype3, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype4, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype5, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype6, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype7, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-10, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-9, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_mean, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_prod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ptp, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ravel, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_repeat, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_reshape, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_py_consistency, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_searchsorted, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_size, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_squeeze, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_std, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_sum, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_swapaxes, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_take, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_trace, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_transpose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_var, test/torch_np/numpy_tests/core/test_numeric.py::TestIsscalar::test_isscalar, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_logical, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_all_any, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_and_or_xor, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_not_abs, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_double, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_default, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_divide_err, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_errobj, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_set, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_G, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_g, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_warnings, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_values, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_promote_types_endian, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_result_type, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_tesult_type_2, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_5, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_9, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_empty_result, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_failed_itemsetting, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_lengths, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_too_few_items, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_types, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_values, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_?, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_B, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_b, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_h, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_i, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_l, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_list, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_axis_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_keepdims, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_twodim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean_edgecase, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_large_neg_int64, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_neg_width_boundaries, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_sufficient_width, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base3, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base_range, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equiv, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_none_compares_elementwise, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_array_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_func_takes_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_array, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_non_contig, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_property, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_scalar_nan_propagation_arr0_amin0_amax0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin2_amax2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax1, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax_0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_outint32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_memory_overlap, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_transposed, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_noncontig_inplace, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_D, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_F, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_e, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_?, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_B, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_b, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_d, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_f, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_h, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_i, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_l, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting_unsafe, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_inout, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_nonnative, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_03, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_04, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_05, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_06, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_07, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_08, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_09, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_10, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_11, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_12, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_equalnan, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_not_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_min_int, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_all_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_none_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_non_finite_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_scalar_return, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof1, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof2, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_out_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_for_reference_leak, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_full, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_ones, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_zeros, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_empty_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_filled_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_ones_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_zeros_like, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_zero_size, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_numpy_doc_examples, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_object, test/torch_np/numpy_tests/core/test_numeric.py::TestDtypePositional::test_dtype_positional, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_2D, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_list, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_0, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_1, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_2, test/torch_np/numpy_tests/core/test_numeric.py::TestStringFunction::test_set_string_function, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll1d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll2d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_exceptions, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_results, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_errors, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_multiples, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_new_position, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_to_end, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_preserve_order, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x2, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_3x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting_shapes, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_uint8_int32_mixed_dtypes, test/torch_np/numpy_tests/core/test_numeric.py::TestOuterMisc::test_outer_out_param, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_scalar_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_single_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_C_and_F_simul, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_non_array_input, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_require_each, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_unknown_requirement, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_error_kwargs, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_in_args, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_single_arg, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_number_of_arguments, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_shape_mismatch_error_message, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension_einsum, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimensional
2024-11-01T17:57:03.2148599Z 
2024-11-01T17:57:07.3754329Z Running dynamo/test_recompile_ux 1/1 ... [2024-11-01 17:57:07.374850]
2024-11-01T17:57:07.3755818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:07.3759745Z Executing ['/opt/conda/envs/py_3.12/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'] ... [2024-11-01 17:57:07.375308]
2024-11-01T17:57:11.2523468Z 
2024-11-01T17:57:11.2525147Z 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_6d1f47e769567577_.log
2024-11-01T17:57:11.2526211Z 
2024-11-01T17:57:12.0031192Z 
2024-11-01T17:57:12.0034074Z torch_np/numpy_tests/lib/test_histograms 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_96e3d36f962f6400_.log
2024-11-01T17:57:12.0067660Z Running 60 items in this shard: test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_arr_weights_mismatch, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_big_arrays, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_array_dims, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bin_edge_cases, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_bool_conversion, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_density, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_error_binnum_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_exotic_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_f32_rounding, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_histogram_bin_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_invalid_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_last_bin_inclusive_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_no_side_effects, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_object_array_of_0d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_one_bin, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_outliers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_precision, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_signed_overflow_bounds_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_some_nan_values, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_type, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_unsigned_monotonicity_check, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogram::test_weights, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_incorrect_methods, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_limited_variance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_novariance, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_outlier, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_scott_vs_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_auto, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_doane, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_fd, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_rice, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_scott, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_stone, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_signed_integer_data_bins_sturges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_simple_weighted, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramOptimBinNums::test_small, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_array, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_error_2, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_bins_errors, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_1d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_density_non_uniform_2d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_edge_dtype, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_empty, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_equal_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_finite_range, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_identical_samples, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_inf_edges, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_large_integers, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_rightmost_binedge, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_3d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_shape_4d, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_simple, test/torch_np/numpy_tests/lib/test_histograms.py::TestHistogramdd::test_weights
2024-11-01T17:57:12.0098322Z 
2024-11-01T17:57:15.3998315Z Running torch_np/numpy_tests/core/test_indexing 1/1 ... [2024-11-01 17:57:15.399263]
2024-11-01T17:57:15.3999707Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:15.4003586Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_indexing.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'] ... [2024-11-01 17:57:15.399781]
2024-11-01T17:57:15.5852442Z 
2024-11-01T17:57:15.5854952Z higher_order_ops/test_with_effects 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_with_effects_1.1_40501d4a6541ccfa_.log
2024-11-01T17:57:15.5867261Z Running 18 items in this shard: test/higher_order_ops/test_with_effects.py::TestWithEffects::test_alias_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_aot_eager_requires_grad, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_compile_inductor_external_op_return_none, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effectful_custom_op_with_subclasses, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effectful_op_in_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_aliased_outputs, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_is_output, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_mutation_return, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_effects_and_input_output_view_simple, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_buffer_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_print_with_input_mutations, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_register_effectful_custom_op, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_regular_effectful_op_in_forward_and_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_regular_effectful_op_only_in_backward, test/higher_order_ops/test_with_effects.py::TestWithEffects::test_torchbind_custom_op
2024-11-01T17:57:15.5878124Z 
2024-11-01T17:57:16.2032629Z Running torch_np/numpy_tests/lib/test_function_base 1/1 ... [2024-11-01 17:57:16.202718]
2024-11-01T17:57:16.2033692Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:16.2036274Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_function_base.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'] ... [2024-11-01 17:57:16.203166]
2024-11-01T17:57:19.7214893Z Running test_legacy_vmap 1/1 ... [2024-11-01 17:57:19.721041]
2024-11-01T17:57:19.7215740Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:19.7218612Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_legacy_vmap.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'] ... [2024-11-01 17:57:19.721459]
2024-11-01T17:57:23.8003396Z 
2024-11-01T17:57:23.8006159Z torch_np/numpy_tests/core/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_e413adc322cda8b0_.log
2024-11-01T17:57:23.8078569Z Running 67 items in this shard: test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_assignment_value_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_list, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_onedim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_indexing_twodim, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_boolean_shape_mismatch, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broaderrors_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_broken_sequence_not_nd_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_ellipsis_index_2, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_fancy_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_empty_tuple_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_everything_returns_views, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_array_to_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_index_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_negative_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_indexing_array_weird_strides, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_memory_order, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_none_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_nontuple_ndindex, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reverse_strides_and_subspace_bufferinit, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_reversed_strides_result_allocation, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_same_kind_index_casting, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_scalar_array_bool, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_bool_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_single_int_index, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_slicing_no_floats, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_small_regressions, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index2_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_False_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_32_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_1, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_advanced_indices_index_True_num_40_original_ndim_32, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_too_many_fancy_indices_special_case, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_not_possible, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_trivial_fancy_out_of_bounds, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_tuple_subclass, test/torch_np/numpy_tests/core/test_indexing.py::TestIndexing::test_uncontiguous_subspace_assignment, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index0, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index1, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_error_reports_correct_shape_index2, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_broadcast_subspace, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_index_is_larger, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepend_not_one, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_prepending_ones, test/torch_np/numpy_tests/core/test_indexing.py::TestBroadcastedAssignments::test_simple_broadcasting_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFancyIndexingCast::test_boolean_index_cast_assign, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_1d, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_boolean, test/torch_np/numpy_tests/core/test_indexing.py::TestMultiIndexingAutomated::test_multidim, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_non_integer_sequence_multiplication, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_reduce_axis_float_index, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_indexing, test/torch_np/numpy_tests/core/test_indexing.py::TestFloatNonIntegerArgument::test_valid_slicing, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_bool_as_int_argument_errors, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_fast_path, test/torch_np/numpy_tests/core/test_indexing.py::TestBooleanIndexing::test_boolean_indexing_weirdness, test/torch_np/numpy_tests/core/test_indexing.py::TestArrayToIndexDeprecation::test_array_to_index_error, test/torch_np/numpy_tests/core/test_indexing.py::TestNonIntegerArrayLike::test_basic, test/torch_np/numpy_tests/core/test_indexing.py::TestMultipleEllipsisError::test_basic
2024-11-01T17:57:23.8148053Z 
2024-11-01T17:57:28.1785008Z Running dynamo/test_hooks 1/1 ... [2024-11-01 17:57:28.177984]
2024-11-01T17:57:28.1786065Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:28.1788037Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'dynamo/test_hooks.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'] ... [2024-11-01 17:57:28.178364]
2024-11-01T17:57:32.1065439Z 
2024-11-01T17:57:32.1068075Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_42e9f6bfa4794b73_.log
2024-11-01T17:57:32.1069648Z 
2024-11-01T17:57:36.1715322Z Running torch_np/numpy_tests/core/test_numerictypes 1/1 ... [2024-11-01 17:57:36.171019]
2024-11-01T17:57:36.1716623Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:36.1719575Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numerictypes.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'] ... [2024-11-01 17:57:36.171426]
2024-11-01T17:57:40.9438206Z 
2024-11-01T17:57:40.9441213Z torch_np/numpy_tests/core/test_numerictypes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_b21909574b20f26c_.log
2024-11-01T17:57:40.9462166Z Running 34 items in this shard: test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_loses2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestCommonType::test_scalar_wins3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_both_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_nondtype_nonscalartype, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_same, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_sibling_class, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass, test/torch_np/numpy_tests/core/test_numerictypes.py::TestIsSubDType::test_subclass_backwards, test/torch_np/numpy_tests/core/test_numerictypes.py::TestBitName::test_abstract, test/torch_np/numpy_tests/core/test_numerictypes.py::TestDocStrings::test_platform_dependent_aliases, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_undersood_by_dtype_t9, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_are_unique, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t0, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t1, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t2, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t3, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t4, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t5, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t6, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t7, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t8, test/torch_np/numpy_tests/core/test_numerictypes.py::TestScalarTypeNames::test_names_reflect_attributes_t9
2024-11-01T17:57:40.9481893Z 
2024-11-01T17:57:45.0981350Z Running torch_np/numpy_tests/lib/test_arraysetops 1/1 ... [2024-11-01 17:57:45.097509]
2024-11-01T17:57:45.0982803Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:45.0985267Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraysetops.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'] ... [2024-11-01 17:57:45.097918]
2024-11-01T17:57:54.5803302Z 
2024-11-01T17:57:54.5805987Z torch_np/numpy_tests/lib/test_arraysetops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_ac9791ae89f6e264_.log
2024-11-01T17:57:54.5891659Z Running 62 items in this shard: test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary0_prepend0_append_nan_expected_to_end, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary1_prepend1_append1_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_forbidden_type_casts_ary2_prepend_nan_append_nan_expected_to_begin, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary0_prepend_65536_append_65540_expected0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary1_prepend1_append1_expected1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary2_prepend_0_append_0_expected2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_ediff1d_scalar_handling_ary3_prepend_3_append_-9_expected3, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_are_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_both_arrays_have_structured_dtype, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_first_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_hit_alternate_algorithm, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_invert_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_boolean_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype10_dtype20_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_mixed_dtype_dtype11_dtype21_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_ravel_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_second_array_is_object, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_table_timedelta_fails, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_timedelta_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_in1d_with_arrays_containing_tuples, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_array_like, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_intersect1d_indices, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_sort, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_isin_kind_table, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_manyways, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_char_array, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setdiff1d_unique, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_setxor1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestSetOps::test_union1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_2, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_-1, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_1d_with_axis_axis_0, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_errors, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_list, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_axis_zeros, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_nanequals, test/torch_np/numpy_tests/lib/test_arraysetops.py::TestUnique::test_unique_sort_order_with_axis
2024-11-01T17:57:54.5975436Z 
2024-11-01T17:57:58.7818014Z Running test_cuda_multigpu 1/1 ... [2024-11-01 17:57:58.781302]
2024-11-01T17:57:58.7819036Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:57:58.7822270Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_cuda_multigpu.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'] ... [2024-11-01 17:57:58.781770]
2024-11-01T17:58:02.8018342Z 
2024-11-01T17:58:02.8020645Z test_cuda_multigpu 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_multigpu_1.1_6df3e937220d3052_.log
2024-11-01T17:58:02.8022586Z Running 0 items in this shard:
2024-11-01T17:58:02.8023066Z 
2024-11-01T17:58:06.8951347Z Running profiler/test_profiler_tree 1/1 ... [2024-11-01 17:58:06.894580]
2024-11-01T17:58:06.8952386Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:58:06.8955938Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_profiler_tree.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'] ... [2024-11-01 17:58:06.895064]
2024-11-01T17:58:11.1389511Z 
2024-11-01T17:58:11.1392626Z torch_np/numpy_tests/lib/test_function_base 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_94abc71b62af76cd_.log
2024-11-01T17:58:11.1997715Z Running 530 items in this shard: test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestRot90::test_rotation_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_3d_swap_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_4d, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_basic_lr, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_basic_ud, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_default_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestFlip::test_multiple_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestAny::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAny::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestAll::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAll::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestCopy::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCopy::test_order, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_average_class_without_dtype, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic_keepdims_x0_axis0_expected_avg0_weights0_expected_wavg0_expected_wsum0, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_basic_keepdims_x1_axis_0_expected_avg1_weights1_expected_wavg1_expected_wsum1, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_returned, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_upcasting, test/torch_np/numpy_tests/lib/test_function_base.py::TestAverage::test_weights, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_broadcasting, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_deprecated_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_many_arguments, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_non_bool_deprecation, test/torch_np/numpy_tests/lib/test_function_base.py::TestSelect::test_return_dtype, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_0d, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_array_copied, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_floats, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_out_of_bounds_idx_-4, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_index_out_of_bounds_idx_4, test/torch_np/numpy_tests/lib/test_function_base.py::TestInsert::test_multidim, test/torch_np/numpy_tests/lib/test_function_base.py::TestAmax::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestAmin::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestPtp::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCumsum::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestProd::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCumprod::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_append, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_n, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_nd, test/torch_np/numpy_tests/lib/test_function_base.py::TestDiff::test_prepend, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_0d, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_array_order_preserve, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_fancy, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_index_floats, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_[1], test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_array([1]), test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_single_item_array_non_int, test/torch_np/numpy_tests/lib/test_function_base.py::TestDelete::test_slices, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_args, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_badargs, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_decreasing_unsigned_int_f_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype1, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype2, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_f_signed_int_big_jump_f_dtype3, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_inexact_dtypes, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_second_order_accurate, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_spacing, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_specific_axes, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_values, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_decreasing_unsigned_x_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_signed_int_big_jump_x_dtype0, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_signed_int_big_jump_x_dtype1, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_signed_int_big_jump_x_dtype2, test/torch_np/numpy_tests/lib/test_function_base.py::TestGradient::test_x_signed_int_big_jump_x_dtype3, test/torch_np/numpy_tests/lib/test_function_base.py::TestAngle::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_all_zero, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_leading_skip, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_list_to_list, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_no_trim, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_overflow_arr0, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_size_zero, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrimZeros::test_trailing_skip, test/torch_np/numpy_tests/lib/test_function_base.py::TestExtins::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestExtins::test_both, test/torch_np/numpy_tests/lib/test_function_base.py::TestExtins::test_place, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_casting_error, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_forward, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_large_integers_decreasing, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_large_integers_increasing, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_monotonic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_random, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_reverse, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_right_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_right_open, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_right_open_random, test/torch_np/numpy_tests/lib/test_function_base.py::TestDigitize::test_right_open_reverse, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_B_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_B_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_B_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_b_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_b_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_b_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_d_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_d_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_d_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_e_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_e_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_e_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_f_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_f_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_f_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_h_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_h_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_h_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_i_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_i_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_i_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_l_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_l_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_bartlett_dtype_l_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_B_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_B_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_B_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_b_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_b_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_b_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_d_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_d_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_d_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_e_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_e_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_e_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_f_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_f_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_f_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_h_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_h_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_h_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_i_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_i_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_i_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_l_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_l_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_blackman_dtype_l_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_B_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_B_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_B_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_b_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_b_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_b_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_d_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_d_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_d_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_e_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_e_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_e_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_f_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_f_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_f_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_h_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_h_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_h_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_i_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_i_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_i_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_l_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_l_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hamming_dtype_l_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_B_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_B_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_B_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_b_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_b_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_b_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_d_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_d_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_d_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_e_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_e_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_e_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_f_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_f_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_f_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_h_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_h_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_h_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_i_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_i_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_i_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_l_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_l_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_hanning_dtype_l_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_B_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_B_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_B_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_b_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_b_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_b_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_d_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_d_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_d_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_e_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_e_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_e_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_f_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_f_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_f_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_h_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_h_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_h_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_i_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_i_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_i_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_l_M_0, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_l_M_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestFilterwindows::test_kaiser_dtype_l_M_10, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrapz::test_ndim, test/torch_np/numpy_tests/lib/test_function_base.py::TestTrapz::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestSinc::test_array_like, test/torch_np/numpy_tests/lib/test_function_base.py::TestSinc::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestUnique::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestUnique::test_simple_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestCheckFinite::test_dtype_order, test/torch_np/numpy_tests/lib/test_function_base.py::TestCheckFinite::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_bias, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_corrcoef_dtype_test_type0, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_corrcoef_dtype_test_type1, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_corrcoef_dtype_test_type2, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_ddof, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_extreme, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_non_array, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestCorrCoef::test_xy, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_1D_rowvar, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_1D_variance, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_aweights, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_cov_dtype_test_type0, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_cov_dtype_test_type1, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_cov_dtype_test_type2, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_fweights, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_unit_fweights_and_aweights, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_wrong_ddof, test/torch_np/numpy_tests/lib/test_function_base.py::TestCov::test_xy, test/torch_np/numpy_tests/lib/test_function_base.py::Test_I0::test_complex, test/torch_np/numpy_tests/lib/test_function_base.py::Test_I0::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestKaiser::test_int_beta, test/torch_np/numpy_tests/lib/test_function_base.py::TestKaiser::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestMsort::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_indexing, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_invalid_arguments, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_nd_indexing, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_nd_shape, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_nd_values, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_no_input, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_return_type, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_single_input, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_sparse, test/torch_np/numpy_tests/lib/test_function_base.py::TestMeshgrid::test_writeback, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_0d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_0d_0d_condition, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_0d_comparison, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_default, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_multidimensional_extrafunc, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_scalar_domains_three_conditions, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestPiecewise::test_two_conditions, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_dtype_reference_leaks, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_empty_with_minlength, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_error_not_1d_vals0, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_error_not_1d_vals_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_simple, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_simple2, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_simple_weight, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_simple_weight2, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_with_incorrect_minlength, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_with_minlength, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_with_minlength_and_weights, test/torch_np/numpy_tests/lib/test_function_base.py::TestBincount::test_with_minlength_smaller_than_maxvalue, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_complex_interp, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_exceptions, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_if_len_x_is_small, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_any_nan_complex-both, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_any_nan_complex-imag, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_any_nan_complex-real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_any_nan_real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_behavior_exact_x, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_f_complex-both, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_f_complex-imag, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_f_complex-real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_f_real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_x_complex-both, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_x_complex-imag, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_x_complex-real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_x_real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_xf_complex-both, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_xf_complex-imag, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_xf_complex-real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_half_inf_xf_real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_inf_complex-both, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_inf_complex-imag, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_inf_complex-real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_non_finite_inf_real, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_period, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_right_left_behavior, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_scalar_interpolation_point, test/torch_np/numpy_tests/lib/test_function_base.py::TestInterp::test_zero_dimensional_interpolation_point, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_2D, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_api, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_exception, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_extended_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_extended_axis_invalid, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_fraction, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q1_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q1_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q1_axis3, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q1_axis4, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q1_axis_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q_7_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q_7_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q_7_axis3, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q_7_axis4, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_keepdims_out_q_7_axis_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_extrapolation, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype5_expected_dtype5_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype6_expected_dtype6_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype7_expected_dtype7_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_B_expected_dtype0_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_b_expected_dtype1_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_h_expected_dtype2_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_i_expected_dtype3_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_averaged_inverted_cdf_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_closest_observation_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_hazen_expected_27_5, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_interpolated_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_inverted_cdf_expected_20, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_linear_expected_29, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_median_unbiased_expected_27, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_normal_unbiased_expected_27_125, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_interpolation_input_dtype_l_expected_dtype4_method_weibull_expected_26, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_linear_nan_1D_dtype_g, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_H, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_I, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_L, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_P, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_Q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_g, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_p, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_lower_higher_dtype_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_H, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_I, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_L, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_P, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_Q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_g, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_p, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_midpoint_dtype_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nan_behavior, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nan_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_H, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_I, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_L, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_P, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_Q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_d, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_e, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_f, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_g, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_p, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_nearest_dtype_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_no_p_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_out, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_out_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_empty_dim, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_list, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_no_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_out, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_percentile_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_scalar_q, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_scalar_q_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestPercentile::test_sequence, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_correct_quantile_value, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_fraction, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_max_ulp, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_no_p_overwrite, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_hypo, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_averaged_inverted_cdf, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_closest_observation, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_hazen, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_higher, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_interpolated_inverted_cdf, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_inverted_cdf, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_linear, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_lower, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_median_unbiased, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_midpoint, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_nearest, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_normal_unbiased, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_monotonic_method_weibull, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_B, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_b, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_h, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_i, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_preserve_int_type_dtype_l, test/torch_np/numpy_tests/lib/test_function_base.py::TestQuantile::test_quantile_scalar_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_array_like, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_axis_keyword, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_basic, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_basic_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_empty, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_extended_axis, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_extended_axis_invalid, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis0, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis3, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis4, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_keepdims_out_axis_1, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior_2, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_nan_behavior_3, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_out, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_out_nan, test/torch_np/numpy_tests/lib/test_function_base.py::TestMedian::test_overwrite_keyword, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_complex, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_B_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_H_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_b_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_g_type_out_G, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_h_type_out_F, test/torch_np/numpy_tests/lib/test_function_base.py::TestSortComplex::test_sort_real_type_in_l_type_out_D
2024-11-01T17:58:11.2496294Z 
2024-11-01T17:58:14.8235216Z 
2024-11-01T17:58:14.8237812Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_b44812720d5a6940_.log
2024-11-01T17:58:14.8244766Z Running 10 items in this shard: test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_detailed, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_with_stream, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory_and_stack, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_record_function, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_modules, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_dispatch, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_function
2024-11-01T17:58:14.8250985Z 
2024-11-01T17:58:15.3366839Z Running torch_np/numpy_tests/fft/test_helper 1/1 ... [2024-11-01 17:58:15.336188]
2024-11-01T17:58:15.3368089Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:58:15.3370988Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/fft/test_helper.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'] ... [2024-11-01 17:58:15.336588]
2024-11-01T17:58:19.0107711Z Running torch_np/test_scalars_0D_arrays 1/1 ... [2024-11-01 17:58:19.010231]
2024-11-01T17:58:19.0108909Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:58:19.0111502Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/test_scalars_0D_arrays.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'] ... [2024-11-01 17:58:19.010641]
2024-11-01T17:58:20.4750452Z 
2024-11-01T17:58:20.4753176Z torch_np/numpy_tests/fft/test_helper 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_826ad725a2705f38_.log
2024-11-01T17:58:20.4761956Z Running 8 items in this shard: test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_axes_keyword, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_equal_to_original, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_inverse, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTShift::test_uneven_dims, test/torch_np/numpy_tests/fft/test_helper.py::TestFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestRFFTFreq::test_definition, test/torch_np/numpy_tests/fft/test_helper.py::TestIRFFTN::test_not_last_axis_success
2024-11-01T17:58:20.4768864Z 
2024-11-01T17:58:24.4340237Z 
2024-11-01T17:58:24.4343007Z torch_np/test_scalars_0D_arrays 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_scalars_0D_arrays_1.1_31e09c3406c61153_.log
2024-11-01T17:58:24.4378304Z Running 33 items in this shard: test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_array, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_asarray, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_asarray_int, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_array_scalar_basic_int64, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_array, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_asarray, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_asarray_int, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_conversion_to_int_int64, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_array, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_asarray, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_asarray_int, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_decay_to_py_scalar_int64, test/torch_np/test_scalars_0D_arrays.py::TestArrayScalars::test_scalar_comparisons, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value0, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value1, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value10, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value11, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value4, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value5, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value6, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value7, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value8, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value9, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value_s, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_not_scalar_value_string, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_array_0D, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_array_1D, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_array_2D, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_float32, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_int, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_list, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_list-list, test/torch_np/test_scalars_0D_arrays.py::TestIsScalar::test_is_scalar_literal
2024-11-01T17:58:24.4406017Z 
2024-11-01T17:58:24.6458676Z Running profiler/test_memory_profiler 1/1 ... [2024-11-01 17:58:24.645360]
2024-11-01T17:58:24.6459814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:58:24.6462686Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'profiler/test_memory_profiler.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'] ... [2024-11-01 17:58:24.645776]
2024-11-01T17:58:28.6111987Z Running torch_np/numpy_tests/core/test_scalar_ctors 1/1 ... [2024-11-01 17:58:28.610677]
2024-11-01T17:58:28.6113506Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:58:28.6117974Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalar_ctors.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'] ... [2024-11-01 17:58:28.611145]
2024-11-01T17:58:28.7674135Z 
2024-11-01T17:58:28.7676630Z profiler/test_memory_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_memory_profiler_1.1_327929cae280b01b_.log
2024-11-01T17:58:28.7695169Z Running 33 items in this shard: test/profiler/test_memory_profiler.py::TestMemoryProfiler::test_config_check, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_module_and_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_from_optimizer_set_to_none, test/profiler/test_memory_profiler.py::TestIdentifyGradients::test_extract_gradients_low_level, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_complicated, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_non_op_allocations, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_simple_inplace, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_stacked, test/profiler/test_memory_profiler.py::TestDataFlow::test_data_flow_graph_with_annotations, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_backward, test/profiler/test_memory_profiler.py::TestDataFlow::test_match_schemas_tensorlist, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_sequential_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_categories_e2e_simple_module_fwd_bwd_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_bwd, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_inputs_fwd_lazy, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_lazily_initialized, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_manual_optimizer_step, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_memory_timeline, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_memory_timeline_no_id, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients, test/profiler/test_memory_profiler.py::TestMemoryProfilerE2E::test_parameters_and_gradients_set_to_none
2024-11-01T17:58:28.7712826Z 
2024-11-01T17:58:32.8977129Z Running torch_np/numpy_tests/lib/test_arraypad 1/1 ... [2024-11-01 17:58:32.897109]
2024-11-01T17:58:32.8978140Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:58:32.8980590Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'torch_np/numpy_tests/lib/test_arraypad.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'] ... [2024-11-01 17:58:32.897590]
2024-11-01T17:58:35.2378415Z 
2024-11-01T17:58:35.2381397Z torch_np/numpy_tests/core/test_scalar_ctors 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_2d537e1848021890_.log
2024-11-01T17:58:35.2447283Z Running 65 items in this shard: test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_bool, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_floating, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromString::test_floating_overflow, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromInt::test_intp, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestFromInt::test_uint64_from_negative, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t10_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_complex_t11_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_byte_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_int__t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_intc_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_longlong_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_np_short_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_byte, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_int_, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_intc, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_longlong, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_np_short, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_t25, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_integers_t15_t26, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t10_t23, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t11_t23, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t20, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t21, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t22, test/torch_np/numpy_tests/core/test_scalar_ctors.py::TestArrayFromScalar::test_reals_t12_t23
2024-11-01T17:58:35.2511050Z 
2024-11-01T17:58:37.6715099Z 
2024-11-01T17:58:37.6717959Z torch_np/numpy_tests/lib/test_arraypad 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_e4a278c91457ac4f_.log
2024-11-01T17:58:37.6728660Z Running 9 items in this shard: test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float2, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_float3, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_odd_pad_amount, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_pad_2d, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_constant_zeros, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_check_large_integers, test/torch_np/numpy_tests/lib/test_arraypad.py::TestConstant::test_pad_empty_dimension
2024-11-01T17:58:37.6736821Z 
2024-11-01T17:58:39.3797869Z Running test_dataloader 1/1 ... [2024-11-01 17:58:39.379255]
2024-11-01T17:58:39.3798912Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T17:58:39.3802322Z Executing ['/opt/conda/envs/py_3.12/bin/python', '-bb', 'test_dataloader.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'] ... [2024-11-01 17:58:39.379729]
2024-11-01T18:00:07.3015584Z 
2024-11-01T18:00:07.3017854Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_5125bca44082409a_.log
2024-11-01T18:00:07.3131975Z Running 124 items in this shard: test/test_legacy_vmap.py::TestVmapAPILegacy::test_accepts_nested_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_backward_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_batched_gradient_basic, test/test_legacy_vmap.py::TestVmapAPILegacy::test_constant_function, test/test_legacy_vmap.py::TestVmapAPILegacy::test_different_map_dim_size_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_atan2, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_does_not_warn_by_default, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_masked_fill, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_multiple_returns, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_warns_when_warnings_are_enabled, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_with_undefined_grad, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_zero_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_func_with_no_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_functools_partial, test/test_legacy_vmap.py::TestVmapAPILegacy::test_grad_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dim_not_in_tensor_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dims_wrong_type_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_different_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_same_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_unary, test/test_legacy_vmap.py::TestVmapAPILegacy::test_integer_in_dim_but_not_tensor_input_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs_error_cases, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_non_default_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_different_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_same_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nn_module, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_default_in_dims_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_tensor_output_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_zero_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_none_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nonzero_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_noop_in_inner_vmap, test/test_legacy_vmap.py::TestVmapAPILegacy::test_not_enough_in_dims_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dim_out_of_bounds_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_and_num_outputs_mismatch_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_edge_case, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_must_be_int_or_tuple_of_int_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_single_input, test/test_legacy_vmap.py::TestVmapAPILegacy::test_unsupported_op_err_msg, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_T_numpy, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_as_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_binary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_bmm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_cat, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_chunk, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clamp, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clone, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_comparison_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_conj, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_diagonal, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_dot, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_expand_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_fill_and_zero_inplace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_imag, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_floating_point, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_movedim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mv, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_narrow, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_zeros, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_no_random_op_support, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_result_type, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_select, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_slice, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_squeeze, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stack, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stride, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_sum_dim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_t, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_tensor_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_to, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_trace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_transpose, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unbind, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unfold, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check_ok, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_add_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_binary_cross_entropy_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_diagonal_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_div_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_expand_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_index_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_manyview_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_on_view_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_lgamma_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log1p_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_logsumexp_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_max_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_median_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_min_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_mul_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_permute_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_reshape_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_select_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sigmoid_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_slice_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_stack_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sub_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_threshold_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_trace_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_multiple_grad_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check_ok
2024-11-01T18:00:07.3246067Z 
2024-11-01T18:06:17.9930054Z 
2024-11-01T18:06:17.9932250Z test_dataloader 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dataloader_1.1_612cf8a11c812d10_.log
2024-11-01T18:06:18.0140325Z Running 178 items in this shard: test/test_dataloader.py::TestDatasetRandomSplit::test_incomplete_fractional_splits, test/test_dataloader.py::TestDatasetRandomSplit::test_lengths_must_equal_dataset_size, test/test_dataloader.py::TestDatasetRandomSplit::test_slicing_of_subset_of_dataset, test/test_dataloader.py::TestDatasetRandomSplit::test_slicing_of_subset_of_subset, test/test_dataloader.py::TestDatasetRandomSplit::test_splits_are_mutually_exclusive, test/test_dataloader.py::TestDatasetRandomSplit::test_splits_generator, test/test_dataloader.py::TestDatasetRandomSplit::test_splits_have_correct_size, test/test_dataloader.py::TestDatasetRandomSplit::test_splits_indexing_type, test/test_dataloader.py::TestDatasetRandomSplit::test_splits_reproducibility, test/test_dataloader.py::TestTensorDataset::test_getitem, test/test_dataloader.py::TestTensorDataset::test_getitem_1d, test/test_dataloader.py::TestTensorDataset::test_len, test/test_dataloader.py::TestTensorDataset::test_many_tensors, test/test_dataloader.py::TestTensorDataset::test_single_tensor, test/test_dataloader.py::TestStackDataset::test_empty, test/test_dataloader.py::TestStackDataset::test_getitem, test/test_dataloader.py::TestStackDataset::test_getitems, test/test_dataloader.py::TestStackDataset::test_getitems_raises_index_error, test/test_dataloader.py::TestStackDataset::test_getitems_value_error, test/test_dataloader.py::TestStackDataset::test_len, test/test_dataloader.py::TestStackDataset::test_mixed, test/test_dataloader.py::TestStackDataset::test_single, test/test_dataloader.py::TestStackDataset::test_size_mismatch, test/test_dataloader.py::TestConcatDataset::test_add_dataset, test/test_dataloader.py::TestConcatDataset::test_concat_raises_index_error, test/test_dataloader.py::TestConcatDataset::test_concat_two_non_singletons, test/test_dataloader.py::TestConcatDataset::test_concat_two_non_singletons_with_empty, test/test_dataloader.py::TestConcatDataset::test_concat_two_singletons, test/test_dataloader.py::TestConcatDataset::test_iterable_dataset_err, test/test_dataloader.py::TestDataLoader::test_batch_sampler, test/test_dataloader.py::TestDataLoader::test_builtin_collection_conversion, test/test_dataloader.py::TestDataLoader::test_bulk_loading_nobatch, test/test_dataloader.py::TestDataLoader::test_chain_iterable_style_dataset, test/test_dataloader.py::TestDataLoader::test_default_collate_bad_numpy_types, test/test_dataloader.py::TestDataLoader::test_default_collate_bad_sequence_type, test/test_dataloader.py::TestDataLoader::test_default_collate_dtype, test/test_dataloader.py::TestDataLoader::test_default_collate_mapping_keep_type, test/test_dataloader.py::TestDataLoader::test_default_collate_numpy_memmap, test/test_dataloader.py::TestDataLoader::test_default_collate_sequence_dont_keep_type, test/test_dataloader.py::TestDataLoader::test_default_collate_sequence_keep_type, test/test_dataloader.py::TestDataLoader::test_default_collate_shared_tensor, test/test_dataloader.py::TestDataLoader::test_default_convert_mapping_keep_type, test/test_dataloader.py::TestDataLoader::test_default_convert_sequence_dont_keep_type, test/test_dataloader.py::TestDataLoader::test_default_convert_sequence_keep_type, test/test_dataloader.py::TestDataLoader::test_distributed_sampler_invalid_rank, test/test_dataloader.py::TestDataLoader::test_duplicating_data_with_drop_last, test/test_dataloader.py::TestDataLoader::test_error, test/test_dataloader.py::TestDataLoader::test_error_in_init, test/test_dataloader.py::TestDataLoader::test_error_workers, test/test_dataloader.py::TestDataLoader::test_excessive_thread_creation_warning, test/test_dataloader.py::TestDataLoader::test_fd_limit_exceeded, test/test_dataloader.py::TestDataLoader::test_get_worker_info, test/test_dataloader.py::TestDataLoader::test_growing_dataset, test/test_dataloader.py::TestDataLoader::test_invalid_assign_after_init, test/test_dataloader.py::TestDataLoader::test_invalid_ctor_args_combinations, test/test_dataloader.py::TestDataLoader::test_iterable_style_dataset, test/test_dataloader.py::TestDataLoader::test_iterabledataset_len, test/test_dataloader.py::TestDataLoader::test_large_sampler_indices, test/test_dataloader.py::TestDataLoader::test_len, test/test_dataloader.py::TestDataLoader::test_multi_epochs_reproducibility, test/test_dataloader.py::TestDataLoader::test_multiple_dataloaders, test/test_dataloader.py::TestDataLoader::test_multiprocessing_contexts, test/test_dataloader.py::TestDataLoader::test_multiprocessing_iterdatapipe, test/test_dataloader.py::TestDataLoader::test_multiprocessing_iterdatapipe_with_dill, test/test_dataloader.py::TestDataLoader::test_no_segfault, test/test_dataloader.py::TestDataLoader::test_numpy, test/test_dataloader.py::TestDataLoader::test_numpy_gen_state, test/test_dataloader.py::TestDataLoader::test_numpy_scalars, test/test_dataloader.py::TestDataLoader::test_partial_workers, test/test_dataloader.py::TestDataLoader::test_proper_exit, test/test_dataloader.py::TestDataLoader::test_random_sampler, test/test_dataloader.py::TestDataLoader::test_random_sampler_len_with_replacement, test/test_dataloader.py::TestDataLoader::test_random_sampler_len_without_replacement, test/test_dataloader.py::TestDataLoader::test_sampler, test/test_dataloader.py::TestDataLoader::test_sampler_reproducibility, test/test_dataloader.py::TestDataLoader::test_segfault, test/test_dataloader.py::TestDataLoader::test_seqential_batch_workers, test/test_dataloader.py::TestDataLoader::test_seqential_batch_workers_prefetch, test/test_dataloader.py::TestDataLoader::test_sequential_batch, test/test_dataloader.py::TestDataLoader::test_sequential_nonbatch, test/test_dataloader.py::TestDataLoader::test_sequential_pin_memory, test/test_dataloader.py::TestDataLoader::test_sequential_workers, test/test_dataloader.py::TestDataLoader::test_shuffle, test/test_dataloader.py::TestDataLoader::test_shuffle_batch, test/test_dataloader.py::TestDataLoader::test_shuffle_batch_none, test/test_dataloader.py::TestDataLoader::test_shuffle_batch_workers, test/test_dataloader.py::TestDataLoader::test_shuffle_batch_workers_prefetch, test/test_dataloader.py::TestDataLoader::test_shuffle_pin_memory, test/test_dataloader.py::TestDataLoader::test_shuffle_reproducibility, test/test_dataloader.py::TestDataLoader::test_shuffle_workers, test/test_dataloader.py::TestDataLoader::test_timeout, test/test_dataloader.py::TestDataLoader::test_typing, test/test_dataloader.py::TestDataLoader::test_worker_init_fn, test/test_dataloader.py::TestDataLoader::test_worker_seed, test/test_dataloader.py::TestDataLoader::test_worker_seed_reproducibility, test/test_dataloader.py::IntegrationTestDataLoaderDataPipe::test_shuffler_iterdatapipe, test/test_dataloader.py::TestStringDataLoader::test_shuffle_pin_memory, test/test_dataloader.py::TestDictDataLoader::test_pin_memory, test/test_dataloader.py::TestDictDataLoader::test_pin_memory_device, test/test_dataloader.py::TestDictDataLoader::test_pin_memory_with_only_device, test/test_dataloader.py::TestDictDataLoader::test_sequential_batch, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_batch_sampler, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_builtin_collection_conversion, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_bulk_loading_nobatch, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_chain_iterable_style_dataset, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_dataset_not_reset, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_bad_numpy_types, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_bad_sequence_type, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_dtype, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_mapping_keep_type, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_numpy_memmap, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_sequence_dont_keep_type, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_sequence_keep_type, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_collate_shared_tensor, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_convert_mapping_keep_type, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_convert_sequence_dont_keep_type, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_default_convert_sequence_keep_type, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_distributed_sampler_invalid_rank, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_duplicating_data_with_drop_last, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_early_exit, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_error, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_error_in_init, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_error_workers, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_excessive_thread_creation_warning, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_fd_limit_exceeded, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_get_worker_info, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_growing_dataset, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_invalid_assign_after_init, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_invalid_ctor_args_combinations, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_iterable_style_dataset, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_iterabledataset_len, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_large_sampler_indices, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_len, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_multi_epochs_reproducibility, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_multiple_dataloaders, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_multiprocessing_contexts, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_multiprocessing_iterdatapipe, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_multiprocessing_iterdatapipe_with_dill, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_no_segfault, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_numpy, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_numpy_gen_state, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_numpy_scalars, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_partial_workers, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_proper_exit, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_random_sampler, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_random_sampler_len_with_replacement, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_random_sampler_len_without_replacement, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_sampler, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_sampler_reproducibility, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_segfault, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_seqential_batch_workers, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_seqential_batch_workers_prefetch, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_sequential_batch, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_sequential_nonbatch, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_sequential_pin_memory, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_sequential_workers, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle_batch, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle_batch_none, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle_batch_workers, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle_batch_workers_prefetch, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle_pin_memory, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle_reproducibility, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_shuffle_workers, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_timeout, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_typing, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_worker_init_fn, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_worker_seed, test/test_dataloader.py::TestDataLoaderPersistentWorkers::test_worker_seed_reproducibility, test/test_dataloader.py::TestNamedTupleDataLoader::test_dataloader_with_namedtuple, test/test_dataloader.py::TestCustomPinFn::test_custom_batch_pin, test/test_dataloader.py::TestCustomPinFn::test_custom_batch_pin_worker, test/test_dataloader.py::TestIndividualWorkerQueue::test_ind_worker_queue, test/test_dataloader.py::TestSetAffinity::test_set_affinity_in_worker_init, test/test_dataloader.py::TestConvAfterFork::test_conv_after_fork, test/test_dataloader.py::TestDataLoaderDeviceTypeCPU::test_nested_tensor_multiprocessing_context_fork_cpu, test/test_dataloader.py::TestDataLoaderDeviceTypeCPU::test_nested_tensor_multiprocessing_context_forkserver_cpu, test/test_dataloader.py::TestDataLoaderDeviceTypeCPU::test_nested_tensor_multiprocessing_context_spawn_cpu
2024-11-01T18:06:18.0285667Z 
2024-11-01T18:06:18.9592483Z Running test batch 'tests to run' cost 5320.69 seconds
2024-11-01T18:06:19.9568595Z 
2024-11-01T18:06:19.9569618Z real	88m46.401s
2024-11-01T18:06:19.9570200Z user	109m43.745s
2024-11-01T18:06:19.9572856Z sys	10m11.793s
2024-11-01T18:06:19.9573378Z + assert_git_not_dirty
2024-11-01T18:06:19.9575730Z + [[ linux-focal-py3.12-clang10 != *rocm* ]]
2024-11-01T18:06:19.9576833Z + [[ linux-focal-py3.12-clang10 != *xla* ]]
2024-11-01T18:06:19.9579183Z ++ git status --porcelain
2024-11-01T18:06:19.9580145Z ++ grep -v '?? third_party'
2024-11-01T18:06:45.1026015Z ++ true
2024-11-01T18:06:45.1026993Z + git_status=
2024-11-01T18:06:45.1027726Z + [[ -n '' ]]
2024-11-01T18:06:45.1029697Z + [[ 1 == 1 ]]
2024-11-01T18:06:45.1030114Z + test_aten
2024-11-01T18:06:45.1030920Z + echo 'Running ATen tests with pytorch lib'
2024-11-01T18:06:45.1031673Z Running ATen tests with pytorch lib
2024-11-01T18:06:45.1032491Z + [[ -n '' ]]
2024-11-01T18:06:45.1032914Z + echo 'Running test with the build folder'
2024-11-01T18:06:45.1033401Z Running test with the build folder
2024-11-01T18:06:45.1034299Z + TEST_BASE_DIR=build/bin
2024-11-01T18:06:45.1035282Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libc10.so build/bin
2024-11-01T18:06:45.1060414Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libcaffe2*' build/bin
2024-11-01T18:06:45.1071390Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libmkldnn*' build/bin
2024-11-01T18:06:45.1081269Z + ln -sf '/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libnccl*' build/bin
2024-11-01T18:06:45.1094045Z + ln -sf /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/lib/libtorchbind_test.so build/bin
2024-11-01T18:06:45.1101776Z + ls build/bin
2024-11-01T18:06:45.1155227Z  BackoffTest			       cpu_profiling_allocator_test
2024-11-01T18:06:45.1156200Z  CMakeFiles			       cpu_rng_test
2024-11-01T18:06:45.1157055Z  CTestTestfile.cmake		       dispatch_key_set_test
2024-11-01T18:06:45.1157947Z  CppSignature_test		       dlconvertor_test
2024-11-01T18:06:45.1158464Z  Dict_test			       example_allreduce
2024-11-01T18:06:45.1159132Z  Dimname_test			       extension_backend_test
2024-11-01T18:06:45.1159682Z  FileStoreTest			       half_test
2024-11-01T18:06:45.1160178Z  HashStoreTest			       inline_container_test
2024-11-01T18:06:45.1160678Z  IListRef_test			       ivalue_test
2024-11-01T18:06:45.1161206Z  KernelFunction_test		       kernel_function_legacy_test
2024-11-01T18:06:45.1161773Z  List_test			       kernel_function_test
2024-11-01T18:06:45.1162272Z  Makefile			       kernel_lambda_legacy_test
2024-11-01T18:06:45.1162760Z  MaybeOwned_test		       kernel_lambda_test
2024-11-01T18:06:45.1163282Z  NamedTensor_test		       kernel_stackbased_test
2024-11-01T18:06:45.1163845Z  ProcessGroupGlooTest		       lazy_tensor_test
2024-11-01T18:06:45.1164411Z  StorageUtils_test		       legacy_vmap_test
2024-11-01T18:06:45.1164891Z  TCPStoreTest			       libc10.so
2024-11-01T18:06:45.1165531Z  aot_model_compiler_test	      'libcaffe2*'
2024-11-01T18:06:45.1166758Z  apply_utils_test		      'libmkldnn*'
2024-11-01T18:06:45.1167224Z  atest				      'libnccl*'
2024-11-01T18:06:45.1167702Z  backend_fallback_test		       libtorch.so
2024-11-01T18:06:45.1168172Z  basic				       libtorch_cpu.so
2024-11-01T18:06:45.1168843Z  broadcast_test			       libtorch_global_deps.so
2024-11-01T18:06:45.1169365Z  c10_Bitset_test		       libtorch_python.so
2024-11-01T18:06:45.1169964Z  c10_CompileTimeFunctionPointer_test   libtorchbind_test.so
2024-11-01T18:06:45.1170689Z  c10_ConstexprCrc_test		       make_boxed_from_unboxed_functor_test
2024-11-01T18:06:45.1171340Z  c10_DeadlockDetection_test	       math_kernel_test
2024-11-01T18:06:45.1171902Z  c10_DeviceGuard_test		       memory_format_test
2024-11-01T18:06:45.1172427Z  c10_Device_test		       memory_overlapping_test
2024-11-01T18:06:45.1172991Z  c10_DispatchKeySet_test	       mobile_memory_cleanup
2024-11-01T18:06:45.1173507Z  c10_Half_test			       native_test
2024-11-01T18:06:45.1174000Z  c10_InlineDeviceGuard_test	       op_allowlist_test
2024-11-01T18:06:45.1174603Z  c10_InlineStreamGuard_test	       op_registration_test
2024-11-01T18:06:45.1175262Z  c10_LeftRight_test		       operator_name_test
2024-11-01T18:06:45.1175875Z  c10_Metaprogramming_test	       operators_test
2024-11-01T18:06:45.1176521Z  c10_NetworkFlow_test		       packedtensoraccessor_test
2024-11-01T18:06:45.1177078Z  c10_Scalar_test		       parallel_benchmark
2024-11-01T18:06:45.1177560Z  c10_SizesAndStrides_test	       pow_test
2024-11-01T18:06:45.1178019Z  c10_StreamGuard_test		       protoc
2024-11-01T18:06:45.1178678Z  c10_SymInt_test		       protoc-3.13.0.0
2024-11-01T18:06:45.1179261Z  c10_Synchronized_test		       quantized_test
2024-11-01T18:06:45.1179898Z  c10_ThreadLocal_test		       reduce_ops_test
2024-11-01T18:06:45.1180439Z  c10_TypeIndex_test		       reportMemoryUsage_test
2024-11-01T18:06:45.1180980Z  c10_TypeList_test		       scalar_tensor_test
2024-11-01T18:06:45.1181467Z  c10_TypeTraits_test		       scalar_test
2024-11-01T18:06:45.1181959Z  c10_accumulate_test		       static_runtime_bench
2024-11-01T18:06:45.1182510Z  c10_bfloat16_test		       static_runtime_test
2024-11-01T18:06:45.1183034Z  c10_bit_cast_test		       stride_properties_test
2024-11-01T18:06:45.1183582Z  c10_complex_math_test		       tensor_iterator_test
2024-11-01T18:06:45.1184091Z  c10_complex_test		       test_api
2024-11-01T18:06:45.1184489Z  c10_cow_test			       test_cpp_rpc
2024-11-01T18:06:45.1184944Z  c10_exception_test		       test_dist_autograd
2024-11-01T18:06:45.1185533Z  c10_flags_test			       test_edge_op_registration
2024-11-01T18:06:45.1186031Z  c10_generic_math_test		       test_jit
2024-11-01T18:06:45.1186504Z  c10_intrusive_ptr_benchmark	       test_lazy
2024-11-01T18:06:45.1187005Z  c10_intrusive_ptr_test		       test_mobile_nnc
2024-11-01T18:06:45.1187610Z  c10_irange_test		       test_parallel
2024-11-01T18:06:45.1188259Z  c10_lazy_test			       test_tensorexpr
2024-11-01T18:06:45.1188948Z  c10_logging_test		       thread_init_test
2024-11-01T18:06:45.1189726Z  c10_optional_test		       torch_shm_manager
2024-11-01T18:06:45.1190383Z  c10_ordered_preserving_dict_test      tutorial_tensorexpr
2024-11-01T18:06:45.1190925Z  c10_registry_test		       type_ptr_test
2024-11-01T18:06:45.1191393Z  c10_small_vector_test		       type_test
2024-11-01T18:06:45.1191871Z  c10_ssize_test			       undefined_tensor_test
2024-11-01T18:06:45.1192415Z  c10_string_util_test		       vec_test_all_types_AVX2
2024-11-01T18:06:45.1192999Z  c10_string_view_test		       vec_test_all_types_AVX512
2024-11-01T18:06:45.1193575Z  c10_tempfile_test		       vec_test_all_types_DEFAULT
2024-11-01T18:06:45.1194283Z  c10_typeid_test		       verify_api_visibility
2024-11-01T18:06:45.1194785Z  cmake_install.cmake		       weakref_test
2024-11-01T18:06:45.1195263Z  cpu_allocator_test		       wrapdim_test
2024-11-01T18:06:45.1195743Z  cpu_generator_test		       xla_tensor_test
2024-11-01T18:06:45.1196211Z + aten/tools/run_tests.sh build/bin
2024-11-01T18:06:45.1196690Z + set -e
2024-11-01T18:06:45.1197093Z ++ dirname aten/tools/run_tests.sh
2024-11-01T18:06:45.1197719Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup
2024-11-01T18:06:45.1198325Z + export CPP_TESTS_DIR=build/bin
2024-11-01T18:06:45.1198727Z + CPP_TESTS_DIR=build/bin
2024-11-01T18:06:45.1199065Z + VALGRIND=ON
2024-11-01T18:06:45.1201883Z + 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
2024-11-01T18:06:45.2334955Z /var/lib/jenkins/workspace/test/run_test.py:21: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
2024-11-01T18:06:45.2336203Z   import pkg_resources
2024-11-01T18:06:49.2779012Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json
2024-11-01T18:06:49.2921627Z Found test times from artifacts
2024-11-01T18:06:49.3504489Z Found test times from artifacts
2024-11-01T18:06:49.3523023Z Running all tests
2024-11-01T18:06:49.3527563Z Running parallel tests on 3 processes
2024-11-01T18:06:49.3528984Z Name: tests to run (est. time: 0.0min)
2024-11-01T18:06:49.3529726Z   Serial tests (0):
2024-11-01T18:06:49.3530410Z   Parallel tests (19):
2024-11-01T18:06:49.3530831Z     cpp/Dict_test 1/1
2024-11-01T18:06:49.3531177Z     cpp/Dimname_test 1/1
2024-11-01T18:06:49.3531622Z     cpp/NamedTensor_test 1/1
2024-11-01T18:06:49.3531995Z     cpp/apply_utils_test 1/1
2024-11-01T18:06:49.3532602Z     cpp/atest 1/1
2024-11-01T18:06:49.3532916Z     cpp/basic 1/1
2024-11-01T18:06:49.3533314Z     cpp/broadcast_test 1/1
2024-11-01T18:06:49.3533692Z     cpp/cpu_generator_test 1/1
2024-11-01T18:06:49.3534156Z     cpp/dlconvertor_test 1/1
2024-11-01T18:06:49.3534549Z     cpp/extension_backend_test 1/1
2024-11-01T18:06:49.3535048Z     cpp/lazy_tensor_test 1/1
2024-11-01T18:06:49.3535501Z     cpp/legacy_vmap_test 1/1
2024-11-01T18:06:49.3535876Z     cpp/native_test 1/1
2024-11-01T18:06:49.3536293Z     cpp/operators_test 1/1
2024-11-01T18:06:49.3536668Z     cpp/scalar_tensor_test 1/1
2024-11-01T18:06:49.3537123Z     cpp/scalar_test 1/1
2024-11-01T18:06:49.3537483Z     cpp/tensor_iterator_test 1/1
2024-11-01T18:06:49.3538057Z     cpp/undefined_tensor_test 1/1
2024-11-01T18:06:49.3538446Z     cpp/wrapdim_test 1/1
2024-11-01T18:06:49.3538892Z Name: excluded (est. time: 0.0min)
2024-11-01T18:06:49.3539326Z   Serial tests (0):
2024-11-01T18:06:49.3539699Z   Parallel tests (0):
2024-11-01T18:06:49.3590329Z Running cpp/Dict_test 1/1 ... [2024-11-01 18:06:49.358650]
2024-11-01T18:06:49.3591334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:06:49.3599293Z 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-8496927988dbc92a.xml', '-x', '--reruns=2'] ... [2024-11-01 18:06:49.359393]
2024-11-01T18:06:52.0317170Z 
2024-11-01T18:06:52.0319109Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_d4068767114b2d3a_.log
2024-11-01T18:06:52.0320484Z 
2024-11-01T18:06:52.0321048Z Running cpp/Dimname_test 1/1 ... [2024-11-01 18:06:52.031688]
2024-11-01T18:06:52.0321920Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:06:52.0327936Z 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-6bbe9b9921925de0.xml', '-x', '--reruns=2'] ... [2024-11-01 18:06:52.032301]
2024-11-01T18:06:54.2016838Z 
2024-11-01T18:06:54.2018644Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_36316fe48486d0ee_.log
2024-11-01T18:06:54.2020908Z 
2024-11-01T18:06:54.2021418Z Running cpp/NamedTensor_test 1/1 ... [2024-11-01 18:06:54.201450]
2024-11-01T18:06:54.2022022Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:06:54.2024009Z 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-b11750b41b65f859.xml', '-x', '--reruns=2'] ... [2024-11-01 18:06:54.201811]
2024-11-01T18:06:56.0700049Z 
2024-11-01T18:06:56.0702391Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_7245c9a47a6ab667_.log
2024-11-01T18:06:56.0704056Z 
2024-11-01T18:06:56.0704833Z Running cpp/apply_utils_test 1/1 ... [2024-11-01 18:06:56.069813]
2024-11-01T18:06:56.0705846Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:06:56.0708530Z 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-fe47a43220d2f59e.xml', '-x', '--reruns=2'] ... [2024-11-01 18:06:56.070250]
2024-11-01T18:06:57.9387847Z 
2024-11-01T18:06:57.9389767Z 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_4b3ade7e1be5dcc0_.log
2024-11-01T18:06:57.9390939Z 
2024-11-01T18:06:57.9391326Z Running cpp/atest 1/1 ... [2024-11-01 18:06:57.938582]
2024-11-01T18:06:57.9392062Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:06:57.9394535Z 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-3ffa705172ce40e7.xml', '-x', '--reruns=2'] ... [2024-11-01 18:06:57.938984]
2024-11-01T18:06:59.8074288Z 
2024-11-01T18:06:59.8076049Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_7c8987c83ab6f8bb_.log
2024-11-01T18:06:59.8077501Z 
2024-11-01T18:06:59.8078100Z Running cpp/basic 1/1 ... [2024-11-01 18:06:59.807252]
2024-11-01T18:06:59.8078810Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:06:59.8080700Z 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-0a456e4ca6f7f2ee.xml', '-x', '--reruns=2'] ... [2024-11-01 18:06:59.807661]
2024-11-01T18:07:01.6758642Z 
2024-11-01T18:07:01.6760280Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_fdaafaac2d0a0063_.log
2024-11-01T18:07:01.6761343Z 
2024-11-01T18:07:01.6761728Z Running cpp/broadcast_test 1/1 ... [2024-11-01 18:07:01.675720]
2024-11-01T18:07:01.6762443Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:01.6764904Z 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-38f0cc17c5eab57a.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:01.676125]
2024-11-01T18:07:03.5445551Z 
2024-11-01T18:07:03.5448063Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_ce47bbe7985f586c_.log
2024-11-01T18:07:03.5449099Z 
2024-11-01T18:07:03.5449518Z Running cpp/cpu_generator_test 1/1 ... [2024-11-01 18:07:03.544387]
2024-11-01T18:07:03.5450622Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:03.5452580Z 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-355118ff2249893e.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:03.544816]
2024-11-01T18:07:05.3625909Z 
2024-11-01T18:07:05.3628021Z 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_d9d65dff54f62ae0_.log
2024-11-01T18:07:05.3629198Z 
2024-11-01T18:07:05.3629690Z Running cpp/dlconvertor_test 1/1 ... [2024-11-01 18:07:05.362461]
2024-11-01T18:07:05.3630513Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:05.3632725Z 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-17ea9a92be1ebc73.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:05.362861]
2024-11-01T18:07:07.1810275Z 
2024-11-01T18:07:07.1811925Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_7e9b9517603c5727_.log
2024-11-01T18:07:07.1812934Z 
2024-11-01T18:07:07.1813521Z Running cpp/extension_backend_test 1/1 ... [2024-11-01 18:07:07.180853]
2024-11-01T18:07:07.1814199Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:07.1816442Z 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-34d355a843b3b147.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:07.181228]
2024-11-01T18:07:09.0496099Z 
2024-11-01T18:07:09.0498082Z 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_aa2d3ed83098c28b_.log
2024-11-01T18:07:09.0499327Z 
2024-11-01T18:07:09.0499835Z Running cpp/lazy_tensor_test 1/1 ... [2024-11-01 18:07:09.049415]
2024-11-01T18:07:09.0500431Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:09.0502941Z 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-66cf3a07c483fa43.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:09.049845]
2024-11-01T18:07:10.8679207Z 
2024-11-01T18:07:10.8681326Z 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_0befa1b96e445a42_.log
2024-11-01T18:07:10.8682473Z 
2024-11-01T18:07:10.8683150Z Running cpp/legacy_vmap_test 1/1 ... [2024-11-01 18:07:10.867732]
2024-11-01T18:07:10.8683886Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:10.8685836Z 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-1092907e7ad91219.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:10.868104]
2024-11-01T18:07:12.6861144Z 
2024-11-01T18:07:12.6863059Z 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_539beb997fbd489a_.log
2024-11-01T18:07:12.6864328Z 
2024-11-01T18:07:12.6864719Z Running cpp/native_test 1/1 ... [2024-11-01 18:07:12.685919]
2024-11-01T18:07:12.6865658Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:12.6867601Z 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-9fcaa18a2ceca04a.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:12.686318]
2024-11-01T18:07:14.5044512Z 
2024-11-01T18:07:14.5046207Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_5cfa3b80209dcbe7_.log
2024-11-01T18:07:14.5047157Z 
2024-11-01T18:07:14.5047606Z Running cpp/operators_test 1/1 ... [2024-11-01 18:07:14.504264]
2024-11-01T18:07:14.5048450Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:14.5050496Z 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-b09ec9770d3a703c.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:14.504714]
2024-11-01T18:07:16.3229198Z 
2024-11-01T18:07:16.3231676Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_9ab70bdbf916ac12_.log
2024-11-01T18:07:16.3233367Z 
2024-11-01T18:07:16.3234257Z Running cpp/scalar_tensor_test 1/1 ... [2024-11-01 18:07:16.322718]
2024-11-01T18:07:16.3235260Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:16.3237429Z 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-39abaf43977f0b36.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:16.323169]
2024-11-01T18:07:18.1412738Z 
2024-11-01T18:07:18.1414519Z 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_8a07939b856735d3_.log
2024-11-01T18:07:18.1415777Z 
2024-11-01T18:07:18.1416217Z Running cpp/scalar_test 1/1 ... [2024-11-01 18:07:18.141095]
2024-11-01T18:07:18.1416862Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:18.1419035Z 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-2a694cb62e8f230c.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:18.141493]
2024-11-01T18:07:19.9599138Z 
2024-11-01T18:07:19.9600651Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_da406063e2029d1a_.log
2024-11-01T18:07:19.9601601Z 
2024-11-01T18:07:19.9602517Z Running cpp/tensor_iterator_test 1/1 ... [2024-11-01 18:07:19.959738]
2024-11-01T18:07:19.9603300Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:19.9605304Z 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-7429ec4f67a44fc9.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:19.960137]
2024-11-01T18:07:21.7782617Z 
2024-11-01T18:07:21.7784605Z 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_98bb8013e5c045b0_.log
2024-11-01T18:07:21.7785720Z 
2024-11-01T18:07:21.7786152Z Running cpp/undefined_tensor_test 1/1 ... [2024-11-01 18:07:21.778033]
2024-11-01T18:07:21.7787064Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:21.7789060Z 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-8ed554ec7695a2ab.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:21.778432]
2024-11-01T18:07:23.6468000Z 
2024-11-01T18:07:23.6470109Z 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_f1b418c43534b903_.log
2024-11-01T18:07:23.6471232Z 
2024-11-01T18:07:23.6471717Z Running cpp/wrapdim_test 1/1 ... [2024-11-01 18:07:23.646620]
2024-11-01T18:07:23.6472655Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:23.6474675Z 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-505d0bb1f666a73d.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:23.647000]
2024-11-01T18:07:25.5147515Z 
2024-11-01T18:07:25.5149136Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_d4c6da4bf459f4ec_.log
2024-11-01T18:07:25.5150232Z 
2024-11-01T18:07:25.5160059Z Running cpp/Dict_test 1/1 ... [2024-11-01 18:07:25.515667]
2024-11-01T18:07:25.5161122Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:25.5162261Z Running cpp/Dimname_test 1/1 ... [2024-11-01 18:07:25.515852]
2024-11-01T18:07:25.5163629Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:25.5164713Z Running cpp/NamedTensor_test 1/1 ... [2024-11-01 18:07:25.515916]
2024-11-01T18:07:25.5165574Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:25.5168612Z 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-5137ff296c50d852.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:25.516391]
2024-11-01T18:07:25.5173259Z 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-044bf9209c52b2bc.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:25.516499]
2024-11-01T18:07:25.5176511Z 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-05bc9f463dd47028.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:25.516666]
2024-11-01T18:07:30.2420103Z 
2024-11-01T18:07:30.2424624Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_d7f190929e759e7d_.log
2024-11-01T18:07:30.2426338Z 
2024-11-01T18:07:30.7428609Z 
2024-11-01T18:07:30.7430771Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_831a45ca519e066d_.log
2024-11-01T18:07:30.7433547Z 
2024-11-01T18:07:34.5847861Z Running cpp/apply_utils_test 1/1 ... [2024-11-01 18:07:34.584117]
2024-11-01T18:07:34.5850516Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:34.5854714Z 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-724958a0935ea287.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:34.584697]
2024-11-01T18:07:35.1916863Z Running cpp/atest 1/1 ... [2024-11-01 18:07:35.191197]
2024-11-01T18:07:35.1918428Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:35.1925798Z 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-b5263561bbe059e8.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:35.192061]
2024-11-01T18:07:38.7126775Z 
2024-11-01T18:07:38.7129620Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_17658e5783df7d34_.log
2024-11-01T18:07:38.7134794Z 
2024-11-01T18:07:39.2105837Z 
2024-11-01T18:07:39.2107968Z 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_264d72064ac771ab_.log
2024-11-01T18:07:39.2109194Z 
2024-11-01T18:07:41.6259477Z 
2024-11-01T18:07:41.6261690Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_9c18ad8a99d52f7b_.log
2024-11-01T18:07:41.6263141Z 
2024-11-01T18:07:43.2694565Z Running cpp/basic 1/1 ... [2024-11-01 18:07:43.268930]
2024-11-01T18:07:43.2695139Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:43.2698493Z 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-5d981ee5247c3c2b.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:43.269468]
2024-11-01T18:07:43.5422775Z Running cpp/broadcast_test 1/1 ... [2024-11-01 18:07:43.541762]
2024-11-01T18:07:43.5423743Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:43.5429281Z 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-b421538301543e74.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:43.542389]
2024-11-01T18:07:46.3650449Z Running cpp/cpu_generator_test 1/1 ... [2024-11-01 18:07:46.364541]
2024-11-01T18:07:46.3651502Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:46.3656019Z 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-698eca15b78bfdb8.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:46.365104]
2024-11-01T18:07:46.6699543Z 
2024-11-01T18:07:46.6701663Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_6b2598db85c54889_.log
2024-11-01T18:07:46.6703199Z 
2024-11-01T18:07:46.9538500Z 
2024-11-01T18:07:46.9540594Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_5115c17f24dbc94f_.log
2024-11-01T18:07:46.9542221Z 
2024-11-01T18:07:51.1251511Z Running cpp/dlconvertor_test 1/1 ... [2024-11-01 18:07:51.124550]
2024-11-01T18:07:51.1252710Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:51.1257384Z 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-dba9d84bed476d4d.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:51.125216]
2024-11-01T18:07:51.3555112Z Running cpp/extension_backend_test 1/1 ... [2024-11-01 18:07:51.354999]
2024-11-01T18:07:51.3556298Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:51.3561652Z 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-16b3494269da3f97.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:51.355611]
2024-11-01T18:07:51.9737110Z 
2024-11-01T18:07:51.9739336Z 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_974f3d5cfdb056be_.log
2024-11-01T18:07:51.9741118Z 
2024-11-01T18:07:54.2540997Z 
2024-11-01T18:07:54.2543763Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_be481891470f6e37_.log
2024-11-01T18:07:54.2550153Z 
2024-11-01T18:07:54.4777316Z 
2024-11-01T18:07:54.4779730Z 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_992180e53dbfaf23_.log
2024-11-01T18:07:54.4781525Z 
2024-11-01T18:07:56.6432254Z Running cpp/lazy_tensor_test 1/1 ... [2024-11-01 18:07:56.642734]
2024-11-01T18:07:56.6433010Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:56.6436212Z 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-b853361d76a8d113.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:56.643237]
2024-11-01T18:07:58.4257725Z Running cpp/legacy_vmap_test 1/1 ... [2024-11-01 18:07:58.425163]
2024-11-01T18:07:58.4258745Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:58.4262709Z 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-047afa422507e269.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:58.425780]
2024-11-01T18:07:58.9437838Z Running cpp/native_test 1/1 ... [2024-11-01 18:07:58.943068]
2024-11-01T18:07:58.9438947Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:07:58.9447399Z 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-724184fd4f5a207d.xml', '-x', '--reruns=2'] ... [2024-11-01 18:07:58.943813]
2024-11-01T18:07:59.5141478Z 
2024-11-01T18:07:59.5144145Z 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_1895295dbbb5fefd_.log
2024-11-01T18:07:59.5147728Z 
2024-11-01T18:08:02.1918465Z 
2024-11-01T18:08:02.1921097Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_fa05e8ccdb670b0c_.log
2024-11-01T18:08:02.1923212Z 
2024-11-01T18:08:04.3527958Z Running cpp/operators_test 1/1 ... [2024-11-01 18:08:04.352246]
2024-11-01T18:08:04.3529017Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:08:04.3534432Z 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-414bbf106f2cc142.xml', '-x', '--reruns=2'] ... [2024-11-01 18:08:04.352809]
2024-11-01T18:08:05.3055537Z 
2024-11-01T18:08:05.3058223Z 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_a24feb59c47ab93a_.log
2024-11-01T18:08:05.3060206Z 
2024-11-01T18:08:06.6613511Z Running cpp/scalar_tensor_test 1/1 ... [2024-11-01 18:08:06.660833]
2024-11-01T18:08:06.6614641Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:08:06.6620452Z 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-e8161910b7136b8d.xml', '-x', '--reruns=2'] ... [2024-11-01 18:08:06.661535]
2024-11-01T18:08:07.8276617Z 
2024-11-01T18:08:07.8279283Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_3b9c81d71fafc399_.log
2024-11-01T18:08:07.8281140Z 
2024-11-01T18:08:09.7329123Z 
2024-11-01T18:08:09.7331346Z 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_dca0e2677c4e68b3_.log
2024-11-01T18:08:09.7332541Z 
2024-11-01T18:08:10.0176946Z Running cpp/scalar_test 1/1 ... [2024-11-01 18:08:10.017209]
2024-11-01T18:08:10.0177870Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:08:10.0183685Z 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-1273e15d90575f07.xml', '-x', '--reruns=2'] ... [2024-11-01 18:08:10.017763]
2024-11-01T18:08:12.5252330Z Running cpp/tensor_iterator_test 1/1 ... [2024-11-01 18:08:12.524648]
2024-11-01T18:08:12.5253459Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:08:12.5259439Z 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-0711e19c2302382c.xml', '-x', '--reruns=2'] ... [2024-11-01 18:08:12.525399]
2024-11-01T18:08:13.2891026Z 
2024-11-01T18:08:13.2893152Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_774eaf744e28518e_.log
2024-11-01T18:08:13.2894555Z 
2024-11-01T18:08:13.9384133Z Running cpp/undefined_tensor_test 1/1 ... [2024-11-01 18:08:13.937809]
2024-11-01T18:08:13.9385924Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:08:13.9392881Z 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-50c4d6995db9540b.xml', '-x', '--reruns=2'] ... [2024-11-01 18:08:13.938388]
2024-11-01T18:08:17.0137941Z 
2024-11-01T18:08:17.0140424Z 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_5d7c76fc7f3fda25_.log
2024-11-01T18:08:17.0142573Z 
2024-11-01T18:08:18.8400788Z Running cpp/wrapdim_test 1/1 ... [2024-11-01 18:08:18.839499]
2024-11-01T18:08:18.8402260Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set
2024-11-01T18:08:18.8407974Z 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-5dcb8e5725cc48e6.xml', '-x', '--reruns=2'] ... [2024-11-01 18:08:18.840209]
2024-11-01T18:08:22.1162679Z 
2024-11-01T18:08:22.1165260Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_c35c0c6bafc08019_.log
2024-11-01T18:08:22.1167128Z 
2024-11-01T18:08:26.1738487Z 
2024-11-01T18:08:26.1740752Z 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_318d3eca38c05299_.log
2024-11-01T18:08:26.1741782Z 
2024-11-01T18:08:27.1029640Z Running test batch 'tests to run' cost 97.75 seconds
2024-11-01T18:08:27.8698078Z + run_if_exists tensor_interop_test
2024-11-01T18:08:27.8699027Z + local test_name=tensor_interop_test
2024-11-01T18:08:27.8700152Z + [[ -x build/bin/tensor_interop_test ]]
2024-11-01T18:08:27.8701151Z + echo 'Warning: tensor_interop_test does not exist.'
2024-11-01T18:08:27.8702137Z Warning: tensor_interop_test does not exist.
2024-11-01T18:08:27.8702933Z + run_if_exists cudnn_test
2024-11-01T18:08:27.8703567Z + local test_name=cudnn_test
2024-11-01T18:08:27.8704401Z + [[ -x build/bin/cudnn_test ]]
2024-11-01T18:08:27.8705294Z + echo 'Warning: cudnn_test does not exist.'
2024-11-01T18:08:27.8706089Z Warning: cudnn_test does not exist.
2024-11-01T18:08:27.8706843Z + run_if_exists cuda_generator_test
2024-11-01T18:08:27.8707762Z + local test_name=cuda_generator_test
2024-11-01T18:08:27.8712481Z + [[ -x build/bin/cuda_generator_test ]]
2024-11-01T18:08:27.8713248Z + echo 'Warning: cuda_generator_test does not exist.'
2024-11-01T18:08:27.8715371Z Warning: cuda_generator_test does not exist.
2024-11-01T18:08:27.8715970Z + run_if_exists apply_test
2024-11-01T18:08:27.8716336Z + local test_name=apply_test
2024-11-01T18:08:27.8716809Z + [[ -x build/bin/apply_test ]]
2024-11-01T18:08:27.8717589Z + echo 'Warning: apply_test does not exist.'
2024-11-01T18:08:27.8718468Z Warning: apply_test does not exist.
2024-11-01T18:08:27.8719213Z + run_if_exists stream_test
2024-11-01T18:08:27.8719907Z + local test_name=stream_test
2024-11-01T18:08:27.8720728Z + [[ -x build/bin/stream_test ]]
2024-11-01T18:08:27.8721471Z + echo 'Warning: stream_test does not exist.'
2024-11-01T18:08:27.8721970Z Warning: stream_test does not exist.
2024-11-01T18:08:27.8722402Z + run_if_exists cuda_half_test
2024-11-01T18:08:27.8722781Z + local test_name=cuda_half_test
2024-11-01T18:08:27.8723246Z + [[ -x build/bin/cuda_half_test ]]
2024-11-01T18:08:27.8723772Z + echo 'Warning: cuda_half_test does not exist.'
2024-11-01T18:08:27.8724271Z Warning: cuda_half_test does not exist.
2024-11-01T18:08:27.8724720Z + run_if_exists cuda_vectorized_test
2024-11-01T18:08:27.8725190Z + local test_name=cuda_vectorized_test
2024-11-01T18:08:27.8725713Z + [[ -x build/bin/cuda_vectorized_test ]]
2024-11-01T18:08:27.8726290Z + echo 'Warning: cuda_vectorized_test does not exist.'
2024-11-01T18:08:27.8726854Z Warning: cuda_vectorized_test does not exist.
2024-11-01T18:08:27.8727346Z + run_if_exists cuda_distributions_test
2024-11-01T18:08:27.8727818Z + local test_name=cuda_distributions_test
2024-11-01T18:08:27.8728356Z + [[ -x build/bin/cuda_distributions_test ]]
2024-11-01T18:08:27.8728975Z + echo 'Warning: cuda_distributions_test does not exist.'
2024-11-01T18:08:27.8729565Z Warning: cuda_distributions_test does not exist.
2024-11-01T18:08:27.8730071Z + run_if_exists cuda_optional_test
2024-11-01T18:08:27.8730493Z + local test_name=cuda_optional_test
2024-11-01T18:08:27.8730982Z + [[ -x build/bin/cuda_optional_test ]]
2024-11-01T18:08:27.8731538Z + echo 'Warning: cuda_optional_test does not exist.'
2024-11-01T18:08:27.8732075Z Warning: cuda_optional_test does not exist.
2024-11-01T18:08:27.8732663Z + run_if_exists cuda_tensor_interop_test
2024-11-01T18:08:27.8733204Z + local test_name=cuda_tensor_interop_test
2024-11-01T18:08:27.8733759Z + [[ -x build/bin/cuda_tensor_interop_test ]]
2024-11-01T18:08:27.8734397Z + echo 'Warning: cuda_tensor_interop_test does not exist.'
2024-11-01T18:08:27.8734981Z Warning: cuda_tensor_interop_test does not exist.
2024-11-01T18:08:27.8735479Z + run_if_exists cuda_complex_test
2024-11-01T18:08:27.8735892Z + local test_name=cuda_complex_test
2024-11-01T18:08:27.8736375Z + [[ -x build/bin/cuda_complex_test ]]
2024-11-01T18:08:27.8736934Z + echo 'Warning: cuda_complex_test does not exist.'
2024-11-01T18:08:27.8737456Z Warning: cuda_complex_test does not exist.
2024-11-01T18:08:27.8737929Z + run_if_exists cuda_complex_math_test
2024-11-01T18:08:27.8738379Z + local test_name=cuda_complex_math_test
2024-11-01T18:08:27.8738902Z + [[ -x build/bin/cuda_complex_math_test ]]
2024-11-01T18:08:27.8739514Z + echo 'Warning: cuda_complex_math_test does not exist.'
2024-11-01T18:08:27.8740098Z Warning: cuda_complex_math_test does not exist.
2024-11-01T18:08:27.8740570Z + run_if_exists cuda_cub_test
2024-11-01T18:08:27.8740957Z + local test_name=cuda_cub_test
2024-11-01T18:08:27.8741413Z + [[ -x build/bin/cuda_cub_test ]]
2024-11-01T18:08:27.8741935Z + echo 'Warning: cuda_cub_test does not exist.'
2024-11-01T18:08:27.8742424Z Warning: cuda_cub_test does not exist.
2024-11-01T18:08:27.8742853Z + run_if_exists cuda_atomic_ops_test
2024-11-01T18:08:27.8752673Z + local test_name=cuda_atomic_ops_test
2024-11-01T18:08:27.8753455Z + [[ -x build/bin/cuda_atomic_ops_test ]]
2024-11-01T18:08:27.8754176Z + echo 'Warning: cuda_atomic_ops_test does not exist.'
2024-11-01T18:08:27.8754858Z Warning: cuda_atomic_ops_test does not exist.
2024-11-01T18:08:27.8755359Z + '[' ON == ON ']'
2024-11-01T18:08:27.8756458Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA'
2024-11-01T18:08:27.9020915Z ==8316== Memcheck, a memory error detector
2024-11-01T18:08:27.9022000Z ==8316== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al.
2024-11-01T18:08:27.9022890Z ==8316== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info
2024-11-01T18:08:27.9023689Z ==8316== Command: build/bin/basic --gtest_filter=-*CUDA
2024-11-01T18:08:27.9024175Z ==8316== 
2024-11-01T18:08:28.4056725Z ==8316== Warning: set address range perms: large range [0x4a08000, 0x15068000) (defined)
2024-11-01T18:08:58.5871318Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc
2024-11-01T18:08:58.6104164Z Note: Google Test filter = -*CUDA
2024-11-01T18:08:58.6158321Z [==========] Running 4 tests from 1 test suite.
2024-11-01T18:08:58.6185698Z [----------] Global test environment set-up.
2024-11-01T18:08:58.6227306Z [----------] 4 tests from BasicTest
2024-11-01T18:08:58.6249660Z [ RUN      ] BasicTest.BasicTestCPU
2024-11-01T18:09:00.1080760Z    391 ms
2024-11-01T18:09:00.1964329Z    55 ms
2024-11-01T18:09:00.2740775Z    68 ms
2024-11-01T18:09:00.9862949Z [       OK ] BasicTest.BasicTestCPU (2359 ms)
2024-11-01T18:09:00.9871275Z [ RUN      ] BasicTest.BasicTestHalfCPU
2024-11-01T18:09:01.1503370Z    115 ms
2024-11-01T18:09:01.2027476Z    47 ms
2024-11-01T18:09:01.2729385Z    68 ms
2024-11-01T18:09:01.3321463Z [       OK ] BasicTest.BasicTestHalfCPU (344 ms)
2024-11-01T18:09:01.3323114Z [ RUN      ] BasicTest.FactoryMethodsTest
2024-11-01T18:09:01.3739910Z [       OK ] BasicTest.FactoryMethodsTest (41 ms)
2024-11-01T18:09:01.3740514Z [ RUN      ] BasicTest.BasicStdTestCPU
2024-11-01T18:09:01.5064633Z Simple example: called once
2024-11-01T18:09:01.5608236Z throw: call_once will retry
2024-11-01T18:09:01.6051632Z throw: call_once will retry
2024-11-01T18:09:01.6060844Z Didn't throw, call_once will not attempt again
2024-11-01T18:09:01.6081867Z [       OK ] BasicTest.BasicStdTestCPU (234 ms)
2024-11-01T18:09:01.6106004Z [----------] 4 tests from BasicTest (2984 ms total)
2024-11-01T18:09:01.6106886Z 
2024-11-01T18:09:01.6117965Z [----------] Global test environment tear-down
2024-11-01T18:09:01.6151428Z [==========] 4 tests from 1 test suite ran. (3006 ms total)
2024-11-01T18:09:01.6163444Z [  PASSED  ] 4 tests.
2024-11-01T18:09:03.6350793Z ==8316== 
2024-11-01T18:09:03.6354677Z ==8316== HEAP SUMMARY:
2024-11-01T18:09:03.6355236Z ==8316==     in use at exit: 240,168 bytes in 3,998 blocks
2024-11-01T18:09:03.6357389Z ==8316==   total heap usage: 737,895 allocs, 733,897 frees, 214,356,996 bytes allocated
2024-11-01T18:09:03.6358434Z ==8316== 
2024-11-01T18:09:03.6760390Z ==8316== LEAK SUMMARY:
2024-11-01T18:09:03.6761077Z ==8316==    definitely lost: 0 bytes in 0 blocks
2024-11-01T18:09:03.6761694Z ==8316==    indirectly lost: 0 bytes in 0 blocks
2024-11-01T18:09:03.6762740Z ==8316==      possibly lost: 0 bytes in 0 blocks
2024-11-01T18:09:03.6763555Z ==8316==    still reachable: 240,168 bytes in 3,998 blocks
2024-11-01T18:09:03.6764160Z ==8316==         suppressed: 0 bytes in 0 blocks
2024-11-01T18:09:03.6765615Z ==8316== Rerun with --leak-check=full to see details of leaked memory
2024-11-01T18:09:03.6766294Z ==8316== 
2024-11-01T18:09:03.6766798Z ==8316== For lists of detected and suppressed errors, rerun with: -s
2024-11-01T18:09:03.6767551Z ==8316== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)
2024-11-01T18:09:03.7293825Z + [[ -x build/bin/tensor_interop_test ]]
2024-11-01T18:09:03.7294899Z + [[ -n '' ]]
2024-11-01T18:09:03.7295491Z + assert_git_not_dirty
2024-11-01T18:09:03.7296063Z + [[ linux-focal-py3.12-clang10 != *rocm* ]]
2024-11-01T18:09:03.7296631Z + [[ linux-focal-py3.12-clang10 != *xla* ]]
2024-11-01T18:09:03.7302604Z ++ git status --porcelain
2024-11-01T18:09:03.7303287Z ++ grep -v '?? third_party'
2024-11-01T18:09:03.9507601Z ++ true
2024-11-01T18:09:03.9508254Z + git_status=
2024-11-01T18:09:03.9508994Z + [[ -n '' ]]
2024-11-01T18:09:03.9511127Z + cleanup_workspace
2024-11-01T18:09:03.9512264Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.'
2024-11-01T18:09:03.9513495Z sudo may print the following warning message that can be ignored. The chown command will still run.
2024-11-01T18:09:03.9514655Z + echo '    sudo: setrlimit(RLIMIT_STACK): Operation not permitted'
2024-11-01T18:09:03.9515363Z     sudo: setrlimit(RLIMIT_STACK): Operation not permitted
2024-11-01T18:09:03.9516270Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42'
2024-11-01T18:09:03.9517468Z For more details refer to https://github.com/sudo-project/sudo/issues/42
2024-11-01T18:09:03.9518225Z + sudo chown -R 1000 /var/lib/jenkins/workspace
2024-11-01T18:09:06.5016582Z ##[group]Run cat test/**/*_toprint.log || true
2024-11-01T18:09:06.5017157Z cat test/**/*_toprint.log || true
2024-11-01T18:09:06.5027160Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:06.5027701Z env:
2024-11-01T18:09:06.5027979Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:06.5028625Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:06.5029324Z ##[endgroup]
2024-11-01T18:09:06.5124779Z cat: 'test/**/*_toprint.log': No such file or directory
2024-11-01T18:09:06.5160979Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2024-11-01T18:09:06.5161531Z kill "$MONITOR_SCRIPT_PID"
2024-11-01T18:09:06.5168653Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:06.5169189Z env:
2024-11-01T18:09:06.5169479Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:06.5170109Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:06.5170832Z   MONITOR_SCRIPT_PID: 39667
2024-11-01T18:09:06.5171199Z ##[endgroup]
2024-11-01T18:09:06.5392231Z Prepare all required actions
2024-11-01T18:09:06.5392720Z Getting action download info
2024-11-01T18:09:06.7791688Z Download action repository 'actions/upload-artifact@v3' (SHA:ff15f0306b3f739f7b6fd43fb5d26cd321bd4de5)
2024-11-01T18:09:06.9355365Z ##[group]Run ./.github/actions/upload-test-artifacts
2024-11-01T18:09:06.9356046Z with:
2024-11-01T18:09:06.9356487Z   file-suffix: test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T18:09:06.9357070Z   s3-bucket: gha-artifacts
2024-11-01T18:09:06.9357425Z env:
2024-11-01T18:09:06.9357709Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:06.9358335Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:06.9359025Z ##[endgroup]
2024-11-01T18:09:06.9392455Z ##[group]Run # Remove any previous test jsons if they exist
2024-11-01T18:09:06.9393130Z # Remove any previous test jsons if they exist
2024-11-01T18:09:06.9393669Z rm -f test-jsons-*.zip
2024-11-01T18:09:06.9394511Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json'
2024-11-01T18:09:06.9401328Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:06.9401855Z env:
2024-11-01T18:09:06.9402124Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:06.9402781Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:06.9403633Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T18:09:06.9404191Z ##[endgroup]
2024-11-01T18:09:06.9538107Z   adding: test/test-reports/td_exclusions-112996643e7cda849cec.json (deflated 81%)
2024-11-01T18:09:06.9539323Z   adding: test/test-reports/td_exclusions-1caf89c88689ba344f32.json (deflated 73%)
2024-11-01T18:09:06.9568916Z ##[group]Run # Remove any previous test reports if they exist
2024-11-01T18:09:06.9569630Z # Remove any previous test reports if they exist
2024-11-01T18:09:06.9570201Z rm -f test-reports-*.zip
2024-11-01T18:09:06.9570920Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv'
2024-11-01T18:09:06.9577555Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:06.9578081Z env:
2024-11-01T18:09:06.9578364Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:06.9579006Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:06.9579865Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T18:09:06.9580431Z ##[endgroup]
2024-11-01T18:09:06.9655903Z   adding: test/test-reports/python-pytest/test_cpp_extensions_open_device_registration/test_cpp_extensions_open_device_registration-ad5e5d1214867b9a.xml (deflated 91%)
2024-11-01T18:09:06.9710884Z   adding: test/test-reports/python-pytest/test_cpp_api_parity/test_cpp_api_parity-c6bad802c4ef1ae0.xml (deflated 99%)
2024-11-01T18:09:06.9713055Z   adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-d731b034feaf1cf9.xml (deflated 94%)
2024-11-01T18:09:06.9714665Z   adding: test/test-reports/python-pytest/test_autocast/test_autocast-6cd71de920db7a62.xml (deflated 86%)
2024-11-01T18:09:06.9716509Z   adding: test/test-reports/python-pytest/test_cpp_extensions_jit/test_cpp_extensions_jit-06f0489e6af06af5.xml (deflated 89%)
2024-11-01T18:09:06.9718901Z   adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-188e081cd8362cc2.xml (deflated 89%)
2024-11-01T18:09:06.9720929Z   adding: test/test-reports/python-pytest/test_native_mha/test_native_mha-4590a3bf713743fa.xml (deflated 95%)
2024-11-01T18:09:06.9723556Z   adding: test/test-reports/python-pytest/test_sort_and_select/test_sort_and_select-0afe38b0f739d121.xml (deflated 90%)
2024-11-01T18:09:06.9725852Z   adding: test/test-reports/python-pytest/nn.test_pooling/nn.test_pooling-4231ceff21bb0a8c.xml (deflated 89%)
2024-11-01T18:09:06.9746438Z   adding: test/test-reports/python-pytest/test_tensor_creation_ops/test_tensor_creation_ops-d3511866f487abd6.xml (deflated 94%)
2024-11-01T18:09:06.9748352Z   adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-f7f5d1a4d8b1d246.xml (deflated 59%)
2024-11-01T18:09:06.9784809Z   adding: test/test-reports/python-pytest/test_nn/test_nn-811b35c4316ae4ff.xml (deflated 95%)
2024-11-01T18:09:06.9792729Z   adding: test/test-reports/python-pytest/test_spectral_ops/test_spectral_ops-6d1b4aa6d25e4e96.xml (deflated 95%)
2024-11-01T18:09:06.9796724Z   adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-a2d0bb2583bcd9f0.xml (deflated 93%)
2024-11-01T18:09:06.9799785Z   adding: test/test-reports/python-pytest/distributions.test_distributions/distributions.test_distributions-85c942d2d7700d56.xml (deflated 93%)
2024-11-01T18:09:06.9802723Z   adding: test/test-reports/python-pytest/test_cpp_extensions_aot_ninja/test_cpp_extensions_aot_ninja-fd2fbf1053157edb.xml (deflated 90%)
2024-11-01T18:09:06.9805551Z   adding: test/test-reports/python-pytest/test_cpp_extensions_aot_no_ninja/test_cpp_extensions_aot_no_ninja-dbe8c4e582b27d08.xml (deflated 90%)
2024-11-01T18:09:06.9807913Z   adding: test/test-reports/python-pytest/test_namedtuple_return_api/test_namedtuple_return_api-fc76c45bc816d54b.xml (deflated 72%)
2024-11-01T18:09:06.9809503Z   adding: test/test-reports/python-pytest/test_autograd_fallback/test_autograd_fallback-a4a10ee4d0a41b73.xml (deflated 88%)
2024-11-01T18:09:06.9810982Z   adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-5d580537b054fdd3.xml (deflated 56%)
2024-11-01T18:09:06.9812578Z   adding: test/test-reports/python-pytest/test_cpp_extensions_mtia_backend/test_cpp_extensions_mtia_backend-fab63b2e521a5fdd.xml (deflated 79%)
2024-11-01T18:09:06.9814814Z   adding: test/test-reports/python-pytest/test_cpp_extensions_stream_and_event/test_cpp_extensions_stream_and_event-6c4ccc50f7f12612.xml (deflated 59%)
2024-11-01T18:09:06.9816411Z   adding: test/test-reports/python-pytest/test_tensorexpr/test_tensorexpr-0653b23e48a67357.xml (deflated 95%)
2024-11-01T18:09:06.9817846Z   adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-0c19ade8ef23f48f.xml (deflated 93%)
2024-11-01T18:09:06.9869212Z   adding: test/test-reports/python-pytest/test_reductions/test_reductions-5911c60ad6681e6c.xml (deflated 98%)
2024-11-01T18:09:06.9929359Z   adding: test/test-reports/python-pytest/test_reductions/test_reductions-048dd231d93f2e84.xml (deflated 98%)
2024-11-01T18:09:06.9955325Z   adding: test/test-reports/python-pytest/test_overrides/test_overrides-c57c3d580fd47a17.xml (deflated 95%)
2024-11-01T18:09:06.9958214Z   adding: test/test-reports/python-pytest/test_model_exports_to_core_aten/test_model_exports_to_core_aten-dd4dbc9a608eca5c.xml (deflated 28%)
2024-11-01T18:09:06.9961002Z   adding: test/test-reports/python-pytest/test_model_exports_to_core_aten/test_model_exports_to_core_aten-1c4f20f408dbf8fa.xml (deflated 58%)
2024-11-01T18:09:06.9963693Z   adding: test/test-reports/python-pytest/test_namedtensor/test_namedtensor-75bb2c4e7c4eb6b9.xml (deflated 28%)
2024-11-01T18:09:06.9965124Z   adding: test/test-reports/python-pytest/test_namedtensor/test_namedtensor-21d5e5ba28a23eca.xml (deflated 87%)
2024-11-01T18:09:06.9966800Z   adding: test/test-reports/python-pytest/higher_order_ops.test_invoke_subgraph/higher_order_ops.test_invoke_subgraph-1f367c3d7afed401.xml (deflated 28%)
2024-11-01T18:09:06.9969661Z   adding: test/test-reports/python-pytest/higher_order_ops.test_invoke_subgraph/higher_order_ops.test_invoke_subgraph-8676288cad31d04c.xml (deflated 87%)
2024-11-01T18:09:06.9972004Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_numeric/torch_np.numpy_tests.core.test_numeric-d067dc48d508576d.xml (deflated 28%)
2024-11-01T18:09:06.9975265Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_numeric/torch_np.numpy_tests.core.test_numeric-3a7d3c16874e1ee6.xml (deflated 92%)
2024-11-01T18:09:06.9977780Z   adding: test/test-reports/python-pytest/test_cuda_sanitizer/test_cuda_sanitizer-a30743b35364dbfe.xml (deflated 28%)
2024-11-01T18:09:06.9979269Z   adding: test/test-reports/python-pytest/test_cuda_sanitizer/test_cuda_sanitizer-caca7889aa6bde82.xml (deflated 28%)
2024-11-01T18:09:06.9981205Z   adding: test/test-reports/python-pytest/test_fx_passes/test_fx_passes-b819b22edcfb13f0.xml (deflated 27%)
2024-11-01T18:09:06.9982635Z   adding: test/test-reports/python-pytest/test_fx_passes/test_fx_passes-6b39f08f9b838159.xml (deflated 91%)
2024-11-01T18:09:06.9984742Z   adding: test/test-reports/python-pytest/distributions.test_constraints/distributions.test_constraints-148277e120ad9734.xml (deflated 28%)
2024-11-01T18:09:06.9986536Z   adding: test/test-reports/python-pytest/distributions.test_constraints/distributions.test_constraints-26318f72390fa084.xml (deflated 96%)
2024-11-01T18:09:06.9988180Z   adding: test/test-reports/python-pytest/test_fx_reinplace_pass/test_fx_reinplace_pass-8172e630a05a7070.xml (deflated 28%)
2024-11-01T18:09:06.9989713Z   adding: test/test-reports/python-pytest/test_fx_reinplace_pass/test_fx_reinplace_pass-5d567349ac260282.xml (deflated 86%)
2024-11-01T18:09:06.9991388Z   adding: test/test-reports/python-pytest/higher_order_ops.test_with_effects/higher_order_ops.test_with_effects-537366aeb87871b8.xml (deflated 28%)
2024-11-01T18:09:06.9993222Z   adding: test/test-reports/python-pytest/higher_order_ops.test_with_effects/higher_order_ops.test_with_effects-9650a3ee91ed37e0.xml (deflated 81%)
2024-11-01T18:09:06.9995234Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_type_check/torch_np.numpy_tests.lib.test_type_check-201b81f18e6b2b97.xml (deflated 28%)
2024-11-01T18:09:06.9997208Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_type_check/torch_np.numpy_tests.lib.test_type_check-ca9a576f30fa563e.xml (deflated 89%)
2024-11-01T18:09:06.9999631Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_histograms/torch_np.numpy_tests.lib.test_histograms-e2db65c851082fa9.xml (deflated 27%)
2024-11-01T18:09:07.0001640Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_histograms/torch_np.numpy_tests.lib.test_histograms-66be50a63e18769f.xml (deflated 88%)
2024-11-01T18:09:07.0003609Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_indexing/torch_np.numpy_tests.core.test_indexing-ab5999a06cae1f28.xml (deflated 28%)
2024-11-01T18:09:07.0006289Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_indexing/torch_np.numpy_tests.core.test_indexing-4c488fc303e1846b.xml (deflated 89%)
2024-11-01T18:09:07.0009323Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_function_base/torch_np.numpy_tests.lib.test_function_base-39e577786a01b558.xml (deflated 28%)
2024-11-01T18:09:07.0012428Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_function_base/torch_np.numpy_tests.lib.test_function_base-dedcdea9cac8ecc3.xml (deflated 94%)
2024-11-01T18:09:07.0015223Z   adding: test/test-reports/python-pytest/test_legacy_vmap/test_legacy_vmap-a854cc71e5034146.xml (deflated 28%)
2024-11-01T18:09:07.0017475Z   adding: test/test-reports/python-pytest/test_legacy_vmap/test_legacy_vmap-5f5dc13c9e70c01f.xml (deflated 91%)
2024-11-01T18:09:07.0019811Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_numerictypes/torch_np.numpy_tests.core.test_numerictypes-4cc4b5b393718b2e.xml (deflated 28%)
2024-11-01T18:09:07.0021897Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_numerictypes/torch_np.numpy_tests.core.test_numerictypes-f0819c37a9d519ed.xml (deflated 90%)
2024-11-01T18:09:07.0025414Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_arraysetops/torch_np.numpy_tests.lib.test_arraysetops-66150fe680ff0cea.xml (deflated 28%)
2024-11-01T18:09:07.0029253Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_arraysetops/torch_np.numpy_tests.lib.test_arraysetops-81abb6b383977e20.xml (deflated 92%)
2024-11-01T18:09:07.0032481Z   adding: test/test-reports/python-pytest/test_cuda_multigpu/test_cuda_multigpu-fcc97c8198cc165b.xml (deflated 28%)
2024-11-01T18:09:07.0035433Z   adding: test/test-reports/python-pytest/test_cuda_multigpu/test_cuda_multigpu-f52ac0ef5810ae90.xml (deflated 28%)
2024-11-01T18:09:07.0038141Z   adding: test/test-reports/python-pytest/profiler.test_profiler_tree/profiler.test_profiler_tree-70d37e702099deec.xml (deflated 28%)
2024-11-01T18:09:07.0040498Z   adding: test/test-reports/python-pytest/profiler.test_profiler_tree/profiler.test_profiler_tree-653b96083fbe583e.xml (deflated 89%)
2024-11-01T18:09:07.0042300Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.fft.test_helper/torch_np.numpy_tests.fft.test_helper-3eaebfa9ec85551d.xml (deflated 28%)
2024-11-01T18:09:07.0044396Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.fft.test_helper/torch_np.numpy_tests.fft.test_helper-b2c3149d25924d32.xml (deflated 74%)
2024-11-01T18:09:07.0046768Z   adding: test/test-reports/python-pytest/torch_np.test_scalars_0D_arrays/torch_np.test_scalars_0D_arrays-6e5fe7bdc558810a.xml (deflated 28%)
2024-11-01T18:09:07.0048583Z   adding: test/test-reports/python-pytest/torch_np.test_scalars_0D_arrays/torch_np.test_scalars_0D_arrays-c2c5daf244e5e272.xml (deflated 84%)
2024-11-01T18:09:07.0050379Z   adding: test/test-reports/python-pytest/profiler.test_memory_profiler/profiler.test_memory_profiler-551e8d90f9a8c615.xml (deflated 28%)
2024-11-01T18:09:07.0052202Z   adding: test/test-reports/python-pytest/profiler.test_memory_profiler/profiler.test_memory_profiler-dd9659b5ec344034.xml (deflated 93%)
2024-11-01T18:09:07.0054201Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_scalar_ctors/torch_np.numpy_tests.core.test_scalar_ctors-0a8509ba2526b1bd.xml (deflated 28%)
2024-11-01T18:09:07.0056292Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.core.test_scalar_ctors/torch_np.numpy_tests.core.test_scalar_ctors-714e2f03975b721c.xml (deflated 93%)
2024-11-01T18:09:07.0058319Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_arraypad/torch_np.numpy_tests.lib.test_arraypad-80f6afcb03d5baff.xml (deflated 28%)
2024-11-01T18:09:07.0060273Z   adding: test/test-reports/python-pytest/torch_np.numpy_tests.lib.test_arraypad/torch_np.numpy_tests.lib.test_arraypad-70e052c5650e91b4.xml (deflated 77%)
2024-11-01T18:09:07.0061934Z   adding: test/test-reports/python-pytest/test_dataloader/test_dataloader-fbe08735c657f14c.xml (deflated 28%)
2024-11-01T18:09:07.0063320Z   adding: test/test-reports/python-pytest/test_dataloader/test_dataloader-3c8aec68bba78894.xml (deflated 90%)
2024-11-01T18:09:07.0064661Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-8496927988dbc92a.xml (deflated 29%)
2024-11-01T18:09:07.0065976Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-6bbe9b9921925de0.xml (deflated 29%)
2024-11-01T18:09:07.0067469Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-b11750b41b65f859.xml (deflated 29%)
2024-11-01T18:09:07.0068781Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-fe47a43220d2f59e.xml (deflated 29%)
2024-11-01T18:09:07.0070082Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-3ffa705172ce40e7.xml (deflated 29%)
2024-11-01T18:09:07.0071388Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-0a456e4ca6f7f2ee.xml (deflated 29%)
2024-11-01T18:09:07.0072709Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-38f0cc17c5eab57a.xml (deflated 29%)
2024-11-01T18:09:07.0074134Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-355118ff2249893e.xml (deflated 29%)
2024-11-01T18:09:07.0075438Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-17ea9a92be1ebc73.xml (deflated 29%)
2024-11-01T18:09:07.0077138Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-34d355a843b3b147.xml (deflated 29%)
2024-11-01T18:09:07.0078997Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-66cf3a07c483fa43.xml (deflated 28%)
2024-11-01T18:09:07.0080411Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1092907e7ad91219.xml (deflated 29%)
2024-11-01T18:09:07.0081712Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-9fcaa18a2ceca04a.xml (deflated 29%)
2024-11-01T18:09:07.0083085Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-b09ec9770d3a703c.xml (deflated 29%)
2024-11-01T18:09:07.0084387Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-39abaf43977f0b36.xml (deflated 29%)
2024-11-01T18:09:07.0085697Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-2a694cb62e8f230c.xml (deflated 29%)
2024-11-01T18:09:07.0087005Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-7429ec4f67a44fc9.xml (deflated 29%)
2024-11-01T18:09:07.0088317Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-8ed554ec7695a2ab.xml (deflated 29%)
2024-11-01T18:09:07.0089624Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-505d0bb1f666a73d.xml (deflated 29%)
2024-11-01T18:09:07.0090907Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-044bf9209c52b2bc.xml (deflated 57%)
2024-11-01T18:09:07.0092203Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-05bc9f463dd47028.xml (deflated 73%)
2024-11-01T18:09:07.0093507Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-5137ff296c50d852.xml (deflated 83%)
2024-11-01T18:09:07.0094794Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-724958a0935ea287.xml (deflated 67%)
2024-11-01T18:09:07.0096085Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-b5263561bbe059e8.xml (deflated 79%)
2024-11-01T18:09:07.0115766Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-b421538301543e74.xml (deflated 37%)
2024-11-01T18:09:07.0117452Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-5d981ee5247c3c2b.xml (deflated 60%)
2024-11-01T18:09:07.0118766Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-698eca15b78bfdb8.xml (deflated 79%)
2024-11-01T18:09:07.0120253Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-dba9d84bed476d4d.xml (deflated 50%)
2024-11-01T18:09:07.0121982Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-16b3494269da3f97.xml (deflated 35%)
2024-11-01T18:09:07.0123284Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-b853361d76a8d113.xml (deflated 46%)
2024-11-01T18:09:07.0124560Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-724184fd4f5a207d.xml (deflated 46%)
2024-11-01T18:09:07.0125847Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-047afa422507e269.xml (deflated 83%)
2024-11-01T18:09:07.0127374Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-414bbf106f2cc142.xml (deflated 58%)
2024-11-01T18:09:07.0128656Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-e8161910b7136b8d.xml (deflated 58%)
2024-11-01T18:09:07.0130353Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-1273e15d90575f07.xml (deflated 59%)
2024-11-01T18:09:07.0132582Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-50c4d6995db9540b.xml (deflated 37%)
2024-11-01T18:09:07.0133897Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-5dcb8e5725cc48e6.xml (deflated 37%)
2024-11-01T18:09:07.0135190Z   adding: test/test-reports/python-pytest/test.run_test/test.run_test-0711e19c2302382c.xml (deflated 90%)
2024-11-01T18:09:07.0136650Z   adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241101175142.xml (deflated 43%)
2024-11-01T18:09:07.0138279Z   adding: test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20241101175155.xml (deflated 43%)
2024-11-01T18:09:07.0167504Z ##[group]Run # Remove any previous usage logs if they exist
2024-11-01T18:09:07.0168303Z # Remove any previous usage logs if they exist
2024-11-01T18:09:07.0168818Z rm -f logs-*.zip
2024-11-01T18:09:07.0169522Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2024-11-01T18:09:07.0170417Z # so check to see if the file exists first
2024-11-01T18:09:07.0171010Z if [ -f 'usage_log.txt' ]; then
2024-11-01T18:09:07.0171557Z     zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt'
2024-11-01T18:09:07.0172064Z fi
2024-11-01T18:09:07.0172590Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then
2024-11-01T18:09:07.0173414Z     zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log'
2024-11-01T18:09:07.0174003Z fi
2024-11-01T18:09:07.0181150Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:07.0181967Z env:
2024-11-01T18:09:07.0182367Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:07.0183475Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:07.0184347Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T18:09:07.0184894Z ##[endgroup]
2024-11-01T18:09:07.0286074Z   adding: usage_log.txt (deflated 92%)
2024-11-01T18:09:07.0383926Z   adding: test/test-reports/cpp.broadcast_test_1.1_6b2598db85c54889_.log (deflated 50%)
2024-11-01T18:09:07.0385363Z   adding: test/test-reports/test_cpp_extensions_open_device_registration_1.1_333c68a6522ca19a_.log (deflated 80%)
2024-11-01T18:09:07.0398124Z   adding: test/test-reports/test_cpp_api_parity_1.1_e21b79fb5a78cf65_.log (deflated 94%)
2024-11-01T18:09:07.0399822Z   adding: test/test-reports/test_multiprocessing_spawn_1.1_4bb4a48772ed07e3_.log (deflated 82%)
2024-11-01T18:09:07.0400964Z   adding: test/test-reports/test_autocast_1.1_8dc95c71f147c8df_.log (deflated 76%)
2024-11-01T18:09:07.0402838Z   adding: test/test-reports/test_cpp_extensions_jit_1.1_822b78bbea6995cc_.log (deflated 88%)
2024-11-01T18:09:07.0404377Z   adding: test/test-reports/test_multiprocessing_1.1_fcccfe043cbf4d0b_.log (deflated 84%)
2024-11-01T18:09:07.0405695Z   adding: test/test-reports/test_native_mha_1.1_1ee7d32922407e43_.log (deflated 90%)
2024-11-01T18:09:07.0413434Z   adding: test/test-reports/test_sort_and_select_1.1_79eecb1ce35f7db5_.log (deflated 93%)
2024-11-01T18:09:07.0418108Z   adding: test/test-reports/nn.test_pooling_1.1_a2335df675fd1fdb_.log (deflated 88%)
2024-11-01T18:09:07.0442468Z   adding: test/test-reports/test_tensor_creation_ops_1.1_bfc47e8b5423cdb1_.log (deflated 93%)
2024-11-01T18:09:07.0443607Z   adding: test/test-reports/test_mobile_optimizer_1.1_08ff60c25316dcce_.log (deflated 67%)
2024-11-01T18:09:07.0480529Z   adding: test/test-reports/test_nn_1.2_d224967e91d1399f_.log (deflated 93%)
2024-11-01T18:09:07.0488482Z   adding: test/test-reports/test_spectral_ops_1.1_48473e46d54742d4_.log (deflated 92%)
2024-11-01T18:09:07.0489639Z   adding: test/test-reports/cpp.undefined_tensor_test_1.1_5d7c76fc7f3fda25_.log (deflated 50%)
2024-11-01T18:09:07.0492929Z   adding: test/test-reports/distributions.test_distributions_1.2_9ce848439eddd43a_.log (deflated 90%)
2024-11-01T18:09:07.0494108Z   adding: test/test-reports/cpp.wrapdim_test_1.1_c35c0c6bafc08019_.log (deflated 49%)
2024-11-01T18:09:07.0496520Z   adding: test/test-reports/distributions.test_distributions_2.2_47bbaa3120ab56c0_.log (deflated 89%)
2024-11-01T18:09:07.0498006Z   adding: test/test-reports/test_cpp_extensions_aot_ninja_1.1_3a04cb86d7225945_.log (deflated 75%)
2024-11-01T18:09:07.0499313Z   adding: test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_a68d723780563470_.log (deflated 76%)
2024-11-01T18:09:07.0500682Z   adding: test/test-reports/test_namedtuple_return_api_1.1_e7436ab579af59c1_.log (deflated 63%)
2024-11-01T18:09:07.0501962Z   adding: test/test-reports/test_autograd_fallback_1.1_4959cd20e4031c44_.log (deflated 84%)
2024-11-01T18:09:07.0503128Z   adding: test/test-reports/test_jit_disabled_1.1_d17db7187ee6504d_.log (deflated 57%)
2024-11-01T18:09:07.0504625Z   adding: test/test-reports/test_cpp_extensions_mtia_backend_1.1_40609143dc0bf7e4_.log (deflated 69%)
2024-11-01T18:09:07.0505863Z   adding: test/test-reports/cpp.broadcast_test_1.1_ce47bbe7985f586c_.log (deflated 48%)
2024-11-01T18:09:07.0507796Z   adding: test/test-reports/test_cpp_extensions_stream_and_event_1.1_49b18495c7022ebf_.log (deflated 61%)
2024-11-01T18:09:07.0509079Z   adding: test/test-reports/test_tensorexpr_1.1_b13b6adc7226750b_.log (deflated 89%)
2024-11-01T18:09:07.0510289Z   adding: test/test-reports/test_cuda_trace_1.1_958069b087edbee5_.log (deflated 11%)
2024-11-01T18:09:07.0511360Z   adding: test/test-reports/test_cuda_primary_ctx_1.1_291d02597d20d75d_.log (deflated 11%)
2024-11-01T18:09:07.0512466Z   adding: test/test-reports/test_python_dispatch_1.1_d6d46100cfacbff4_.log (deflated 84%)
2024-11-01T18:09:07.0513609Z   adding: test/test-reports/test_cuda_nvml_based_avail_1.1_e6bcba5725a6c58a_.log (deflated 11%)
2024-11-01T18:09:07.0566471Z   adding: test/test-reports/test_reductions_1.2_51149d8fa8105d36_.log (deflated 95%)
2024-11-01T18:09:07.0619575Z   adding: test/test-reports/test_reductions_2.2_318c8c82f6ef48af_.log (deflated 95%)
2024-11-01T18:09:07.0650971Z   adding: test/test-reports/test_overrides_1.1_1dee04e21e67b1bf_.log (deflated 92%)
2024-11-01T18:09:07.0652438Z   adding: test/test-reports/dynamo.test_dynamic_shapes_1.1_644550c975865836_.log (stored 0%)
2024-11-01T18:09:07.0653758Z   adding: test/test-reports/dynamo.test_config_1.1_a02dfdbbf4609c9d_.log (stored 0%)
2024-11-01T18:09:07.0654937Z   adding: test/test-reports/dynamo.test_interop_1.1_f7e47bc584599b7b_.log (stored 0%)
2024-11-01T18:09:07.0656075Z   adding: test/test-reports/dynamo.test_after_aot_1.1_b2be89674ad9940c_.log (stored 0%)
2024-11-01T18:09:07.0657311Z   adding: test/test-reports/dynamo.test_export_mutations_1.1_0e3bdf4643cb7642_.log (stored 0%)
2024-11-01T18:09:07.0658556Z   adding: test/test-reports/dynamo.test_misc_1.1_c73d52cd65f7a842_.log (stored 0%)
2024-11-01T18:09:07.0659670Z   adding: test/test-reports/dynamo.test_export_1.1_18821a3afa81d9b4_.log (stored 0%)
2024-11-01T18:09:07.0660778Z   adding: test/test-reports/dynamo.test_modules_1.1_d112e1df4d670da6_.log (stored 0%)
2024-11-01T18:09:07.0662059Z   adding: test/test-reports/dynamo.test_verify_correctness_1.1_7630bdd28c682b36_.log (stored 0%)
2024-11-01T18:09:07.0663310Z   adding: test/test-reports/dynamo.test_higher_order_ops_1.1_1bd238c29a570549_.log (stored 0%)
2024-11-01T18:09:07.0664453Z   adding: test/test-reports/dynamo.test_exc_1.1_0a5cd04876624cb5_.log (stored 0%)
2024-11-01T18:09:07.0665722Z   adding: test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_83347a8f1705555b_.log (stored 0%)
2024-11-01T18:09:07.0666891Z   adding: test/test-reports/dynamo.test_utils_1.1_6d9e044f4c604bc7_.log (stored 0%)
2024-11-01T18:09:07.0668265Z   adding: test/test-reports/dynamo.test_sdpa_1.1_8f0f20a46993c272_.log (stored 0%)
2024-11-01T18:09:07.0669354Z   adding: test/test-reports/dynamo.test_view_1.1_8a757e978ffee5e1_.log (stored 0%)
2024-11-01T18:09:07.0670491Z   adding: test/test-reports/dynamo.test_profiler_1.1_b219a47ebf067313_.log (stored 0%)
2024-11-01T18:09:07.0671701Z   adding: test/test-reports/dynamo.test_deviceguard_1.1_43245597333b6b8f_.log (stored 0%)
2024-11-01T18:09:07.0672887Z   adding: test/test-reports/dynamo.test_model_output_1.1_db95e63ce0edde20_.log (stored 0%)
2024-11-01T18:09:07.0674121Z   adding: test/test-reports/cpp.cpu_generator_test_1.1_d9d65dff54f62ae0_.log (deflated 48%)
2024-11-01T18:09:07.0675366Z   adding: test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_adcd1d951466b710_.log (stored 0%)
2024-11-01T18:09:07.0676585Z   adding: test/test-reports/dynamo.test_bytecode_utils_1.1_b4bfd2b61efd2e47_.log (stored 0%)
2024-11-01T18:09:07.0677739Z   adding: test/test-reports/test_model_exports_to_core_aten_1.1_d6f8e39b5e988d74_.log (deflated 51%)
2024-11-01T18:09:07.0678850Z   adding: test/test-reports/test_namedtensor_1.1_44c9e058dc41e6a6_.log (deflated 49%)
2024-11-01T18:09:07.0680163Z   adding: test/test-reports/cpp.dlconvertor_test_1.1_7e9b9517603c5727_.log (deflated 48%)
2024-11-01T18:09:07.0681374Z   adding: test/test-reports/higher_order_ops.test_invoke_subgraph_1.1_3dddb2d9cf1aaafb_.log (deflated 52%)
2024-11-01T18:09:07.0682607Z   adding: test/test-reports/cpp.extension_backend_test_1.1_aa2d3ed83098c28b_.log (deflated 48%)
2024-11-01T18:09:07.0683935Z   adding: test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_3c9e05d83c9add4e_.log (deflated 52%)
2024-11-01T18:09:07.0685115Z   adding: test/test-reports/test_cuda_sanitizer_1.1_cc43a6b3a6b66341_.log (deflated 49%)
2024-11-01T18:09:07.0686184Z   adding: test/test-reports/cpp.lazy_tensor_test_1.1_0befa1b96e445a42_.log (deflated 48%)
2024-11-01T18:09:07.0687363Z   adding: test/test-reports/dynamo.test_backward_higher_order_ops_1.1_01e7e561f34e203d_.log (stored 0%)
2024-11-01T18:09:07.0688484Z   adding: test/test-reports/test_fx_passes_1.1_dfd9034756e46940_.log (deflated 61%)
2024-11-01T18:09:07.0689540Z   adding: test/test-reports/dynamo.test_trace_rules_1.1_5673cf113470352d_.log (stored 0%)
2024-11-01T18:09:07.0690988Z   adding: test/test-reports/distributions.test_constraints_1.1_22d4ad73b4772b5e_.log (deflated 52%)
2024-11-01T18:09:07.0692218Z   adding: test/test-reports/test_fx_reinplace_pass_1.1_02245ad1f81ac16f_.log (deflated 50%)
2024-11-01T18:09:07.0693628Z   adding: test/test-reports/higher_order_ops.test_with_effects_1.1_db9fce67dd4a49b8_.log (deflated 52%)
2024-11-01T18:09:07.0694998Z   adding: test/test-reports/cpp.legacy_vmap_test_1.1_539beb997fbd489a_.log (deflated 48%)
2024-11-01T18:09:07.0696444Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_0fc9610f3586d480_.log (deflated 53%)
2024-11-01T18:09:07.0697779Z   adding: test/test-reports/cpp.native_test_1.1_5cfa3b80209dcbe7_.log (deflated 48%)
2024-11-01T18:09:07.0699071Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_1451bae8edc16be8_.log (deflated 52%)
2024-11-01T18:09:07.0700294Z   adding: test/test-reports/dynamo.test_recompile_ux_1.1_2bfa1fc048066ea7_.log (stored 0%)
2024-11-01T18:09:07.0701641Z   adding: test/test-reports/cpp.operators_test_1.1_9ab70bdbf916ac12_.log (deflated 48%)
2024-11-01T18:09:07.0702862Z   adding: test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_6a58abcf69c2d912_.log (deflated 52%)
2024-11-01T18:09:07.0704035Z   adding: test/test-reports/cpp.operators_test_1.1_3b9c81d71fafc399_.log (deflated 60%)
2024-11-01T18:09:07.0705273Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_694e5bf70be670da_.log (deflated 53%)
2024-11-01T18:09:07.0706842Z   adding: test/test-reports/test_legacy_vmap_1.1_d6d3c96427965199_.log (deflated 49%)
2024-11-01T18:09:07.0708071Z   adding: test/test-reports/dynamo.test_hooks_1.1_88060e1a7da33e9c_.log (stored 0%)
2024-11-01T18:09:07.0709339Z   adding: test/test-reports/cpp.cpu_generator_test_1.1_974f3d5cfdb056be_.log (deflated 78%)
2024-11-01T18:09:07.0710755Z   adding: test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_a3ef93eb0891d207_.log (deflated 53%)
2024-11-01T18:09:07.0712123Z   adding: test/test-reports/cpp.scalar_tensor_test_1.1_dca0e2677c4e68b3_.log (deflated 60%)
2024-11-01T18:09:07.0713354Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_ef1f9ae00107b612_.log (deflated 53%)
2024-11-01T18:09:07.0714666Z   adding: test/test-reports/test_cuda_multigpu_1.1_11e846f664f1dc32_.log (deflated 49%)
2024-11-01T18:09:07.0715880Z   adding: test/test-reports/profiler.test_profiler_tree_1.1_e21832d15e757e50_.log (deflated 52%)
2024-11-01T18:09:07.0717013Z   adding: test/test-reports/cpp.legacy_vmap_test_1.1_a24feb59c47ab93a_.log (deflated 81%)
2024-11-01T18:09:07.0718381Z   adding: test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_245c4edfe5557cfc_.log (deflated 52%)
2024-11-01T18:09:07.0719711Z   adding: test/test-reports/torch_np.test_scalars_0D_arrays_1.1_f0f9103f04f86261_.log (deflated 51%)
2024-11-01T18:09:07.0720921Z   adding: test/test-reports/profiler.test_memory_profiler_1.1_6e3993150becfdf8_.log (deflated 52%)
2024-11-01T18:09:07.0722290Z   adding: test/test-reports/cpp.lazy_tensor_test_1.1_1895295dbbb5fefd_.log (deflated 54%)
2024-11-01T18:09:07.0723537Z   adding: test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_851e81f08eac54ff_.log (deflated 53%)
2024-11-01T18:09:07.0724808Z   adding: test/test-reports/cpp.native_test_1.1_fa05e8ccdb670b0c_.log (deflated 53%)
2024-11-01T18:09:07.0726040Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_3966850fb2ea7725_.log (deflated 52%)
2024-11-01T18:09:07.0727193Z   adding: test/test-reports/test_dataloader_1.1_1a8033082f3785f7_.log (deflated 69%)
2024-11-01T18:09:07.0728545Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_function_base_1.1_94abc71b62af76cd_.log (deflated 92%)
2024-11-01T18:09:07.0729783Z   adding: test/test-reports/cpp.basic_1.1_fdaafaac2d0a0063_.log (deflated 48%)
2024-11-01T18:09:07.0730799Z   adding: test/test-reports/dynamo.test_interop_1.1_dc6d79e692c45197_.log (stored 0%)
2024-11-01T18:09:07.0731824Z   adding: test/test-reports/dynamo.test_config_1.1_eb8c41a9484cfd77_.log (stored 0%)
2024-11-01T18:09:07.0732993Z   adding: test/test-reports/dynamo.test_dynamic_shapes_1.1_62d3222b75545f00_.log (stored 0%)
2024-11-01T18:09:07.0734261Z   adding: test/test-reports/torch_np.numpy_tests.core.test_numerictypes_1.1_b21909574b20f26c_.log (deflated 86%)
2024-11-01T18:09:07.0735618Z   adding: test/test-reports/cpp.NamedTensor_test_1.1_7245c9a47a6ab667_.log (deflated 48%)
2024-11-01T18:09:07.0736778Z   adding: test/test-reports/dynamo.test_after_aot_1.1_3252f3d956fda34f_.log (stored 0%)
2024-11-01T18:09:07.0737882Z   adding: test/test-reports/dynamo.test_export_mutations_1.1_74029d9841f266e5_.log (stored 0%)
2024-11-01T18:09:07.0738918Z   adding: test/test-reports/dynamo.test_misc_1.1_1ba3719d6da5fda1_.log (stored 0%)
2024-11-01T18:09:07.0739939Z   adding: test/test-reports/test_cuda_multigpu_1.1_6df3e937220d3052_.log (deflated 49%)
2024-11-01T18:09:07.0740957Z   adding: test/test-reports/cpp.Dict_test_1.1_d4068767114b2d3a_.log (deflated 48%)
2024-11-01T18:09:07.0741976Z   adding: test/test-reports/dynamo.test_export_1.1_fa3e7a70975dd18e_.log (stored 0%)
2024-11-01T18:09:07.0743063Z   adding: test/test-reports/dynamo.test_modules_1.1_480f5eb9feda628e_.log (stored 0%)
2024-11-01T18:09:07.0744165Z   adding: test/test-reports/dynamo.test_verify_correctness_1.1_c0f4943a347bf11a_.log (stored 0%)
2024-11-01T18:09:07.0745264Z   adding: test/test-reports/dynamo.test_hooks_1.1_42e9f6bfa4794b73_.log (stored 0%)
2024-11-01T18:09:07.0746529Z   adding: test/test-reports/torch_np.numpy_tests.fft.test_helper_1.1_826ad725a2705f38_.log (deflated 75%)
2024-11-01T18:09:07.0747749Z   adding: test/test-reports/dynamo.test_higher_order_ops_1.1_bdcde1b03d966990_.log (stored 0%)
2024-11-01T18:09:07.0748806Z   adding: test/test-reports/dynamo.test_exc_1.1_a455295925d55e8b_.log (stored 0%)
2024-11-01T18:09:07.0750138Z   adding: test/test-reports/dynamo.test_fx_passes_pre_grad_1.1_7429d6c83c02abe3_.log (stored 0%)
2024-11-01T18:09:07.0751378Z   adding: test/test-reports/profiler.test_profiler_tree_1.1_b44812720d5a6940_.log (deflated 81%)
2024-11-01T18:09:07.0752513Z   adding: test/test-reports/cpp.apply_utils_test_1.1_4b3ade7e1be5dcc0_.log (deflated 48%)
2024-11-01T18:09:07.0753549Z   adding: test/test-reports/dynamo.test_utils_1.1_8a37e00b575a0b45_.log (stored 0%)
2024-11-01T18:09:07.0754712Z   adding: test/test-reports/dynamo.test_sdpa_1.1_5383843b9137c19e_.log (stored 0%)
2024-11-01T18:09:07.0755709Z   adding: test/test-reports/dynamo.test_view_1.1_9d7d9fb7ae3b6c12_.log (stored 0%)
2024-11-01T18:09:07.0756987Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_arraysetops_1.1_ac9791ae89f6e264_.log (deflated 86%)
2024-11-01T18:09:07.0758318Z   adding: test/test-reports/cpp.Dimname_test_1.1_36316fe48486d0ee_.log (deflated 48%)
2024-11-01T18:09:07.0759454Z   adding: test/test-reports/dynamo.test_profiler_1.1_6e2b32e36d71abe4_.log (stored 0%)
2024-11-01T18:09:07.0760661Z   adding: test/test-reports/dynamo.test_deviceguard_1.1_feb50e1e6376e79c_.log (stored 0%)
2024-11-01T18:09:07.0762050Z   adding: test/test-reports/dynamo.test_model_output_1.1_51b7ed3a83f3ff35_.log (stored 0%)
2024-11-01T18:09:07.0763300Z   adding: test/test-reports/torch_np.numpy_tests.core.test_indexing_1.1_e413adc322cda8b0_.log (deflated 85%)
2024-11-01T18:09:07.0764824Z   adding: test/test-reports/profiler.test_memory_profiler_1.1_327929cae280b01b_.log (deflated 87%)
2024-11-01T18:09:07.0766094Z   adding: test/test-reports/dynamo.test_cudagraphs_expandable_segments_1.1_4aa278471685c744_.log (stored 0%)
2024-11-01T18:09:07.0767301Z   adding: test/test-reports/dynamo.test_bytecode_utils_1.1_4765fe43d6ace71d_.log (stored 0%)
2024-11-01T18:09:07.0768458Z   adding: test/test-reports/test_model_exports_to_core_aten_1.1_4fa52d9ffad64a17_.log (deflated 52%)
2024-11-01T18:09:07.0769664Z   adding: test/test-reports/torch_np.test_scalars_0D_arrays_1.1_31e09c3406c61153_.log (deflated 81%)
2024-11-01T18:09:07.0771681Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_arraypad_1.1_e4a278c91457ac4f_.log (deflated 72%)
2024-11-01T18:09:07.0773101Z   adding: test/test-reports/higher_order_ops.test_invoke_subgraph_1.1_4576481b33443eaf_.log (deflated 77%)
2024-11-01T18:09:07.0774296Z   adding: test/test-reports/test_cuda_sanitizer_1.1_2f4f4399b1285656_.log (deflated 49%)
2024-11-01T18:09:07.0775366Z   adding: test/test-reports/test_namedtensor_1.1_d91d3fd216cab141_.log (deflated 83%)
2024-11-01T18:09:07.0776588Z   adding: test/test-reports/torch_np.numpy_tests.core.test_scalar_ctors_1.1_2d537e1848021890_.log (deflated 90%)
2024-11-01T18:09:07.0777926Z   adding: test/test-reports/dynamo.test_backward_higher_order_ops_1.1_c31efdf6ac707ba0_.log (stored 0%)
2024-11-01T18:09:07.0779094Z   adding: test/test-reports/dynamo.test_trace_rules_1.1_e5f7c82b129025a7_.log (stored 0%)
2024-11-01T18:09:07.0780140Z   adding: test/test-reports/test_fx_passes_1.1_8ca683ab8dcea69e_.log (deflated 87%)
2024-11-01T18:09:07.0781198Z   adding: test/test-reports/test_legacy_vmap_1.1_5125bca44082409a_.log (deflated 91%)
2024-11-01T18:09:07.0782293Z   adding: test/test-reports/cpp.tensor_iterator_test_1.1_318d3eca38c05299_.log (deflated 88%)
2024-11-01T18:09:07.0783485Z   adding: test/test-reports/distributions.test_constraints_1.1_b63b931d18739096_.log (deflated 92%)
2024-11-01T18:09:07.0784646Z   adding: test/test-reports/test_fx_reinplace_pass_1.1_a172072e0db705c6_.log (deflated 90%)
2024-11-01T18:09:07.0785665Z   adding: test/test-reports/cpp.atest_1.1_7c8987c83ab6f8bb_.log (deflated 49%)
2024-11-01T18:09:07.0786826Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_type_check_1.1_f4023aa3157d0967_.log (deflated 85%)
2024-11-01T18:09:07.0788128Z   adding: test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_6fa5c12d23a1cb2a_.log (deflated 88%)
2024-11-01T18:09:07.0789331Z   adding: test/test-reports/dynamo.test_recompile_ux_1.1_6d1f47e769567577_.log (stored 0%)
2024-11-01T18:09:07.0791042Z   adding: test/test-reports/torch_np.numpy_tests.lib.test_histograms_1.1_96e3d36f962f6400_.log (deflated 86%)
2024-11-01T18:09:07.0792226Z   adding: test/test-reports/cpp.scalar_test_1.1_774eaf744e28518e_.log (deflated 59%)
2024-11-01T18:09:07.0793398Z   adding: test/test-reports/higher_order_ops.test_with_effects_1.1_40501d4a6541ccfa_.log (deflated 80%)
2024-11-01T18:09:07.0794658Z   adding: test/test-reports/test_dataloader_1.1_612cf8a11c812d10_.log (deflated 86%)
2024-11-01T18:09:07.0795728Z   adding: test/test-reports/cpp.scalar_tensor_test_1.1_8a07939b856735d3_.log (deflated 48%)
2024-11-01T18:09:07.0796785Z   adding: test/test-reports/cpp.scalar_test_1.1_da406063e2029d1a_.log (deflated 48%)
2024-11-01T18:09:07.0797883Z   adding: test/test-reports/cpp.tensor_iterator_test_1.1_98bb8013e5c045b0_.log (deflated 48%)
2024-11-01T18:09:07.0799026Z   adding: test/test-reports/cpp.undefined_tensor_test_1.1_f1b418c43534b903_.log (deflated 48%)
2024-11-01T18:09:07.0800135Z   adding: test/test-reports/cpp.wrapdim_test_1.1_d4c6da4bf459f4ec_.log (deflated 48%)
2024-11-01T18:09:07.0801271Z   adding: test/test-reports/cpp.atest_1.1_9c18ad8a99d52f7b_.log (deflated 72%)
2024-11-01T18:09:07.0802337Z   adding: test/test-reports/cpp.extension_backend_test_1.1_992180e53dbfaf23_.log (deflated 50%)
2024-11-01T18:09:07.0803424Z   adding: test/test-reports/cpp.Dimname_test_1.1_d7f190929e759e7d_.log (deflated 59%)
2024-11-01T18:09:07.0804525Z   adding: test/test-reports/cpp.NamedTensor_test_1.1_831a45ca519e066d_.log (deflated 71%)
2024-11-01T18:09:07.0805558Z   adding: test/test-reports/cpp.basic_1.1_5115c17f24dbc94f_.log (deflated 61%)
2024-11-01T18:09:07.0806971Z   adding: test/test-reports/cpp.Dict_test_1.1_17658e5783df7d34_.log (deflated 83%)
2024-11-01T18:09:07.0808041Z   adding: test/test-reports/cpp.apply_utils_test_1.1_264d72064ac771ab_.log (deflated 65%)
2024-11-01T18:09:07.0809141Z   adding: test/test-reports/cpp.dlconvertor_test_1.1_be481891470f6e37_.log (deflated 56%)
2024-11-01T18:09:07.0858529Z ##[group]Run # Remove any previous debugging artifacts if they exist
2024-11-01T18:09:07.0859302Z # Remove any previous debugging artifacts if they exist
2024-11-01T18:09:07.0859891Z rm -f debug-*.zip
2024-11-01T18:09:07.0860279Z if [ -d 'test/debug' ]; then
2024-11-01T18:09:07.0860801Z   zip -r "debug-${FILE_SUFFIX}.zip" test/debug
2024-11-01T18:09:07.0861294Z fi
2024-11-01T18:09:07.0867585Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:07.0868095Z env:
2024-11-01T18:09:07.0868379Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:07.0869024Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:07.0869885Z   FILE_SUFFIX: test-dynamo_wrapped-1-3-linux.2xlarge_32396395154
2024-11-01T18:09:07.0870445Z ##[endgroup]
2024-11-01T18:09:07.0967263Z ##[group]Run seemethere/upload-artifact-s3@v5
2024-11-01T18:09:07.0967746Z with:
2024-11-01T18:09:07.0968021Z   s3-bucket: gha-artifacts
2024-11-01T18:09:07.0968479Z   s3-prefix: pytorch/pytorch/11632514903/1/artifact

2024-11-01T18:09:07.0968974Z   retention-days: 14
2024-11-01T18:09:07.0969334Z   if-no-files-found: warn
2024-11-01T18:09:07.0969696Z   path: test-jsons-*.zip
2024-11-01T18:09:07.0970043Z   name: artifact
2024-11-01T18:09:07.0970334Z   region: us-east-1
2024-11-01T18:09:07.0970638Z env:
2024-11-01T18:09:07.0970914Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:07.0971562Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:07.0972245Z ##[endgroup]
2024-11-01T18:09:07.4991376Z NOTE: s3-prefix specified, ignoring name parameter
2024-11-01T18:09:07.4992398Z With the provided path, there will be 1 file uploaded
2024-11-01T18:09:07.4993365Z Uploading to s3 prefix: pytorch/pytorch/11632514903/1/artifact
2024-11-01T18:09:07.6494785Z Starting upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_32396395154.zip
2024-11-01T18:09:07.7569729Z Finished upload of test-jsons-test-dynamo_wrapped-1-3-linux.2xlarge_32396395154.zip
2024-11-01T18:09:07.7780726Z ##[group]Run seemethere/upload-artifact-s3@v5
2024-11-01T18:09:07.7781207Z with:
2024-11-01T18:09:07.7781506Z   s3-bucket: gha-artifacts
2024-11-01T18:09:07.7781959Z   s3-prefix: pytorch/pytorch/11632514903/1/artifact

2024-11-01T18:09:07.7782442Z   retention-days: 14
2024-11-01T18:09:07.7782788Z   if-no-files-found: error
2024-11-01T18:09:07.7783170Z   path: test-reports-*.zip
2024-11-01T18:09:07.7783537Z   name: artifact
2024-11-01T18:09:07.7783849Z   region: us-east-1
2024-11-01T18:09:07.7784138Z env:
2024-11-01T18:09:07.7784418Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:07.7785061Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:07.7785753Z ##[endgroup]
2024-11-01T18:09:08.1514976Z NOTE: s3-prefix specified, ignoring name parameter
2024-11-01T18:09:08.1516077Z With the provided path, there will be 1 file uploaded
2024-11-01T18:09:08.1516753Z Uploading to s3 prefix: pytorch/pytorch/11632514903/1/artifact
2024-11-01T18:09:08.1557772Z Starting upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_32396395154.zip
2024-11-01T18:09:08.4267358Z Finished upload of test-reports-test-dynamo_wrapped-1-3-linux.2xlarge_32396395154.zip
2024-11-01T18:09:08.4469104Z ##[group]Run seemethere/upload-artifact-s3@v5
2024-11-01T18:09:08.4469585Z with:
2024-11-01T18:09:08.4469859Z   s3-bucket: gha-artifacts
2024-11-01T18:09:08.4470444Z   s3-prefix: pytorch/pytorch/11632514903/1/artifact

2024-11-01T18:09:08.4470943Z   retention-days: 14
2024-11-01T18:09:08.4471290Z   if-no-files-found: ignore
2024-11-01T18:09:08.4471667Z   path: logs-*.zip
2024-11-01T18:09:08.4471968Z   name: artifact
2024-11-01T18:09:08.4472283Z   region: us-east-1
2024-11-01T18:09:08.4472589Z env:
2024-11-01T18:09:08.4472866Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:08.4473510Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:08.4474380Z ##[endgroup]
2024-11-01T18:09:08.8204728Z NOTE: s3-prefix specified, ignoring name parameter
2024-11-01T18:09:08.8205498Z With the provided path, there will be 1 file uploaded
2024-11-01T18:09:08.8206182Z Uploading to s3 prefix: pytorch/pytorch/11632514903/1/artifact
2024-11-01T18:09:08.8247141Z Starting upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_32396395154.zip
2024-11-01T18:09:09.0472503Z Finished upload of logs-test-dynamo_wrapped-1-3-linux.2xlarge_32396395154.zip
2024-11-01T18:09:09.0673003Z ##[group]Run seemethere/upload-artifact-s3@v5
2024-11-01T18:09:09.0673479Z with:
2024-11-01T18:09:09.0673768Z   s3-bucket: gha-artifacts
2024-11-01T18:09:09.0674337Z   s3-prefix: pytorch/pytorch/11632514903/1/artifact

2024-11-01T18:09:09.0674841Z   retention-days: 14
2024-11-01T18:09:09.0675188Z   if-no-files-found: ignore
2024-11-01T18:09:09.0675561Z   path: debug-*.zip
2024-11-01T18:09:09.0675884Z   name: artifact
2024-11-01T18:09:09.0676183Z   region: us-east-1
2024-11-01T18:09:09.0676503Z env:
2024-11-01T18:09:09.0676779Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:09.0677420Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:09.0678110Z ##[endgroup]
2024-11-01T18:09:09.4381440Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded.
2024-11-01T18:09:09.4592971Z ##[group]Run # shellcheck disable=SC2156
2024-11-01T18:09:09.4593480Z # shellcheck disable=SC2156
2024-11-01T18:09:09.4594500Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2024-11-01T18:09:09.4601132Z shell: /usr/bin/bash -e {0}
2024-11-01T18:09:09.4601504Z env:
2024-11-01T18:09:09.4601790Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:09.4602434Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:09.4603165Z ##[endgroup]
2024-11-01T18:09:09.6977621Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2024-11-01T18:09:09.6978320Z with:
2024-11-01T18:09:09.6978564Z env:
2024-11-01T18:09:09.6978838Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:09.6979487Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:09.6980179Z ##[endgroup]
2024-11-01T18:09:09.7018558Z ##[group]Run set -eou pipefail
2024-11-01T18:09:09.7018994Z set -eou pipefail
2024-11-01T18:09:09.7019338Z 
2024-11-01T18:09:09.7019881Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2024-11-01T18:09:09.7020574Z for _ in $(seq 1440); do
2024-11-01T18:09:09.7021059Z     # Break if no ssh session exists anymore
2024-11-01T18:09:09.7021572Z     if [ "$(who)" = "" ]; then
2024-11-01T18:09:09.7021978Z       break
2024-11-01T18:09:09.7022293Z     fi
2024-11-01T18:09:09.7022642Z     echo "."
2024-11-01T18:09:09.7022972Z     sleep 5
2024-11-01T18:09:09.7023278Z done
2024-11-01T18:09:09.7029575Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:09.7030098Z env:
2024-11-01T18:09:09.7030374Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:09.7031010Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:09.7031698Z ##[endgroup]
2024-11-01T18:09:09.7057569Z Holding runner for 2 hours until all ssh sessions have logged out
2024-11-01T18:09:09.7137712Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2024-11-01T18:09:09.7138635Z # ignore expansion of "docker ps -q" since it could be empty
2024-11-01T18:09:09.7139266Z # shellcheck disable=SC2046
2024-11-01T18:09:09.7139752Z docker stop $(docker ps -q) || true
2024-11-01T18:09:09.7140260Z # Prune all of the docker images
2024-11-01T18:09:09.7140730Z docker system prune -af
2024-11-01T18:09:09.7146786Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:09.7147310Z env:
2024-11-01T18:09:09.7147598Z   GIT_DEFAULT_BRANCH: main
2024-11-01T18:09:09.7148244Z   DOCKER_CONTAINER_ID: be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:09.7148946Z ##[endgroup]
2024-11-01T18:09:10.3188721Z be73fc5f4173
2024-11-01T18:09:10.6860205Z Deleted Containers:
2024-11-01T18:09:10.6860923Z be73fc5f4173327a6e39e7680f3a8ce52446440c9ed21c01872060b133a7c16d
2024-11-01T18:09:10.6861390Z 
2024-11-01T18:09:13.8324067Z Deleted Images:
2024-11-01T18:09:13.8325590Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10:bdd298b12da59246147f016e0693ffd722419941
2024-11-01T18:09:13.8328169Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.12-clang10@sha256:84b16e894952e5aa6d467c4bb4368d576360bf0463a1b87add4ca805eae1be35
2024-11-01T18:09:13.8330988Z deleted: sha256:8ec759243a9a0c0eef32c1eac9ea7bf9cba4aae49fa39ea946910b2e949f2405
2024-11-01T18:09:13.8332695Z deleted: sha256:ba5bb3997254455c96c672ca4ecbdbc0694670ca615562f18d8c68afbe77db7e
2024-11-01T18:09:13.8334432Z deleted: sha256:2e69ae24be3d79fbdc3c769bc27fc3ee5b8b981ce32581b99508f453078bd34f
2024-11-01T18:09:13.8336162Z deleted: sha256:61cca83e6d62bc0fe5f32891f86de4646ba8b250a0b9773f22370b4a668dc718
2024-11-01T18:09:13.8337774Z deleted: sha256:8d678fc7986e27335e0a111517238fcfa0412d003c3aff45b872175c6e649eda
2024-11-01T18:09:13.8339472Z deleted: sha256:f548cc0d9eac1d9393dd2ff42f70658c987d29438a5e9af83517bda134f874b3
2024-11-01T18:09:13.8341100Z deleted: sha256:1f9cb17627482b3e1dbbd7671ff139cbf724da97ef3b17c495fd077375ddfd0d
2024-11-01T18:09:13.8342785Z deleted: sha256:03a965b77cc92136fcb74126cefaa2a9e8fd53f33f64a98b0c6717732e602368
2024-11-01T18:09:13.8344497Z deleted: sha256:d346cc54b383c0f2579883c19a2d87d63b2a5353338ead3a45e4f70d9e398d1e
2024-11-01T18:09:13.8346128Z deleted: sha256:7108e4ed14eda0c955962f7a174bcdc8558e4b649dd7d7566161a55220d2a80f
2024-11-01T18:09:13.8347043Z deleted: sha256:4a5b013954a6d0477175aaa13182b28329208acf2b5774a4299f3dfa68a55ad5
2024-11-01T18:09:13.8348667Z deleted: sha256:5497e246fe79a4d9d48f300980c6c1018de86b8eef345e76647cdf0f246c16b4
2024-11-01T18:09:13.8349590Z deleted: sha256:5005cd3a52d25d489c4f93a54fafa0ecf1a562c44ea0c023e861ee8d37bafb90
2024-11-01T18:09:13.8350535Z deleted: sha256:13b7bc0c40d31aebfa75bd9f6d4f4e3e1dfd38ecc7845c5f68af3a725aa956f5
2024-11-01T18:09:13.8351597Z deleted: sha256:0c3d3301b8e1282890755f42da004c787a2f5964c2bb8f87c498dd205c611028
2024-11-01T18:09:13.8352568Z deleted: sha256:aa682d287359b1af2eede180968228d2fb471d12be793d5cf265b6774614890c
2024-11-01T18:09:13.8353482Z deleted: sha256:a12e5e4bce3b872cb4a1c1971021c3278bf705424654e2a4782fc4077acb11fe
2024-11-01T18:09:13.8354619Z deleted: sha256:1251faefe6bee5dedb61e5235183fda0c5b8bd4173cc4ee215da66b1590e90ab
2024-11-01T18:09:13.8355558Z deleted: sha256:efe118032cb0f664ee64528fe0bb399f5b7723da0f5501510a2aaca08ee65737
2024-11-01T18:09:13.8356483Z deleted: sha256:84056b03c007c6efd22c108dce8b9807887db9be13501fd1785cafd754487fd5
2024-11-01T18:09:13.8357395Z deleted: sha256:b3103ddf3edd968c01745c3d585d4669e18cc593d0e37c130a7769c38d73479b
2024-11-01T18:09:13.8358287Z deleted: sha256:994d97e1fd2739318d6131155097558c871b3b3cd3a6840d47820070c6af5378
2024-11-01T18:09:13.8359182Z deleted: sha256:9b0c1ef062c54e055932dfed0b3fd684e9d1967d7526ea998d59a2514356098f
2024-11-01T18:09:13.8360088Z deleted: sha256:67694ed6276375afc560e3041db73b9e2ef2e1b550967ca185e9bc8bb87b5440
2024-11-01T18:09:13.8361004Z deleted: sha256:17c47bed4c32082300a39a08cdba98d5f071fa3f535d5ccc76ab42a2b3ad499b
2024-11-01T18:09:13.8362191Z deleted: sha256:7f25f7a0c136cf39326dd9d2cdbf744e4f77354831b3288a8a7e6421d09bdcc8
2024-11-01T18:09:13.8363164Z deleted: sha256:1fc76f347ba7e24848e090a831fcaf011648cac2e097b28a0e88c1cf2d8fafb8
2024-11-01T18:09:13.8364082Z deleted: sha256:e658d23fa462b813b7e9311d8bed68fb90422ba90a015a9512dbb53c5559b9c4
2024-11-01T18:09:13.8365006Z deleted: sha256:7399eb0f0c9a25958f74e94de0df2d243508b7b83ece8a29c5bedaf40ff9e4dc
2024-11-01T18:09:13.8365933Z deleted: sha256:cbbd2904524a15fa790fcba03400fc1fad879a2d75cf006852b5831d4c9215a9
2024-11-01T18:09:13.8366829Z deleted: sha256:367f7f6dfd5336274583395a6a8dd7881a35650f0cdb40ceb65c1c9240c3fc1a
2024-11-01T18:09:13.8367722Z deleted: sha256:037c476e7ac6671d224551dfa110f78437f61276a5074f5e3924cfe87607d1de
2024-11-01T18:09:13.8368639Z deleted: sha256:519d234bfed95a5c9e5aa12cb26148daba81dfbf21daa7ddf606faa0532bce34
2024-11-01T18:09:13.8369579Z deleted: sha256:731510409d0d95a375dfa8debab0bb19ae1f55bcf23de160d43ec01cd0d657f6
2024-11-01T18:09:13.8370520Z deleted: sha256:8ea3f3da8c927ecbf7e86a3e5545e5f7cc1772f7b970d3d9afe1131e5fc648e1
2024-11-01T18:09:13.8371453Z deleted: sha256:cf470774fcb628ecfab51bfc27f28745389de100a60c5755a0f7ffbd89373edc
2024-11-01T18:09:13.8372365Z deleted: sha256:88f10106ada360c09b470747b9a2ab49ffa7cc48ab38aa5aa36bac218b791c9b
2024-11-01T18:09:13.8373302Z deleted: sha256:e0b0b1e6febf9babeec85889492f0d52317a132062cd7c8ff6abcc487e57b0e4
2024-11-01T18:09:13.8374232Z deleted: sha256:eee8a422b5622ed4065a5c4997d293cc34488c2fec0f7073d541f5a8ba650b2c
2024-11-01T18:09:13.8375125Z deleted: sha256:a048d7d68b28330969994997964320af690df3294edb76781faa59278276d649
2024-11-01T18:09:13.8376300Z deleted: sha256:bfa3760bc99d11feead0f7a6de2e0f850fe459f72be5d88ded1a7b95758acf4b
2024-11-01T18:09:13.8377695Z deleted: sha256:9ee643a7a6f7dd338953f5990fb57375251ffbb22d4ecedc650dad50243cd0f6
2024-11-01T18:09:13.8378747Z deleted: sha256:7e86ad55989d98e4e4961ce5817565cf7a47a047d52dd4d004ee21c3bbebaf26
2024-11-01T18:09:13.8379673Z deleted: sha256:2e7b0f63aa872fe3c88311a261e521004e19cd68de662cc884fd0047ace58758
2024-11-01T18:09:13.8380593Z deleted: sha256:6875ea901374eab7ebc79b532cce78f709699b7ab4eec2283e8df1fa69ee71f6
2024-11-01T18:09:13.8381510Z deleted: sha256:4266bea5661711f3009f377c7c9518537c45e1b0a6eff8c33b16937ecba7e68b
2024-11-01T18:09:13.8382450Z deleted: sha256:34b2baf2b2a7bca84b326a8b89cac97adc2e1b7b20a4b6294d5da21966882418
2024-11-01T18:09:13.8383549Z deleted: sha256:431981c8deeb5379491697f66194a8f10968a4aa2b5d7ea08306ff85a36e5746
2024-11-01T18:09:13.8384535Z deleted: sha256:dd8a3a29a228cb6f71e4c05b054e3f1a69d910e3c392a55b9306b618a2652bd3
2024-11-01T18:09:13.8385433Z deleted: sha256:d827a1c291719ae4296743a11a9925c567c70c0d41a9713355f79e8249724d4a
2024-11-01T18:09:13.8386345Z deleted: sha256:96be4a079360eaa8febb7a8c7269d167fecfe949387beb65644dc0a012c7fe2b
2024-11-01T18:09:13.8387267Z deleted: sha256:ff2357152953978affbff8475b9a4e43c9d201d246be084d7dd94830b22b0e0e
2024-11-01T18:09:13.8388162Z deleted: sha256:8c83449b7a51fd71d82e54f19b363d67e1790424157d8a0158421e271978ed66
2024-11-01T18:09:13.8389054Z deleted: sha256:37ed5f078af170f9296d345a4440ab491b94296339a3fce59e37006d60dd73aa
2024-11-01T18:09:13.8389952Z deleted: sha256:0a555385499dcfcdd30f67c438e159a0636b303d4d0f6a201a74d254961b2e04
2024-11-01T18:09:13.8390849Z deleted: sha256:4626ff7a212e1a4f7b51634a363bb332aebdbceaf3ad052b6f48361fb8b90a6d
2024-11-01T18:09:13.8392062Z deleted: sha256:1e7c2715df828da940212dd3757d5c3c37066c8f27ce8b02a159e00afc021e63
2024-11-01T18:09:13.8393293Z deleted: sha256:185c24c22dc31bb5dfe8aeb5985059f5d2c44ee862613471c31ed0edce677a77
2024-11-01T18:09:13.8394330Z deleted: sha256:aea888011788319bc5517cc142cbc81b18212a8d6bd8e11d203cf4d40ce00675
2024-11-01T18:09:13.8395233Z deleted: sha256:d142cc05db42f0fe909925b6705f107644428479d851f5479e5d2efe1e75cddf
2024-11-01T18:09:13.8396135Z deleted: sha256:b1dc3c38b6ce3b5d3478353b162c1651a2803169e82aefea0e1a68e7672bc1c7
2024-11-01T18:09:13.8397045Z deleted: sha256:e5b7a8d544111b939cba9254f36e48c441e27147d33a3734bb753c8d6026b680
2024-11-01T18:09:13.8398022Z deleted: sha256:23f9b0feb029466ccba215044d81d803083290fcf329d8c874da0cb88ff95f61
2024-11-01T18:09:13.8398916Z deleted: sha256:d9a3c7147934575e7ea6e543c833eb10d8fec09a1596cb9e5871b25103c54163
2024-11-01T18:09:13.8399820Z deleted: sha256:9186eb287b46fe77aac05c60d56de44b4f18c4509bc49aa2533e6538749b4c8a
2024-11-01T18:09:13.8400745Z deleted: sha256:13f0d3eace3fa5a8254139509a528a03f14b848ad40838212269cac3e859b071
2024-11-01T18:09:13.8401662Z deleted: sha256:72c31591d2c47b2acac8c79f806ecd958376a9cc405be913dc6cf45bd388703b
2024-11-01T18:09:13.8402585Z deleted: sha256:cba63461a38fe7c90a95f610b7f4fc4329090bb44e734b4676c7f04a1275e51f
2024-11-01T18:09:13.8403489Z deleted: sha256:e7f861191383033cefa3b04fb9dc63021e183c93e3d628af3cb8ac85bf3d89c9
2024-11-01T18:09:13.8404396Z deleted: sha256:f2695eafd6b96ca79d5c02f44dd0862c626f4b6ff7f8c2e1f2376a7def6ee985
2024-11-01T18:09:13.8405306Z deleted: sha256:19f490f5176f0743c170d16be777d2670c24cd27e72985b6b73c7679c0bca425
2024-11-01T18:09:13.8406228Z deleted: sha256:e686f151b8adae222b77be2e1bfb6abc3fe7b04c8d1eae4dbb811ff9a7ac16f3
2024-11-01T18:09:13.8407403Z deleted: sha256:88183f6c73966866a27dbab7da8160d31dba442ea055942b47af0d1c3b780a32
2024-11-01T18:09:13.8408317Z deleted: sha256:158f6b5433e9ee890ddab99d33acc7f9ee97897ab4883e4c3025e0f41e31f261
2024-11-01T18:09:13.8409235Z deleted: sha256:a7b392ff64ec7f8c2e100259fd8e64fd991a18e539e0b8528f66892c2a489235
2024-11-01T18:09:13.8410147Z deleted: sha256:e6e74fd544378febd777d40de02099af908cd5e481f801ac0f47ffad756074eb
2024-11-01T18:09:13.8411081Z deleted: sha256:4cf33bc79f65595414e3f115a72aaea45dab54bfdaac4abbde4117d24eaaf326
2024-11-01T18:09:13.8412002Z deleted: sha256:6b337b468391a3c368c03930c3de929ba3cfd54af475748b39ba7b444254e9c9
2024-11-01T18:09:13.8412926Z deleted: sha256:7cc97c86baa0242b8ecb3d4b165772e337ed6fa2df46477eeef71bb39be33055
2024-11-01T18:09:13.8413869Z deleted: sha256:b87bd7cb76d91d9b1a1a8b3fa0859adab6717c1f4707590bebbae48e2849154f
2024-11-01T18:09:13.8414797Z deleted: sha256:73cb804059370d38ed347f81049f17f5f4e28cdb8497e9e9ce0f5fcb2529b5cb
2024-11-01T18:09:13.8415714Z deleted: sha256:5aa533cd6c39f1e1dddffebc0f925d85b60e321035357f35c483595b20aba3bc
2024-11-01T18:09:13.8416628Z deleted: sha256:f720611009fee7112c5755a8b4a9b5c8e8e6e2f03840ae73169df0267e7d3bd1
2024-11-01T18:09:13.8417546Z deleted: sha256:3822bec2c7a32311a56befe068e4b19a32c0d23a2bc2e60c6339bf1d00706ab0
2024-11-01T18:09:13.8418617Z deleted: sha256:01ca2e2550b535b6413937f111516573de4313250a7fec557a3005ff5041e653
2024-11-01T18:09:13.8419578Z deleted: sha256:a849a7039a2e5d2e88d1eaead074e81aa42bcd67128a7fc7e21d84cc18503a0f
2024-11-01T18:09:13.8420506Z deleted: sha256:65bde89d71cf444573b7210ed51dd246592c5cd7fbdb77f25a5acce43ffb9f8b
2024-11-01T18:09:13.8421962Z deleted: sha256:5aa781cf762607301094ced32d72596d9171a3b9355cf09be94c4a9159e0b82f
2024-11-01T18:09:13.8422865Z deleted: sha256:e3be9975f8bdaf8376fd04b77d2e5a29d7e1d72baa7119d527a77c13c5abb4e6
2024-11-01T18:09:13.8423795Z deleted: sha256:183a322c6facb66de851e27d60616de3138cb4aa97d7d1ccdce5b35b1cd41c2e
2024-11-01T18:09:13.8424717Z deleted: sha256:724362d48ebe450259741d5118b20b1e03464d0fcfdec4c2f0ef9699b6d15d19
2024-11-01T18:09:13.8425633Z deleted: sha256:8b65e8c2b459d8b20c82aabd786eaeb5582e6f67858857da9e7b9c304802d472
2024-11-01T18:09:13.8426550Z deleted: sha256:378d3da5b57d7943301715df239fa657b4c17232f03d0af85b8d42492b5f2aa1
2024-11-01T18:09:13.8427474Z deleted: sha256:4bab21e160e0b9ab08c5a531ea95c7a61913efa1d0ef30f8c50337d8825f0aac
2024-11-01T18:09:13.8428381Z deleted: sha256:63679009d31fa8e3d1485776b985e23e7ce5c04ed6515e2c43318e5423a28fe3
2024-11-01T18:09:13.8429289Z deleted: sha256:05a5041d23d580e82f9d34dba9eddb073ec5cf22ab052bed0009e5de7e04d26f
2024-11-01T18:09:13.8430203Z deleted: sha256:a2016795cb3b68378a442bf9beca069a9ac164167036dc41a42d5230e1c20beb
2024-11-01T18:09:13.8431132Z deleted: sha256:fffe76c64ef2dee2d80a8bb3ad13d65d596d04a45510b1956a976a69215dae92
2024-11-01T18:09:13.8431690Z 
2024-11-01T18:09:13.8431873Z Total reclaimed space: 10.32GB
2024-11-01T18:09:13.8521372Z Post job cleanup.
2024-11-01T18:09:13.8589688Z Post job cleanup.
2024-11-01T18:09:13.9549590Z [command]/usr/bin/git version
2024-11-01T18:09:13.9595357Z git version 2.40.1
2024-11-01T18:09:13.9638588Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/8a478f24-9d56-4ce8-8e24-91b2afcf9ae7' before making global git config changes
2024-11-01T18:09:13.9639977Z Adding repository directory to the temporary git global config as a safe directory
2024-11-01T18:09:13.9643995Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2024-11-01T18:09:13.9690609Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2024-11-01T18:09:13.9734797Z [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' || :"
2024-11-01T18:09:14.0073617Z Entering 'android/libs/fbjni'
2024-11-01T18:09:14.0130901Z Entering 'third_party/FP16'
2024-11-01T18:09:14.0184673Z Entering 'third_party/FXdiv'
2024-11-01T18:09:14.0238994Z Entering 'third_party/NNPACK'
2024-11-01T18:09:14.0292441Z Entering 'third_party/NVTX'
2024-11-01T18:09:14.0350402Z Entering 'third_party/VulkanMemoryAllocator'
2024-11-01T18:09:14.0410305Z Entering 'third_party/XNNPACK'
2024-11-01T18:09:14.0495290Z Entering 'third_party/benchmark'
2024-11-01T18:09:14.0555634Z Entering 'third_party/composable_kernel'
2024-11-01T18:09:14.0622918Z Entering 'third_party/cpp-httplib'
2024-11-01T18:09:14.0685948Z Entering 'third_party/cpuinfo'
2024-11-01T18:09:14.0746688Z Entering 'third_party/cudnn_frontend'
2024-11-01T18:09:14.0806247Z Entering 'third_party/cutlass'
2024-11-01T18:09:14.0877489Z Entering 'third_party/eigen'
2024-11-01T18:09:14.0938166Z Entering 'third_party/fbgemm'
2024-11-01T18:09:14.0999250Z Entering 'third_party/fbgemm/third_party/asmjit'
2024-11-01T18:09:14.1060772Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T18:09:14.1121640Z Entering 'third_party/fbgemm/third_party/cutlass'
2024-11-01T18:09:14.1183019Z Entering 'third_party/fbgemm/third_party/googletest'
2024-11-01T18:09:14.1235790Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T18:09:14.1288670Z Entering 'third_party/flatbuffers'
2024-11-01T18:09:14.1347361Z Entering 'third_party/fmt'
2024-11-01T18:09:14.1401770Z Entering 'third_party/gemmlowp/gemmlowp'
2024-11-01T18:09:14.1455107Z Entering 'third_party/gloo'
2024-11-01T18:09:14.1507331Z Entering 'third_party/googletest'
2024-11-01T18:09:14.1559978Z Entering 'third_party/ideep'
2024-11-01T18:09:14.1612159Z Entering 'third_party/ideep/mkl-dnn'
2024-11-01T18:09:14.1672096Z Entering 'third_party/ittapi'
2024-11-01T18:09:14.1731492Z Entering 'third_party/kineto'
2024-11-01T18:09:14.1784624Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T18:09:14.1838143Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T18:09:14.1893666Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T18:09:14.1953267Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T18:09:14.2015179Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T18:09:14.2072736Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T18:09:14.2130287Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T18:09:14.2183178Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T18:09:14.2236105Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T18:09:14.2290075Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T18:09:14.2347863Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T18:09:14.2402468Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T18:09:14.2460198Z Entering 'third_party/mimalloc'
2024-11-01T18:09:14.2520525Z Entering 'third_party/nccl/nccl'
2024-11-01T18:09:14.2582353Z Entering 'third_party/nlohmann'
2024-11-01T18:09:14.2647793Z Entering 'third_party/onnx'
2024-11-01T18:09:14.2728292Z Entering 'third_party/onnx/third_party/pybind11'
2024-11-01T18:09:14.2791179Z Entering 'third_party/opentelemetry-cpp'
2024-11-01T18:09:14.2850848Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T18:09:14.2903431Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T18:09:14.2955442Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T18:09:14.3007105Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T18:09:14.3060809Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T18:09:14.3115587Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T18:09:14.3175619Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T18:09:14.3235351Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T18:09:14.3295483Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T18:09:14.3358637Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T18:09:14.3442078Z Entering 'third_party/pocketfft'
2024-11-01T18:09:14.3499800Z Entering 'third_party/protobuf'
2024-11-01T18:09:14.3560456Z Entering 'third_party/protobuf/third_party/benchmark'
2024-11-01T18:09:14.3616261Z Entering 'third_party/protobuf/third_party/googletest'
2024-11-01T18:09:14.3672042Z Entering 'third_party/psimd'
2024-11-01T18:09:14.3731164Z Entering 'third_party/pthreadpool'
2024-11-01T18:09:14.3791429Z Entering 'third_party/pybind11'
2024-11-01T18:09:14.3846583Z Entering 'third_party/python-peachpy'
2024-11-01T18:09:14.3898531Z Entering 'third_party/sleef'
2024-11-01T18:09:14.3952201Z Entering 'third_party/tensorpipe'
2024-11-01T18:09:14.4006336Z Entering 'third_party/tensorpipe/third_party/googletest'
2024-11-01T18:09:14.4059828Z Entering 'third_party/tensorpipe/third_party/libnop'
2024-11-01T18:09:14.4113403Z Entering 'third_party/tensorpipe/third_party/libuv'
2024-11-01T18:09:14.4166893Z Entering 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T18:09:14.4216924Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T18:09:14.4289138Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2024-11-01T18:09:14.4318601Z http.https://github.com/.extraheader
2024-11-01T18:09:14.4327695Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2024-11-01T18:09:14.4365928Z [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' || :"
2024-11-01T18:09:14.4672077Z Entering 'android/libs/fbjni'
2024-11-01T18:09:14.4712566Z http.https://github.com/.extraheader
2024-11-01T18:09:14.4752800Z Entering 'third_party/FP16'
2024-11-01T18:09:14.4791957Z http.https://github.com/.extraheader
2024-11-01T18:09:14.4830678Z Entering 'third_party/FXdiv'
2024-11-01T18:09:14.4872774Z http.https://github.com/.extraheader
2024-11-01T18:09:14.4912512Z Entering 'third_party/NNPACK'
2024-11-01T18:09:14.4954611Z http.https://github.com/.extraheader
2024-11-01T18:09:14.4991003Z Entering 'third_party/NVTX'
2024-11-01T18:09:14.5029752Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5063557Z Entering 'third_party/VulkanMemoryAllocator'
2024-11-01T18:09:14.5101436Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5140782Z Entering 'third_party/XNNPACK'
2024-11-01T18:09:14.5181676Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5236752Z Entering 'third_party/benchmark'
2024-11-01T18:09:14.5275222Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5311819Z Entering 'third_party/composable_kernel'
2024-11-01T18:09:14.5348030Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5387335Z Entering 'third_party/cpp-httplib'
2024-11-01T18:09:14.5423278Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5457426Z Entering 'third_party/cpuinfo'
2024-11-01T18:09:14.5493519Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5529207Z Entering 'third_party/cudnn_frontend'
2024-11-01T18:09:14.5565182Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5597778Z Entering 'third_party/cutlass'
2024-11-01T18:09:14.5632746Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5674026Z Entering 'third_party/eigen'
2024-11-01T18:09:14.5709716Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5744257Z Entering 'third_party/fbgemm'
2024-11-01T18:09:14.5779405Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5813679Z Entering 'third_party/fbgemm/third_party/asmjit'
2024-11-01T18:09:14.5849442Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5882914Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2024-11-01T18:09:14.5918744Z http.https://github.com/.extraheader
2024-11-01T18:09:14.5953430Z Entering 'third_party/fbgemm/third_party/cutlass'
2024-11-01T18:09:14.5988658Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6029828Z Entering 'third_party/fbgemm/third_party/googletest'
2024-11-01T18:09:14.6064763Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6097286Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2024-11-01T18:09:14.6132333Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6165808Z Entering 'third_party/flatbuffers'
2024-11-01T18:09:14.6201016Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6238324Z Entering 'third_party/fmt'
2024-11-01T18:09:14.6275406Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6314660Z Entering 'third_party/gemmlowp/gemmlowp'
2024-11-01T18:09:14.6352889Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6387243Z Entering 'third_party/gloo'
2024-11-01T18:09:14.6424221Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6457728Z Entering 'third_party/googletest'
2024-11-01T18:09:14.6493721Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6526932Z Entering 'third_party/ideep'
2024-11-01T18:09:14.6563554Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6596336Z Entering 'third_party/ideep/mkl-dnn'
2024-11-01T18:09:14.6634153Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6677079Z Entering 'third_party/ittapi'
2024-11-01T18:09:14.6719598Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6756750Z Entering 'third_party/kineto'
2024-11-01T18:09:14.6793085Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6826615Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2024-11-01T18:09:14.6862916Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6897523Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2024-11-01T18:09:14.6936321Z http.https://github.com/.extraheader
2024-11-01T18:09:14.6972043Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2024-11-01T18:09:14.7009130Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7043025Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2024-11-01T18:09:14.7079343Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7114913Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2024-11-01T18:09:14.7152630Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7189999Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2024-11-01T18:09:14.7231996Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7272066Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2024-11-01T18:09:14.7310771Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7347002Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2024-11-01T18:09:14.7384312Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7420467Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2024-11-01T18:09:14.7456420Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7493291Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2024-11-01T18:09:14.7532555Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7570741Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2024-11-01T18:09:14.7612384Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7648895Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2024-11-01T18:09:14.7691199Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7732648Z Entering 'third_party/mimalloc'
2024-11-01T18:09:14.7774988Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7815869Z Entering 'third_party/nccl/nccl'
2024-11-01T18:09:14.7854397Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7888990Z Entering 'third_party/nlohmann'
2024-11-01T18:09:14.7926402Z http.https://github.com/.extraheader
2024-11-01T18:09:14.7961351Z Entering 'third_party/onnx'
2024-11-01T18:09:14.7996936Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8048211Z Entering 'third_party/onnx/third_party/pybind11'
2024-11-01T18:09:14.8085009Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8121849Z Entering 'third_party/opentelemetry-cpp'
2024-11-01T18:09:14.8158108Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8193989Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2024-11-01T18:09:14.8230559Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8264045Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2024-11-01T18:09:14.8300568Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8337040Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2024-11-01T18:09:14.8373904Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8407041Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2024-11-01T18:09:14.8442194Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8476341Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2024-11-01T18:09:14.8512179Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8544161Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2024-11-01T18:09:14.8579607Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8612028Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2024-11-01T18:09:14.8647078Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8680307Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2024-11-01T18:09:14.8716020Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8751318Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2024-11-01T18:09:14.8787472Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8826081Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2024-11-01T18:09:14.8862068Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8917756Z Entering 'third_party/pocketfft'
2024-11-01T18:09:14.8955928Z http.https://github.com/.extraheader
2024-11-01T18:09:14.8988966Z Entering 'third_party/protobuf'
2024-11-01T18:09:14.9026404Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9065743Z Entering 'third_party/protobuf/third_party/benchmark'
2024-11-01T18:09:14.9102677Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9135959Z Entering 'third_party/protobuf/third_party/googletest'
2024-11-01T18:09:14.9173088Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9211554Z Entering 'third_party/psimd'
2024-11-01T18:09:14.9251205Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9290025Z Entering 'third_party/pthreadpool'
2024-11-01T18:09:14.9333044Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9372539Z Entering 'third_party/pybind11'
2024-11-01T18:09:14.9412995Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9449505Z Entering 'third_party/python-peachpy'
2024-11-01T18:09:14.9486073Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9519379Z Entering 'third_party/sleef'
2024-11-01T18:09:14.9554675Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9588789Z Entering 'third_party/tensorpipe'
2024-11-01T18:09:14.9625765Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9659335Z Entering 'third_party/tensorpipe/third_party/googletest'
2024-11-01T18:09:14.9699010Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9735371Z Entering 'third_party/tensorpipe/third_party/libnop'
2024-11-01T18:09:14.9770913Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9805003Z Entering 'third_party/tensorpipe/third_party/libuv'
2024-11-01T18:09:14.9841180Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9874910Z Entering 'third_party/tensorpipe/third_party/pybind11'
2024-11-01T18:09:14.9910036Z http.https://github.com/.extraheader
2024-11-01T18:09:14.9943456Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2024-11-01T18:09:14.9979834Z http.https://github.com/.extraheader
2024-11-01T18:09:15.0105381Z A job completed hook has been configured by the self-hosted runner administrator
2024-11-01T18:09:15.0127086Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh'
2024-11-01T18:09:15.0132689Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2024-11-01T18:09:15.0133235Z ##[endgroup]
2024-11-01T18:09:23.3257497Z Cleaning up orphan processes